AnomAlert软件手册

Features• Measures from ppb to 3,000 ppm Full Scale (NO/Nox)• Four User Programmable Ranges from 0-1 ppm to 0-3,000 ppm•FastResponseTime•AutoRanging•AutoCalibration • Output Options: Analog (User Scalable), Digital (RS232)including AK Protocol & TCP/IP• Displays & Outputs: NO/NOx/NO2 with Adjustable Time &Hold, Diagnostics, Alarms & Preventative Maintenance• Remote Monitoring and Control• Electronic Sample & Ozone Flow Control /analyticalproductsAnalytical Products and SolutionsNOXMAT 700Accurate, Precise, DependableDescriptionThe NOXMAT Model 700 CLD NO/NOx digital analyzer isdesigned around a state-of-the-art 16-bit microprocessor, with16 digital inputs, 16 digital outputs, 16 analog inputs and 4analog outputs. The analyzer can be manually operated fromthe keypad or remotely via TCP/IP, RS- 232C communicationsand discrete inputs.The analyzer display includes screen presentation of all analyzeralarms. Four levels of password protection are provided. Forprecision measurements, the analyzer’s accuracy is increased byentering calibration curve fit polynomials.Automatic calibration may be activated locally or remotely andincludes auto cal via preset times. The analyzer may also displayNO, NOx and NO2 via selectable time and hold commands.For more information, please contact:Siemens Industry, Inc.5980 West Sam Houston Parkway North Suite 500Houston, TX 77041Phone: 713-939-7400Email:*************************************************************Method of OperationThe NOXMAT 700 CLD Analyzer utilizes the principle ofchemiluminescence for analyzing the NO or NOx concentration within a gaseous sample. In the NO mode, the method is based upon the chemiluminescent reaction between ozone and nitric oxide (NO) yielding nitrogen dioxide (NO 2) and oxygen. This reaction produces light which has an intensity proportional to the mass flow rate of NO into the reaction chamber. The light is measured by means of a photodiode and associatedamplification electronics. In the NOx mode, NO plus NO 2 is determined as above, however, the sample is first routed through the internal NO 2 in the sample to NO. The resultant reaction is then directly proportional to the total concentration of NOx. Local operation is simplified using the 20 button alphanumeric keypad with data presented on a back lit LCD display. All local operations may be performed remote via RS-232 and/or TCP/IP.Applications • Stack Gases (C E M) • Scrubber E fficiency • Turbine/Generator Feedback Control • Process Chemical Gas Analysis • Personnel Safety • Power Plant Stack De-Nitrification • Vehicle EmissionsSpecificationsDetector: Chemiluminescence (CLD) Photodiode (thermally stabilized with Peltier Cooler) NO/NOx RANGES: 0-1 * to 3,000 ppm NO or NOx (Four user programmable ranges) (Higher ranges available upon request)Response Time: T90 < 2 Seconds to 60 Seconds Adjustable Resolution: 10 ppb NO/NOx (Displays 5 significant digits)Repeatability: Better than 0.5% of Full Scale Linearity: Better than 0.5% of Full ScaleNoise*: Less than 1 % of Full Scale (*1.5% @ 0-1 ppm Full Scale)Zero & Span Drift: Less than 1 % of Full Scale per 24 Hours Zero & Span Adjustment: Via front panel, TCP/IP or RS-232NH 3, HCN & SO 2 Effect: Not detectable with 100 ppm CO 2 Effect: Less than 1 % with 10% CO 2H 2O Effect: Less than 1 % with 1 % H 2OSample Gas Pressure: 8-25 psig (Standard model)Sample Flow Rate: 1.5 to 2.0 LPM (Consult factory for other flow rates)Converter:****************************>98%efficiency Ozonator: Ultraviolet LampAir Or O 2 Requirements: Less than 0.01 ppm NOx at 350 cc/Min. @ 25 psig (Dew Point < -35°C)NO/NOx Control: Manual/Remote/Auto Cycle (Remote NOx mode by dry contact closure)Outputs Available: TCP/IP, RS232, Four Scalable Analog 0- 10 V/4-20 mADiscrete Alarms: (Local & Remote Adjustable) General Fault! TTL Logic (Ground True) Calibration Failure/ TTL Logic (Ground True) High Concentration (2 each)/ TTL Logic (Ground True)Digital Diagnostics: Control Voltages, Temperatures, Pressures, Flow ParametersKeypad Displays: Factory Settings,TCP/IP Address, Passwords (4), Scalable Analog Output Voltages, Full Scale Range Select, Auto Cal Times Special Features: Calculated NO 2 derived from NOx converter efficiency, Auto Ranging, Auto Calibration (adjustable through internal clock) Less than 3 cc Gold Plated Reaction Chamber Display: 3” x 5” Back lit LCD Sample Temperature: Up to 50.C Noncondensing Ambient Temperature: 5 to 40.CAmbient Humidity: Less than 90% RH Noncondensing Warm-Up Time: 1 Hour (Typical)Fittings: 1/4 Inch TubePower Requirements: 115/230 (:t100;0) VAC; 50/60 Hz; 200 Watts (350 watts with pump)Dimensions: 5 1/4 H x 19 W x 17 D (Inches) - short case Weight: 45 Pounds。

Application Notepyrogen or bacterial endotoxin test may be used in place of the in vivo rabbit pyrogen test, where appropriate.”Principle of the MATThe monocyte activation test (MAT) is the human in vitro alternative to the rabbit pyrogen test, and allows the detection of the full range of pyrogens, including endotoxins and non-endotoxin pyrogens (NEPs).By putting the product to be tested in contact withhuman monocytic cells, it will mimic what happens in the human body: in presence of pyrogens, the monocytes are activated and produce cytokines such as interleukin-6.The cytokines are then detected using an immunological assay (ELISA) involving specific antibodies and an enzymatic color reaction.Principle of the PyroMAT™ SystemThe PyroMAT™ System uses cryo-preserved Mono-Mac-6 (MM6) human monocytic cells as a source of monocytes. The response to pyrogenic substances is determined by measurement of interleukin-6 (IL-6) produced by the Mono-Mac-6 cells. For this purpose, the ELISA microplate supplied in the kit is coated with an antibody specific to IL-6.IL-6 molecules released by MM6 cells during incubation phase are transferred from the cells supernatant to the ELISA plate, and bound by the immobilized primary antibody.A secondary antibody, linked to an enzyme, is added to form an IL-6 bound complex. After washing anyunbound molecules, the IL-6 bound complex is detected in a color reaction started by the addition of an appropriate substrate.The color development is proportional to the amount of initial IL-6 production in the supernatant and measured with an absorbance reader.Reference Standard Endotoxins suitable for PyroMAT™ SystemIntroductionWhat is a pyrogen?A pyrogen is, by definition, a substance that produces a rise in temperature in a human or animal. Pyrogens constitute a heterogeneous group of contaminantscomprising microbial and non-microbial substances. The most widely known pyrogen is the endotoxin (LPS =Lipo-Polysaccharide), which is produced by gram-negative bacteria. Other microbial substances include thosederived from gram-positive bacteria like Lipoteichoic Acid (LTA), particles from viruses and pyrogens originating from yeasts and fungi. Non-microbial pyrogenicsubstances can be rubber particles, microscopic plastic particles or metal compounds in elastomers.Why to conduct a pyrogen test?Pyrogenic substances in pharmaceutical products can induce life-threatening fever reactions after injection into the human body. Therefore, it is a regulatory requirement to test such products for pyrogens to ensure product quality and patient safety.Purpose of the test is to prove that the amount of pyro-gens contained in the product will not exceed a certain threshold, known as the contaminant limit concentration (CLC), that will guarantee the patient safety.The monocyte activation test (MAT) method has been qualified and validated for the detection of pyrogens by the European Center for the Validation of Alternative Methods (ECVAM) in 2005 and by the InteragencyCoordinating Committee on the Validation of Alternative Methods (ICCVAM) in 2008.It has been among the compendial methods for pyrogen detection in the European Pharmacopeia since 2010 (Chapter 2.6.30). The MAT is also mentioned by the FDA "Guidance For Industry – Pyrogen and Endotoxins testing: Questions and Answers" as an alternative to the rabbit pyrogen test which should be validated according to USP <1225>. Additionally, the USP <151> Pyrogen Test mentions that, "A validated, equivalent in vitroThe life science business of Merck operates as MilliporeSigma in the U.S. and Canada.2Resuspension of RSELyophilized RSE were reconstituted and aliquoted according to supplier guidelines.Dilution of Reference Standard Endotoxin aliquotsThe standard endotoxin solutions were prepared from the RSE stock solution at 2000 EU/mL. Seven (7) endotoxin concentrations (0.0125, 0.025, 0.05, 0.1,0.2, 0.4, and 0.8 EU/mL) were prepared to generate the standard curve according to the following procedure:• Thaw a 50 µL-aliquot of RSE and vortex at maximum speed during 1 min.• Perform serial dilutions in endotoxin-free water, using endotoxin-free glass tubes, as described below. Make sure to vortex all the dilutions before using.Comparison of Reference Standard Endo-toxins (RSE) from different suppliersPreparation of endotoxin standard solutions is needed to assess the limit of detection (LOD) of the system, to build a standard curve for quantification or to estimate the pyrogen content of a sample, depending on the MAT method used.The use of a validated Reference Standard Endotoxin is required and such a standard can be supplied by the European Pharmacopeia (EDQM) or the United States Pharmacopeia (USP).Control Standard Endotoxins (CSE) provided by LAL suppliers should not be used for MAT test.The reference standard endotoxin (RSE) supplied by the USP / EDQM is a reference endotoxin preparation with a certified activity upon reconstitution. Control standard endotoxin preparations (CSEs) are qualified using the RSE, but their activity is certified only in combination with a test system, e.g. a defined preparation of limulus amoebocyte lysate used for the bacterial endotoxin test. Using these CSEs outside their test system might lead to unexpected results and is not recommended by the respective suppliers. As there is currently no dedicated reference standard for the pyrogen test available,standardization is achieved using a strong pyrogen like the RSE whose production and standardization is not depending on the use of a specific bacterial endotoxin test.The scope of this application note is to show the suitability of Reference Standard Endotoxins from different suppliers (USP/EDQM) for MAT with the PyroMAT™ System.PyroMAT™ Kit Pyr0MATkit PyroMAT™ Cells Pyr0MATcellsPyroMAT™ Endotoxin StandardEuropean Pharmacopoeia (EP) Reference Standard Endotoxin1.44161.0001Sigma-Aldrich RSEEuropean Pharmacopoeia (EP) Reference Standard EndotoxinE0150000ACILA RSE USP Reference Standard Endotoxin (USP) 10.000 E.U./Fl1220200NIBSC RSE 3rd International Standard 10,000 USP Endotoxin units Replacement I.S. for 94/58010/178Table 1: Materials used to generate standard curvesMaterialsFigure 2:Materials used to generate standard curves3MAT quick procedure with PyroMAT™Step 1: Preparation and incubation with PyroMAT™ cellsStep 2: Detection of IL-6 with ELISA1• Prepare suitable endotoxin standard dilutions• Load the different solutions on the 96-wells cell culture plate • Prepare the PyroMAT™ cells and dispense in each well2• Incubate the plate for 22 ±2 hours at 37 °C with humidified atmosphere, without CO 23• Transfer the cell supernatants into IL-6 microplate• Add the IL-6 conjugate to each well • Incubate 2 hours at room temperature 4• Remove the liquid and wash the plate 4 times• Prepare the substrate solution by mixing color reagent A and B and add the mixture to each well • Incubate 30 minutes at room temperature, in the dark5• Add the stop solution6• Read the plate at 450 nm and 630 nm within 30 minutes after adding the stop solutionFigure 3: PyroMAT TMworkflow with standard ELISA procedureResultsEndotoxin standard curves were generated using RSE from different suppliers. To be considered “VALID”, the endotoxin standard curve must fulfill the following acceptance criteria described in the EP Chapter 2.6.30 : Effect of dose criteria: a statistical test thatconfirms a positive dose/effect response.Goodness of fit: a statistical test that confirms the suitability of the regression model to describe the raw data. The data are modeled with a 5-parameter logistics regression model.Blank criteria: the mean of blank OD value should be below 0.1.LOD criteria: the test is valid if an LOD ≤ 0.05 EU/mL is reached.An additional criterion was implemented in the protocol to assess the reactivity of the standard curve:Minimum of reactivity: OD of the 4 replicates of the highest standard (0.8 EU/mL) should be above 3.It is not required by the European pharmacopeia and is given as an additional indication for the customer.Data analysis was performed using the PyroMAT™data analysis tool, which consists of a specific protocol developed for PyroMAT™ using Gen5 Software (Biotek).The Figure 4 presents the curves that were obtained with the PyroMAT™ system using Reference Standard Endotoxins (RSE) from four different suppliers as described in table 1.Figure 4: Comparison of standard curves generated with various Reference Standard EndotoxinsThe validity of the acceptance criteria for the endotoxin standard curve was determined using the PyroMAT™ data analysis tool (protocol for Gen5 Software).The Figure 5 shows the results obtained and the legend used in the software for data interpretation:All standard curves generated with RSE from different suppliers passed the acceptance criteria of a valid standard curve according to the EP chapter 2.6.30.ConclusionAll four different Reference Standard Endotoxins (RSE) tested led to the generation of a valid standard curve and can be used to perform MAT with PyroMAT™ system.MaterialEffect of close Goodness of Fit Blank Delta OD LOD criteria Minimum of reactivityPyroMAT™ Endotoxin Standard VALID VALID VALID VALID CONFORM Sigma-Aldrich RSE VALID VALID VALID VALID CONFORM ACILA RSE VALID VALID VALID VALID CONFORM NIBSC RSEVALIDVALIDVALIDVALIDCONFORMLegendEffect of Dose Criteria: VALID: p<0.01 LOD Criteria VALID: LOD ≤ 0.05 EU/mLINVALID: p≥0.01INVALID:LOD > 0.05 EU/mLGoodness of Fit Criteria: VALID: p>0.05 Additional Criteria – Minimum of reactivity CONFORM: All replicates of Delta OD at STD7 are above 3INVALID: p≤0.05 NOT REACHED: At least one replicate of Delta OD at STD7 is below 3BLK Delta OD Criteria: VALID: MEAN(BLK)<0.1 : Unable to EvaluateINVALID: MEAN(BLK)≥0.1Figure 5: Acceptance criteria for the endotoxin standard curves and legend for data interpretationTo place an order or receive technical assistanceIn Europe, please call Customer Service:France: 0825 045 645 Germany************ I taly: 848 845 645Spain: 901 516 645 Option 1Switzerland: 0848 645 645UnitedKingdom************For other countries across Europe, p lease call: +44 (0) 115 943 0840 Or visit: /officesFor Technical Service visit: /techservice /pyromatMerck, the vibrant M, Millipore and PyroMAT are trademarks of Merck KGaA or its afiliates. All other trademarks are the property of their respective owners. Detailed information on trademarks is available via publicly accessible resources.© 2018 Merck KGaA, Darmstadt, Germany and/or its affiliates. All Rights Reserved.MK_AN2150EN 2018-1255306/2018Merck KGaAFrankfurter Str. 250 64293 Darmstadt。

Minitab Statistical Software入门内容1简介4概述4案例4 Minitab用户界面4数据类型6打开并检查工作表6在下一章中72用图形表示数据8概述8创建、解释和编辑直方图8创建并解释散点图13在一个布局上排列多个图形16保存Minitab项目19在下一章中193分析数据20概述20对数据进行汇总摘要20比较两个或更多均值22保存项目28在下一章中284评估质量29概述29创建并解释控制图29创建并解释能力统计量33保存项目35在下一章中355设计实验36概述36创建设计的试验36查看设计39将数据输入到工作表40分析设计并解释结果40使用存储的模型进行其他分析44保存项目47在下一章中486重新执行分析49概述49使用会话命令执行分析49复制会话命令50使用exec文件重复进行分析51保存项目52在下一章中527导入和准备数据53概述53从不同来源导入数据53准备数据以进行分析55当数据值更改时57保存工作表57索引591.简介概述Minitab Statistical Software入门介绍了Minitab中一些最常用的功能和任务。

备注:本指南中某些功能仅在桌面应用中可用。

使用Web应用时在Windows计算机或Mac上，您可以打开桌面应用来访问Minitab提供的所有功能。

大部分统计分析都需要执行一系列步骤，这些步骤通常由背景知识或您要调查的主题领域来指导完成。

第2章到第5章介绍以下步骤：•利用图形研究数据并显示结果•进行统计分析•评估质量•设计试验在第6章和第7章中，您学习如何执行以下操作：•使用快捷方式自动执行将来的分析•将数据从不同文件类型导入到Minitab中，并准备数据以进行分析。

案例某家在网上销售图书的公司具有三个区域出货中心。

每个出货中心都用不同的计算机系统来输入和处理订单。

该公司想要确定最高效的计算机系统，并在每个出货中心使用该计算机系统。

在整个Minitab Statistical Software入门中，您学习了使用Minitab来分析来自出货中心的数据。

Anova TableSourcea | SSb dfc MSd -------------+------------------------------Model | 9543.72074 4 2385.93019Residual | 9963.77926 195 51.0963039 -------------+------------------------------Total | 19507.5 199 98.0276382a. Source - This is the source of variance, Model, Residual, and Total. The Total variance is partitioned into the variance which can be explained by the independent variables (Model) and the variance which is not explained by the independent variables (Residual, sometimes called Error). Note that the Sums of Squares for the Model and Residual add up to the Total Variance, reflecting the fact that the Total Variance is partitioned into Model and Residual variance.b. SS - These are the Sum of Squares associated with the three sources of variance, Total, Model and Residual. These can be computed in many ways. Conceptually, these formulas can be expressed as:SSTotal The total variability around the mean. S(Y - Ybar)2.SSResidual The sum of squared errors in prediction. S(Y - Ypredicted)2.SSModel The improvement in prediction by using the predicted value of Y over just using the mean of Y. Hence, this would be the squared differences between the predicted value of Y and the mean of Y, S(Ypredicted - Ybar)2. Another way to think of this is the SSModel is SSTotal - SSResidual. Note that the SSTotal = SSModel + SSResidual. Note that SSModel / SSTotal is equal to .4892, the value of R-Square. This is because R-Square is the proportion of the variance explained by the independent variables, hence can be computed by SSModel / SSTotal.c. df - These are the degrees of freedom associated with the sources of variance. The total variance has N-1 degrees of freedom. In this case, there were N=200 students, so the DF for total is 199. The model degrees of freedom corresponds to the number of predictors minus 1 (K-1). You may think this would be 4-1 (since there were 4 independent variables in the model, math, female, socst and read). But, the intercept is automatically included in the model (unless you explicitly omit the intercept). Including the intercept, there are 5 predictors, so the model has 5-1=4 degrees of freedom. The Residual degrees of freedom is the DF total minus the DF model, 199 - 4 is 195.d. MS - These are the Mean Squares, the Sum of Squares divided by their respective DF. For the Model, 9543.72074 / 4 = 2385.93019. For the Residual, 9963.77926 / 195 = 51.0963039. These are computed so you can compute the F ratio, dividing the Mean Square Model by the Mean Square Residual to test the significance of the predictors in the model.--------------------------------------------------------------------------------Overall Model FitNumber of obse = 200 F( 4, 195)f = 46.69 Prob > Ff = 0.0000 R-squaredg = 0.4892 Adj R-squaredh = 0.4788 Root MSEi = 7.1482e. Number of obs - This is the number of observations used in the regression analysis.f. F and Prob > F - The F-value is the Mean Square Model (2385.93019) divided by the Mean Square Residual (51.0963039), yielding F=46.69. The p-value associated with this F value is very small (0.0000). These values are used to answer the question "Do the independent variables reliably predict the dependent variable?". The p-value is compared to your alpha level (typically 0.05) and, if smaller, you can conclude "Yes, the independent variables reliably predict the dependent variable". You could say that the group of variables math and female can be used to reliably predict science (the dependent variable). If the p-value were greater than 0.05, you would say that the group of independent variables does not show a statistically significant relationship with the dependent variable, or that the group of independent variables does not reliably predict the dependent variable. Note that this is an overall significance test assessing whether the group of independent variables when used together reliably predict the dependent variable, and does not address the ability of any of the particular independent variables to predict the dependent variable. The ability of each individual independent variable to predict the dependent variable is addressed in the table below where each of the individual variables are listed.g. R-squared - R-Squared is the proportion of variance in the dependent variable (science) which can be predicted from the independent variables (math, female, socst and read). This value indicates that 48.92% of the variance in science scores can be predicted from the variables math, female, socst and read. Note that this is an overall measure of the strength of association, and does not reflect the extent to which any particular independent variable is associated with the dependent variable.h. Adj R-squared - Adjusted R-square. As predictors are added to the model, each predictor will explain some of the variance in the dependent variable simply due to chance. One could continue to add predictors to the model which would continue to improve the ability of the predictors to explain the dependent variable, although some of this increase in R-square would be simply due to chance variation in that particular sample. The adjusted R-square attempts to yield a more honest value to estimate the R-squared for the population. The value of R-square was .4892, while the value of Adjusted R-square was .4788 Adjusted R-squared is computed using the formula 1 - ((1 - Rsq)((N - 1) /( N - k - 1)). From this formula, you can see that when the number of observations is small and the number of predictors is large, there will be a much greater difference between R-square and adjusted R-square (because the ratio of (N - 1) / (N - k - 1) will be much greater than 1). By contrast, when the number of observations is very large compared to the number of predictors, the value of R-square and adjusted R-square will be much closer because the ratio of (N - 1)/(N - k - 1) will approach 1.i. Root MSE - Root MSE is the standard deviation of the error term, and is the square root of the Mean Square Residual (or Error).--------------------------------------------------------------------------------Parameter Estimates------------------------------------------------------------------------------ sciencej | Coef.k Std. Err.l tm P>|t|m [95% Conf. Interval]n -------------+---------------------------------------------------------------- math | .3893102 .0741243 5.25 0.000 .243122 .5354983 female | -2.009765 1.022717 -1.97 0.051 -4.026772 .0072428 socst | .0498443 .062232 0.80 0.424 -.0728899 .1725784 read | .3352998 .0727788 4.61 0.000 .1917651 .4788345 _cons | 12.32529 3.193557 3.86 0.000 6.026943 18.62364 ------------------------------------------------------------------------------j. science - This column shows the dependent variable at the top (science) with the predictor variables below it (math, female, socst, read and _cons). The last variable (_cons) represents the constant, also referred to in textbooks as the Y intercept, the height of the regression line when it crosses the Y axis. In other words, this is the predicted value of science when all other variables are 0.k. Coef. - These are the values for the regression equation for predicting the dependent variable from the independent variable. The regression equation is presented in many different ways, for example:Ypredicted = b0 + b1*x1 + b2*x2 + b3*x3 + b4*x4The column of estimates (coefficients or parameter estimates, from here on labeled coefficients) provides the values for b0, b1, b2, b3 and b4 for this equation. Expressed in terms of the variables used in this example, the regression equation issciencePredicted = 12.32529 + .3893102*math + -2.009765*female+.0498443*socst+.3352998*readThese estimates tell you about the relationship between the independent variables and the dependent variable. These estimates tell the amount of increase in science scores that would be predicted by a 1 unit increase in the predictor. Note: For the independent variables which are not significant, the coefficients are not significantly different from 0, which should be taken into account when interpreting the coefficients. (See the columns with the t-value and p-value about testing whether the coefficients are significant).math - The coefficient (parameter estimate) is .3893102. So, for every unit (i.e., point, since this is the metric in which the tests are measured) increase in math, a .3893102 unit increase in science is predicted, holding all other variables constant. (It does not matter at what value you hold the other variables constant, because it is a linear model.) Or, for every increase of one point on the math test, your science score is predicted to be higher by .3893102 points. This is significantly different from 0.female - For every unit increase in female, there is a -2.009765 unit decrease in the predicted science score, holding all other variables constant. Since female is coded 0/1 (0=male, 1=female) the interpretation can be put more simply. For females the predicted science score would be 2 points lower than for males. The variable female is technically not statistically significantly different from 0, because the p-value is greater than .05. However, .051 is so close to .05 that some researchers would still consider it to be statistically significant.socst - The coefficient for socst is .0498443. This means that for a 1-unit increase in thesocial studies score, we expect an approximately .05 point increase in the science score. This is not statistically significant; in other words, .0498443 is not different from 0.read - The coefficient for read is .3352998. Hence, for every unit increase in reading score we expect a .34 point increase in the science score. This is statistically significant.l. Std. Err. - These are the standard errors associated with the coefficients. The standard error is used for testing whether the parameter is significantly different from 0 by dividing the parameter estimate by the standard error to obtain a t-value (see the column with t-values and p-values). The standard errors can also be used to form a confidence interval for the parameter, as shown in the last two columns of this table.m. t and P>|t| - These columns provide the t-value and 2-tailed p-value used in testing the null hypothesis that the coefficient (parameter) is 0. If you use a 2-tailed test, then you would compare each p-value to your preselected value of alpha. Coefficients having p-values less than alpha are statistically significant. For example, if you chose alpha to be 0.05, coefficients having a p-value of 0.05 or less would be statistically significant (i.e., you can reject the null hypothesis and say that the coefficient is significantly different from 0). If you use a 1-tailed test (i.e., you predict that the parameter will go in a particular direction), then you can divide the p-value by 2 before comparing it to your preselected alpha level. With a 2-tailed test and alpha of 0.05, you may reject the null hypothesis that the coefficient for female is equal to 0. The coefficient of -2.009765 is significantly greater than 0. However, if you used a 2-tailed test and alpha of 0.01, the p-value of .0255 is greater than 0.01 and the coefficient for female would not be significant at the 0.01 level. Had you predicted that this coefficient would be positive (i.e., a one tail test), you would be able to divide the p-value by 2 before comparing it to alpha. This would yield a 1-tailed p-value of 0.00945, which is less than 0.01 and then you could conclude that this coefficient is greater than 0 with a one tailed alpha of 0.01.The coefficient for math is significantly different from 0 using alpha of 0.05 because its p-value is 0.000, which is smaller than 0.05.The coefficient for socst (.0498443) is not statistically significantly different from 0 because its p-value is definitely larger than 0.05.The coefficient for read (.3352998) is statistically significant because its p-value of 0.000 is less than .05.The constant (_cons) is significantly different from 0 at the 0.05 alpha level. However, having a significant intercept is seldom interesting.n. [95% Conf. Interval] - This shows a 95% confidence interval for the coefficient. This is very useful as it helps you understand how high and how low the actual population value of the parameter might be. The confidence intervals are related to the p-values such that the coefficient will not be statistically significant if the confidence interval includes 0. If you look at the confidence interval for female, you will see that it just includes 0 (-4 to .007). Because .007 is so close to 0, the p-value is close to .05. If the upper confidence level had been a little smaller, such that it did not include 0, the coefficient for female would have been statistically significant. Also, consider the coefficients for female (-2) and read (.34). Immediately you see that the estimate for female is so much bigger, but examine the confidence interval for it (-4 to .007). Now examinethe confidence interval for read (.19 to .48). Even though female has a bigger coefficient (in absolute terms) it could be as small as -4. By contrast, the lower confidence level for read is .19, which is still above 0. So, even though female has a bigger coefficient, read is significant and even the smallest value in the confidence interval is still higher than 0. The same cannot be said about the coefficient for socst. Such confidence intervals help you to put the estimate from the coefficient into perspective by seeing how much the value could vary.。

Package‘tabularaster’November1,2023Type PackageTitle Tidy Tools for'Raster'DataVersion0.7.2Description Facilities to work with vector and raster data in efficientrepeatable and systematic workflow.Missing functionality in existing packages is included here to allow extraction from raster data with'simple features'and'Spatial'types and to make extraction consistent and straightforward.Extract cell numbers from raster data and return the cells as a data framerather than as lists of matrices or vectors.The functions here allow spatial data to be used without special handling for the format currently in use.License GPL-3LazyData TRUEDepends R(>=3.2.5)Imports dplyr,fasterize,magrittr,raster,silicate,spatstat.geom,tibbleEncoding UTF-8Suggests covr,knitr,rmarkdown,testthat(>=2.1.0),spellingVignetteBuilder knitrURL https:///hypertidy/tabularasterBugReports https:///hypertidy/tabularaster/issues Language en-USNeedsCompilation noAuthor Michael D.Sumner[aut,cre]Maintainer Michael D.Sumner<******************>Repository CRANDate/Publication2023-11-0106:10:03UTC12as_tibble R topics documented:as_tibble (2)cellnumbers (3)decimate (5)ghrsst (5)index_extent (6)oisst (7)polycano (7)rastercano (7)sharkcano (8)tabularaster (8)Index10 as_tibble Convert a Raster to a data frame.DescriptionGenerate a data frame version of any raster e the arguments’cell’,’dim’,’split_date’and ’value’to control the columns that are included in the output.Usage##S3method for class BasicRasteras_tibble(x,cell=TRUE,dim=nlayers(x)>1L,value=TRUE,split_date=FALSE,xy=FALSE,...)Argumentsx a RasterLayer,RasterStack or RasterBrickcell logical to include explicit cell numberdim logical to include slice indexvalue logical to return the values as a column or notsplit_date logical to split date into componentsxy logical to include the x and y centre coordinate of each cell...unusedDetailsIf the raster has only one layer,the slice index is not e’dim=FALSE’to not include the slice index value.Valuea data frame(tibble)with columns:•cellvalue the actual value of the raster cell•cellindex the index of the cell(numbered from1to ncell()in the raster way).Columns cellindex or cellvalue may be omitted if either or both of cell and/or value are FALSE,respectivelyOther columns might be included depending on the properties of the raster and the arguments to the function:•year,month,day if split_date is TRUE•x,y if xy is TRUE•dimindex if the input has more than1layer and dim is TRUE.Examples##basic data frame version of a basic rasteras_tibble(raster::raster(volcano))##data frame with time column since raster has that setr<-raster::raster(volcano)br<-raster::brick(r,r)as_tibble(raster::setZ(br,Sys.Date()+1:2),cell=TRUE)cellnumbers Extract cell numbers from a Raster object.DescriptionProvide the’cellnumbers’capability of raster::extract and friends directly,returning a data frame of query-object identifiers’object_’and the cell number.Usagecellnumbers(x,query,...)##Default S3method:cellnumbers(x,query,...)##S3method for class SpatialLinescellnumbers(x,query,...)##S3method for class sfccellnumbers(x,query,...)##S3method for class sfcellnumbers(x,query,...)Argumentsx Raster objectquery Spatial object or matrix of coordinates...unusedDetailsRaster data is inherently2-dimensional,with a time or’level’dimension treated like a layers of these2D forms.The’raster’package cell number is counted from1at the top-left,across the rows and down.This corresponds the the standard"raster graphics"convention used by’GDAL’and the ’sp’package,and many other implementations.Note that this is different to the convention used by the graphics::image function.Currently this function only operates as if the input is a single layer objects,it’s not clear if adding an extra level of grouping for layers would be sensible.Valuea data frame(tibble)with columns•object_-the object ID(what row is it from the spatial object)•cell_-the cell number of the rasterExampleslibrary(raster)library(dplyr)r<-raster(volcano)%>%aggregate(fact=4)cellnumbers(r,rasterToContour(r,level=120))library(dplyr)cr<-cut(r,pretty(values(r)))suppressWarnings(tt<-cellnumbers(cr,polycano))library(dplyr)tt%>%mutate(v=extract(r,cell_))%>%group_by(object_)%>%summarize(mean(v))head(pretty(values(r)),-1)decimate5 decimate Decimate swiftly and ruthlesslyDescriptionReduce the resolution of a raster by ruthless decimation.Usagedecimate(x,dec=10)Argumentsx raster object(single layer).dec decimation factor,raw multiplier for the resolution of the outputDetailsThis is fast,it’s just fast extraction with no care taken for utility purposes when you need to reduce the detail.Valueraster layerExampleslibrary(raster)plot(decimate(raster(volcano)))contour(raster(volcano),add=TRUE)ghrsst Sea surface temperature data.DescriptionA smoothed subset of GHRSST.FormatA raster created GHRSST data and raster smoothing.DetailsSee"data-raw/ghrsst.R"and"data-raw/ghrsst-readme.txt"for details.sst_regions is a simple polygon region layer to sit over the SST data.6index_extent Exampleslibrary(raster)plot(ghrsst,col=hcl.colors(12,"YlOrRd",rev=TRUE))plot(sst_regions,add=TRUE,col=NA)cellnumbers(ghrsst,sst_regions)index_extent Index extentDescriptionExtent in index space.Usageindex_extent(x,ex)Argumentsx raster layerex extentDetailsConvert a geographic extent into purely index space.Valueextent objectExamples##the index extent is the rows/colsindex_extent(raster::raster(volcano),raster::extent(0,1,0,1))index_extent(raster::raster(volcano),raster::extent(0,1,0,.5))oisst7 oisst Optimally interpolated SST in near-native form.DescriptionSee data-raw/oisst.R in the source repository.Thefile was avhrr-only-v2.20170729.nc,its extent -180,180,-90,90with dimensions1440x720in the usual raster configuration.FormatA data frame of sst values created from OISST data.Examplesoisstpolycano The raster volcano as polygons.DescriptionSee data-raw/rastercano.r in the source repository.FormatA sp::SpatialPolygonsDataFrame with variables:volcano_elevation.Examplesexists("polycano")rastercano The raster volcano.DescriptionSee data-raw/rastercano.r in the source repository.FormatA raster created from the volcano data.Exampleslibrary(raster)plot(rastercano)sharkcano Sharkcano,the shark and the volcano.DescriptionThis is just a free image off the internet.The image was read in and all non-essential items dropped.The dimensions in raster::raster terms is stored in attr(sharkcano,"rasterdim").FormatA data frame with117843rows and2variables:cell_integer,cell indexbyte integer,byte value of shark image pixelsThese are cell values on a grid that is648x958.ReferencesThis is the small version from here,see script in data-raw/sharkcano.r /stockphoto/16214 Thanks to@jennybc for pointers onfinding free stuff:https:///jennybc/free-photosExampleslibrary(raster)rd<-attr(sharkcano,"rasterdim")rastershark<-raster(matrix(NA_integer_,rd[1],rd[2]))rastershark[sharkcano$cell_]<-sharkcano$byte##byte,heh##I present to you,Sharkcano!(Just wait for the3D version,Quadshark).plot(rastercano)contour(rastershark,add=TRUE,labels=FALSE)plot(rastershark,col="black")##another wayplot(rastercano)points(xyFromCell(rastershark,sharkcano$cell_),pch=".")tabularaster Tabular tools for rasterDescriptionExtract and index with raster tidy tools for raster.DetailsTabularaster includes these main functions.as_tibble convert raster data to data frame form,with control over output and form of dimension/coordinate columns cellnumbers extract a data frame of query identifiers and cell,pixel index numbersdecimate fast and loose resizing of a raster to coarser resolutionindex_extent build an extent in row column form,as opposed to coordinate value formIndexas_tibble,2,9cellnumbers,3,9decimate,5,9ghrsst,5graphics::image,4index_extent,6,9oisst,7polycano,7raster,5raster::extract,3rastercano,7sharkcano,8sst_regions(ghrsst),5tabularaster,8volcano,710。

Contents[M-0]Introduction to the Mata manualintro.................................................Introduction to the Mata manual[M-1]Introduction and adviceintro........................................................Introduction and advice ing Mata with ado-files first....................................................Introduction andfirst session help........................................................Obtaining help in Stata how.............................................................How Mata works ing Mata interactively LAPACK.........................................The LAPACK linear-algebra routines limits..................................................Limits and memory utilization naming......................................Advice on naming functions and variables permutation................................An aside on permutation matrices and vectors returnedargs..................................Function arguments used to return results source.....................................................Viewing the source code e and specification of tolerances[M-2]Language definitionnguage definition break..............................................Break out of for,while,or do loop class...........................................Object-oriented programming(classes) ments continue...........................Continue with next iteration of for,while,or do loop declarations...................................................Declarations and types do..............................................................do...while(exp) errors.................................................................Error codes exp..................................................................Expressions for......................................................for(exp1;exp2;exp3)stmt ftof...................................................Passing functions to functions goto...................................................................goto label if.............................................................if(exp)...else... op arith........................................................Arithmetic operators op assignment..................................................Assignment operator op colon...........................................................Colon operators op conditional..................................................Conditional operator op increment.......................................Increment and decrement operators op join..............................................Row-and column-join operators op kronecker........................................Kronecker direct-product operator op logical........................................................Logical operators op range..........................................................Range operators op transpose.............................................Conjugate transpose operator12Contents optargs.........................................................Optional arguments pointers..................................................................Pointers pragma...............................................Suppressing warning messages reswords...........................................................Reserved words return........................................................return and return(exp) e of semicolons struct..................................................................Structures e of subscripts syntax............................................Mata language grammar and syntax version............................................................Version control void................................................................V oid matrices while.............................................................while(exp)stmt[M-3]Commands for controlling Matamands for controlling Mata end...................................................Exit Mata and return to Stata mata.....................................................Mata invocation command mata clear.....................................................Clear Mata’s memory mata describe.....................................Describe contents of Mata’s memory mata drop...................................................Drop matrix or function mata help......................................................Obtain help in Stata mata matsave..............................................Save and restore matrices mata memory.........................................Report on Mata’s memory usage mata mlib....................................................Create function library mata mosave................................Save function’s compiled code in objectfile mata rename..............................................Rename matrix or function mata set......................................Set and display Mata system parameters mata stata...................................................Execute Stata command mata which........................................................Identify function namelists........................................Specifying matrix and function names [M-4]Index and guide to functions intro....................................................Index and guide to functions io...................................................................I/O functions manipulation....................................................Matrix manipulation mathematical.........................................Important mathematical functions matrix............................................................Matrix functions programming................................................Programming functions scalar..................................................Scalar mathematical functions solvers................................Functions to solve AX=B and to obtain A inverse standard..........................................Functions to create standard matrices stata........................................................Stata interface functions statistical........................................................Statistical functions string..................................................String manipulation functions utility.......................................................Matrix utility functions [M-5]Mata functions intro...............................................................Mata functionsContents3 abbrev().........................................................Abbreviate strings abs().......................................................Absolute value(length) adosubdir().........................................Determine ado-subdirectory forfile all()..........................................................Element comparisons args()........................................................Number of arguments asarray().........................................................Associative arrays ascii().....................................................Manipulate ASCII codes assert().....................................................Abort execution if false blockdiag()...................................................Block-diagonal matrix bufio()........................................................Buffered(binary)I/O byteorder().............................................Byte order used by computer C()...............................................................Make complex c()...............................................................Access c()value callersversion()........................................Obtain version number of caller cat()....................................................Loadfile into string matrix chdir().......................................................Manipulate directories cholesky().........................................Cholesky square-root decomposition cholinv()...................................Symmetric,positive-definite matrix inversion cholsolve()............................Solve AX=B for X using Cholesky decomposition comb()binatorial function cond()...........................................................Condition number conj()plex conjugate corr()....................................Make correlation matrix from variance matrix cross().............................................................Cross products crossdev()..................................................Deviation cross products cvpermute().................................................Obtain all permutations date()...................................................Date and time manipulation deriv()........................................................Numerical derivatives designmatrix().....................................................Design matrices det()........................................................Determinant of matrix diag().................................................Replace diagonal of a matrix diag().......................................................Create diagonal matrix diag0cnt()..................................................Count zeros on diagonal diagonal().........................................Extract diagonal into column vector dir()....................................................................File list direxists()..................................................Whether directory exists direxternal().....................................Obtain list of existing external globals display().............................................Display text interpreting SMCL displayas()........................................................Set display level displayflush()............................................Flush terminal-output buffer Dmatrix().......................................................Duplication matrix docx*().......................................Generate Office Open XML(.docx)file dsign()............................................FORTRAN-like DSIGN()function e()...................................................................Unit vectors editmissing()..........................................Edit matrix for missing values edittoint()......................................Edit matrix for roundoff error(integers) edittozero().......................................Edit matrix for roundoff error(zeros) editvalue()............................................Edit(change)values in matrix eigensystem()............................................Eigenvectors and eigenvalues4Contentseigensystemselect()pute selected eigenvectors and eigenvalues eltype()..................................Element type and organizational type of object epsilon().......................................Unit roundoff error(machine precision) equilrc()..............................................Row and column equilibration error().........................................................Issue error message errprintf()..................................Format output and display as error message exit()..........................................................Terminate execution exp().................................................Exponentiation and logarithms factorial()..............................................Factorial and gamma function favorspeed()...................................Whether speed or space is to be favored ferrortext()......................................Text and return code offile error code fft().............................................................Fourier transform fileexists().......................................................Whetherfile exists fillmissing()..........................................Fill matrix with missing values findexternal().................................Find,create,and remove external globals findfile().................................................................Findfile floatround().................................................Round tofloat precision fmtwidth().........................................................Width of%fmt fopen()..................................................................File I/O fullsvd()............................................Full singular value decomposition geigensystem().................................Generalized eigenvectors and eigenvalues ghessenbergd()..................................Generalized Hessenberg decomposition ghk()...................Geweke–Hajivassiliou–Keane(GHK)multivariate normal simulator ghkfast().....................GHK multivariate normal simulator using pregenerated points gschurd()...........................................Generalized Schur decomposition halton().........................................Generate a Halton or Hammersley set hash1()...........................................Jenkins’one-at-a-time hash function hessenbergd()..............................................Hessenberg decomposition Hilbert()..........................................................Hilbert matrices I()................................................................Identity matrix inbase()...........................................................Base conversion indexnot()..................................................Find character not in list invorder()............................................Permutation vector manipulation invsym().............................................Symmetric real matrix inversion invtokens()...............................Concatenate string rowvector into string scalar isdiagonal()..............................................Whether matrix is diagonal isfleeting()...........................................Whether argument is temporary isreal()......................................................Storage type of matrix isrealvalues()..................................Whether matrix contains only real values issymmetric().................................Whether matrix is symmetric(Hermitian) isview()....................................................Whether matrix is view J().............................................................Matrix of constants Kmatrix()mutation matrix lapack()PACK linear-algebra functions liststruct()...................................................List structure’s contents Lmatrix().......................................................Elimination matrix logit()...........................................Log odds and complementary log-logContents5 lowertriangle().........................................Extract lower or upper triangle lud()...........................................................LU decomposition luinv()......................................................Square matrix inversion lusolve()...................................Solve AX=B for X using LU decomposition makesymmetric().............................Make square matrix symmetric(Hermitian) matexpsym().......................Exponentiation and logarithms of symmetric matrices matpowersym().........................................Powers of a symmetric matrix mean()............................................Means,variances,and correlations mindouble().................................Minimum and maximum nonmissing value minindex().......................................Indices of minimums and maximums minmax().................................................Minimums and maximums missing().......................................Count missing and nonmissing values missingof()................................................Appropriate missing value mod()..................................................................Modulus moptimize().....................................................Model optimization more().....................................................Create–more–condition negate().......................................................Negate real matrix norm()....................................................Matrix and vector norms normal()................................Cumulatives,reverse cumulatives,and densities optimize().....................................................Function optimization panelsetup()...................................................Panel-data processing pathjoin()....................................................File path manipulation pinv().................................................Moore–Penrose pseudoinverse polyeval()........................................Manipulate and evaluate polynomials printf().............................................................Format output qrd()...........................................................QR decomposition qrinv().............................Generalized inverse of matrix via QR decomposition qrsolve()...................................Solve AX=B for X using QR decomposition quadcross().............................................Quad-precision cross products range()..................................................Vector over specified range rank().............................................................Rank of matrix Re()..................................................Extract real or imaginary part reldif()..................................................Relative/absolute difference rows()........................................Number of rows and number of columns rowshape().........................................................Reshape matrix runiform()..............................Uniform and nonuniform pseudorandom variates runningsum().................................................Running sum of vector schurd()......................................................Schur decomposition select()..............................................Select rows,columns,or indices setbreakintr()..................................................Break-key processing sign()...........................................Sign and complex quadrant functions sin()..........................................Trigonometric and hyperbolic functions sizeof().........................................Number of bytes consumed by object solve tol().....................................Tolerance used by solvers and inverters solvelower()..........................................Solve AX=B for X,A triangular solvenl().........................................Solve systems of nonlinear equations sort().......................................................Reorder rows of matrix6Contentssoundex().............................................Convert string to soundex code spline3()..................................................Cubic spline interpolation sqrt().................................................................Square root st addobs()....................................Add observations to current Stata dataset st addvar().......................................Add variable to current Stata dataset st data()...........................................Load copy of current Stata dataset st dir()..................................................Obtain list of Stata objects st dropvar()...........................................Drop variables or observations st global()........................Obtain strings from and put strings into global macros st isfmt()......................................................Whether valid%fmt st isname()................................................Whether valid Stata name st local()..........................Obtain strings from and put strings into Stata macros st macroexpand().......................................Expand Stata macros in string st matrix().............................................Obtain and put Stata matrices st numscalar().......................Obtain values from and put values into Stata scalars st nvar()........................................Numbers of variables and observations st rclear().....................................................Clear r(),e(),or s() st store().................................Modify values stored in current Stata dataset st subview()..................................................Make view from view st tempname()...............................................Temporary Stata names st tsrevar().....................................Create time-series op.varname variables st updata()....................................Determine or set data-have-changedflag st varformat().................................Obtain/set format,etc.,of Stata variable st varindex()...............................Obtain variable indices from variable names st varname()...............................Obtain variable names from variable indices st varrename()................................................Rename Stata variable st vartype()............................................Storage type of Stata variable st view()..........................Make matrix that is a view onto current Stata dataset st viewvars().......................................Variables and observations of view st vlexists()e and manipulate value labels stata()......................................................Execute Stata command stataversion().............................................Version of Stata being used strdup()..........................................................String duplication strlen()...........................................................Length of string strmatch()....................................Determine whether string matches pattern strofreal().....................................................Convert real to string strpos().....................................................Find substring in string strreverse()..........................................................Reverse string strtoname()...........................................Convert a string to a Stata name strtoreal().....................................................Convert string to real strtrim()...........................................................Remove blanks strupper()......................................Convert string to uppercase(lowercase) subinstr()...........................................................Substitute text sublowertriangle()...........................Return a matrix with zeros above a diagonal substr()......................................................Substitute into string substr()...........................................................Extract substring sum().....................................................................Sums svd()..................................................Singular value decomposition svsolve()..........................Solve AX=B for X using singular value decomposition swap()..............................................Interchange contents of variablesContents7 Toeplitz().........................................................Toeplitz matrices tokenget()........................................................Advanced parsing tokens()..................................................Obtain tokens from string trace().......................................................Trace of square matrix transpose()..................................................Transposition in place transposeonly().......................................Transposition without conjugation trunc()............................................................Round to integeruniqrows()..............................................Obtain sorted,unique values unitcircle()plex vector containing unit circle unlink().................................................................Erasefile valofexternal().........................................Obtain value of external global Vandermonde()................................................Vandermonde matrices vec().........................................................Stack matrix columns xl()............................................................Excelfile I/O class[M-6]Mata glossary of common terms Glossary........................................................................ Subject and author index...........................................................。

Package‘slanter’October14,2022Version0.2-0Date2021-05-09Title Slanted Matrices and Ordered ClusteringDescription Slanted matrices and ordered clustering for better visualization of similarity data. RoxygenNote7.0.2Encoding UTF-8License MIT+file LICENSEImports Matrix,pheatmap,pracma,statsLazyData trueSuggests knitr,rmarkdownVignetteBuilder knitrNeedsCompilation noAuthor Oren Ben-Kiki[aut,cre],Weizmann Institute of Science[cph]Maintainer Oren Ben-Kiki<*****************>Depends R(>=2.10)Repository CRANDate/Publication2021-05-0908:10:02UTCR topics documented:meristems (2)oclust (2)reorder_frame (3)reorder_hclust (4)sheatmap (4)slanted_orders (7)slanted_reorder (8)Index1012oclustmeristems Sample RNA data of similarity between batches of1000cells of tomatomeristem cells.DescriptionThis is a simple matrix where each entry is the similarity(correlation)between a pair of batches.Negative correlations were changed to zero to simplify the analysis.Usagedata(meristems)FormatA simple square matrix.Examplesdata(meristems)similarity<-meristemssimilarity[similarity<0]=0slanter::sheatmap(meristems,order_data=similarity,show_rownames=FALSE,show_colnames=FALSE) oclust Hierarchically cluster ordered data.DescriptionGiven a distance matrix for sorted objects,compute a hierarchical clustering preserving this order.That is,this is similar to hclust with the constraint that the result’s order is always1:N.Usageoclust(distances,method="ward.D2",order=NULL,members=NULL)Argumentsdistances A distances object(as created by stats::dist).method The clustering method to use(only ward.D and ward.D2are supported).order If specified,assume the data will be re-ordered by this order.members Optionally,the number of members for each row/column of the distances(bydefault,one each).reorder_frame3 DetailsIf an order is specified,assumes that the data will be re-ordered by this order.That is,the indices in the returned hclust object will refer to the post-reorder data locations,**not**to the current data locations.This can be applied to the results of slanted_reorder,to give a"plausible"clustering for the data.ValueA clustering object(as created by hclust).Examplesclusters<-slanter::oclust(dist(mtcars),order=1:dim(mtcars)[1])clusters$orderreorder_frame Reorder the rows of a frame.DescriptionYou’d expect data[order,]to"just work".It doesn’t for data frames with a single column,which happens for annotation data,hence the need for this function.Sigh.Usagereorder_frame(frame,order)Argumentsframe A data frame to reorder the rows of.order An array containing indices permutation to apply to the rows.ValueThe data frame with the new row orders.Examplesdf<-data.frame(foo=c(1,2,3))df[c(1,3,2),]slanter::reorder_frame(df,c(1,3,2))reorder_hclust Given a clustering of some data,and some ideal order we’d like touse to visualize it,reorder(but do not modify)the clustering to be asconsistent as possible with this ideal order.DescriptionGiven a clustering of some data,and some ideal order we’d like to use to visualize it,reorder(but do not modify)the clustering to be as consistent as possible with this ideal order.Usagereorder_hclust(clusters,order)Argumentsclusters The existing clustering of the data.order The ideal order we’d like to see the data in.ValueA reordered clustering which is consistent,wherever possible,the ideal order.Examplesclusters<-hclust(dist(mtcars))clusters$orderclusters<-slanter::reorder_hclust(clusters,1:length(clusters$order))clusters$ordersheatmap Plot a heatmap with values as close to the diagonal as possible.DescriptionGiven a matrix expressing the cross-similarity between two(possibly different)sets of entities,this will reorder it to move the high values close to the diagonal,for a better visualization.Usagesheatmap(data,...,order_data=NULL,annotation_col=NULL,annotation_row=NULL,order_rows=TRUE,order_cols=TRUE,squared_order=TRUE,same_order=FALSE,patch_cols_order=NULL,patch_rows_order=NULL,discount_outliers=TRUE,cluster_rows=TRUE,cluster_cols=TRUE,oclust_rows=TRUE,oclust_cols=TRUE,clustering_distance_rows="euclidian",clustering_distance_cols="euclidian",clustering_method="ward.D2",clustering_callback=NA)Argumentsdata A rectangular matrix to plot,of non-negative values(unless order_data is spec-ified)....Additionalflags to pass to pheatmap.order_data An optional matrix of non-negative values of the same size to use for computing the orders.annotation_col Optional data frame describing each column.annotation_row Optional data frame describing each row.order_rows Whether to reorder the rows.Otherwise,use the current order.order_cols Whether to reorder the columns.Otherwise,use the current order.squared_order Whether to reorder to minimize the l2norm(otherwise minimizes the l1norm).same_order Whether to apply the same order to both rows and columns(if reordering both).For a square matrix,may also contain’row’or’column’to force the order ofone axis to apply to both.patch_cols_orderOptional function that may be applied to the columns order,returning a betterorder.patch_rows_orderOptional function that may be applied to the rows order,returning a better order.discount_outliersWhether to do afinal order phase discounting outlier values far from the diago-nal.cluster_rows Whether to cluster the rows,or the clustering to use.cluster_cols Whether to cluster the columns,or the clustering to use.oclust_rows Whether to use oclust instead of hclust for the rows(if clustering them).oclust_cols Whether to use oclust instead of hclust for the columns(if clustering them).clustering_distance_rowsThe default method for computing row distances(by default,euclidian).clustering_distance_colsThe default method for computing column distances(by default,euclidian).clustering_methodThe default method to use for hierarchical clustering(by default,ward.D2and*not*complete).clustering_callbackIs not supported.DetailsIf you have an a-priori order for the rows and/or columns,you can prevent reordering either or bothby specifying order_rows=FALSE and/or order_cols=FALSE.Otherwise,slanted_orders is usedto compute the"ideal"slanted order for the data.By default,the rows and columns are ordered independently from each other.If the matrix isasymmetric but square(e.g.,a matrix of weights of a directed graph such as a K-nearest-neighborsgraph),then you can can specify same_order=TRUE to force both rows and columns to the sameorder.You can also specify same_order= row to force the columns to use the same order as therows,or same_order= column to force the rows to use the same order as the columns.You can also specify a patch_cols_order and/or a‘patch_rows_order‘function that takes thecomputed"ideal"order and returns a patched order.For example,this can be used to force specialvalues(such as"outliers")to the side of the heatmap.There are four options for controlling clustering:*By default,sheatmap will generate a clustering tree using oclust,to generate the"best"cluster-ing that is also compatible with the slanted order.*Request that sheatmap will use the same hclust as pheatmap(e.g.,oclust_rows=FALSE).Inthis case,the tree is reordered to be the"most compatible"with the target slanted order.That is, sheatmap will invoke reorder_hclust so that,for each node of the tree,the order of the two sub-trees will be chosen to best match the target slanted order.The end result need not be identical tothe slanted order,but is as close as possible given the hclust clustering tree.*Specify an explicit clustering(e.g.,cluster_rows=hclust(...)).In this case,sheatmap willagain merely reorder the tree but will not modify it.In addition,you can give this function any of the pheatmapflags,and it will just pass them on.Thisallows full control over the diagram’s features.Note that clustering_callback is not supported.In addition,the default clustering_methodhere is ward.D2instead of complete,since the only methods supported by oclust are ward.D and ward.D2.ValueWhatever pheatmap returns.Examplesslanter::sheatmap(cor(t(mtcars)))slanter::sheatmap(cor(t(mtcars)),oclust_rows=FALSE,oclust_cols=FALSE)pheatmap::pheatmap(cor(t(mtcars)))slanted_orders Compute rows and columns orders which move high values close tothe diagonal.DescriptionFor a matrix expressing the cross-similarity between two(possibly different)sets of entities,this produces better results than clustering(e.g.as done by pheatmap).This is because clustering does not care about the order of each two sub-partitions.That is,clustering is as happy with((2,1), (4,3))as it is with the more sensible((1,2),(3,4)).As a result,visualizations of similarities using naive clustering can be misleading.Usageslanted_orders(data,order_rows=TRUE,order_cols=TRUE,squared_order=TRUE,same_order=FALSE,discount_outliers=TRUE,max_spin_count=10)Argumentsdata A rectangular matrix containing non-negative values.order_rows Whether to reorder the rows.order_cols Whether to reorder the columns.squared_order Whether to reorder to minimize the l2norm(otherwise minimizes the l1norm).same_order Whether to apply the same order to both rows and columns.discount_outliersWhether to do afinal order phase discounting outlier values far from the diago-nal.max_spin_count How many times to retry improving the solution before giving up.ValueA list with two keys,rows and cols,which contain the order.Examplesslanter::slanted_orders(cor(t(mtcars)))slanted_reorder Reorder data rows and columns to move high values close to the diag-onal.DescriptionGiven a matrix expressing the cross-similarity between two(possibly different)sets of entities, this uses slanted_orders to compute the"best"order for visualizing the matrix,then returns the reordered monly used in pheatmap(slanted_reorder(data),...),and of course sheatmap does this internally for you.Usageslanted_reorder(data,order_data=NULL,order_rows=TRUE,order_cols=TRUE,squared_order=TRUE,same_order=FALSE,discount_outliers=TRUE)Argumentsdata A rectangular matrix to reorder,of non-negative values(unless order_data is specified).order_data An optional matrix of non-negative values of the same size to use for computing the orders.order_rows Whether to reorder the rows.order_cols Whether to reorder the columns.squared_order Whether to reorder to minimize the l2norm(otherwise minimizes the l1norm).same_order Whether to apply the same order to both rows and columns.discount_outliersWhether to do afinal order phase discounting outlier values far from the diago-nal.ValueA matrix of the same shape whose rows and columns are a permutation of the input.Examplesslanter::slanted_reorder(cor(t(mtcars)))Index∗datasetsmeristems,2meristems,2oclust,2reorder_frame,3reorder_hclust,4sheatmap,4slanted_orders,7slanted_reorder,810。

Package‘tseriesChaos’October14,2022Title Analysis of Nonlinear Time SeriesDate2013-04-29Version0.1-13.1Author Antonio,Fabio Di NarzoDepends R(>=2.2.0)Imports deSolveSuggests scatterplot3dLazyData yesLazyLoad yesDescription Routines for the analysis of nonlinear time series.Thiswork is largely inspired by the TISEAN project,by RainerHegger,Holger Kantz and Thomas Schreiber:<http://www.mpipks-dresden.mpg.de/~tisean/>.Maintainer Antonio Fabio Di Narzo<***********************>License GPL-2Repository CRANDate/Publication2019-01-0712:21:28UTCNeedsCompilation yesR topics documented:C2 (2)d2 (3)duffing.syst (4)embedd (4)false.nearest (5)lorenz.syst (6)lorenz.ts (6)Lyapunov exponent (7)mutual (8)plot.ami (9)12C2 plot.d2 (10)plot.false.nearest (10)print.d2 (11)print.false.nearest (12)recurr (12)rossler.syst (13)rossler.ts (14)sim.cont (14)stplot (15)Index17 C2Sample correlation integralDescriptionSample correlation integral for the specified length scaleUsageC2(series,m,d,t,eps)Argumentsseries time seriesm embedding dimensiond time delayt Theiler windoweps length scaleDetailsComputes the sample correlation integral on the provided time series for the specified length scale, and considering a time window t(see references).It uses a naif algorithm:simply returns the fraction of points pairs nearer than eps.Normally,you would use d2,which takes roughly the same time,but computes the correlation sum for multiple length scales and embedding dimensions at once.ValueThe sample correlation integral at eps length scale.Author(s)Antonio,Fabio Di Narzod23 ReferencesHegger,R.,Kantz,H.,Schreiber,T.,Practical implementation of nonlinear time series methods: The TISEAN package;CHAOS9,413-435(1999)See Alsod2d2Sample correlation integral(at multiple length scales)DescriptionComputes the sample correlation integral over a grid of neps length scales starting from eps.min, and for multiple embedding dimensionsUsaged2(series,m,d,t,eps.min,neps=100)Argumentsseries time seriesm max embedding dimensiond time delayt Theiler windoweps.min min length scaleneps number of length scales to evaluateDetailsComputes the sample correlation integral over neps length scales starting from eps.min,for em-bedding dimension1,...,m,considering a t time window(see references).The slope of the linear segment in the log-log plot gives an estimate of the correlation dimension(see the example). ValueMatrix.Column1:length scales.Column i=2,...,m+1:sample correlation integral for embedding dimension i-1.Author(s)Antonio,Fabio Di Narzo4embeddReferencesHegger,R.,Kantz,H.,Schreiber,T.,Practical implementation of nonlinear time series methods: The TISEAN package;CHAOS9,413-435(1999)Examplesd2(lorenz.ts,m=6,d=2,t=4,eps.min=2)duffing.syst Duffing oscillatorDescriptionDuffing oscillator system,to be used with sim.contDetailsTo be used with sim.contAuthor(s)Antonio,Fabio Di Narzoembedd Embedding of a time seriesDescriptionEmbedding of a time series with provided time delay and embedding dimension parameters.Usageembedd(x,m,d,lags)Argumentsx time seriesm embedding dimension(if lags missed)d time delay(if lags missed)lags vector of lags(if m and d are missed)DetailsEmbedding of a time series with provided delay and dimension parameters.false.nearest5ValueMatrix with columns corresponding to lagged time series.Author(s)Antonio,Fabio Di Narzo.Multivariate time series patch by Jonathan Shore.Exampleslibrary(scatterplot3d)x<-window(rossler.ts,start=90)xyz<-embedd(x,m=3,d=8)scatterplot3d(xyz,type="l")##embedding multivariate time seriesseries<-cbind(seq(1,50),seq(101,150))head(embedd(series,m=6,d=1))false.nearest Method of false nearest neighboursDescriptionMethod of false nearest neghbours to help deciding the optimal embedding dimensionUsagefalse.nearest(series,m,d,t,rt=10,eps=sd(series)/10)Argumentsseries time seriesm maximum embedding dimensiond delay parametert Theiler windowrt escape factoreps neighborhood diameterDetailsMethod of false nearest neighbours to help deciding the optimal embedding dimension.ValueFraction of false neighbors(first row)and total number of neighbors(second row)for each specified embedding dimension(columns)6lorenz.tsAuthor(s)Antonio,Fabio Di NarzoReferencesHegger,R.,Kantz,H.,Schreiber,T.,Practical implementation of nonlinear time series methods:The TISEAN package;CHAOS9,413-435(1999)Kennel M.B.,Brown R.and Abarbanel H.D.I.,Determining embedding dimension for phase-space reconstruction using a geometrical construction,Phys.Rev.A,V olume45,3403(1992).Examples(fn.out<-false.nearest(rossler.ts,m=6,d=8,t=180,eps=1,rt=3))plot(fn.out)lorenz.syst Lorenz systemDescriptionLorenz system,to be used with sim.contDetailsTo be used with sim.contAuthor(s)Antonio,Fabio Di Narzolorenz.ts Lorenz simulated time series,without noiseDescriptionLorenz simulated time series,without noise.Of each state of the system,we observe the euclideannorm.DetailsLorenz simulated time series,without noise,obtained with the call:lorenz.ts<-sim.cont(lorenz.syst, 0,100,0.05,start.x=c(5,5,5),parms=c(10,28,-8/3),obs.fun=function(x)sqrt(sum(x^2)))Author(s)Antonio,Fabio Di NarzoLyapunov exponent7 Lyapunov exponent Tools to evaluate the maximal Lyapunov exponent of a dynamic systemDescriptionTools to evaluate the maximal Lyapunov exponent of a dynamic system from a univariate time seriesUsagelyap_k(series,m,d,t,k=1,ref,s,eps)lyap(dsts,start,end)Argumentsseries time seriesm embedding dimensiond time delayk number of considered neighbourseps radius where tofind nearest neighbourss iterations along which follow the neighbours of each pointref number of points to take into accountt Theiler windowdsts Should be the output of a call to lyap_k(see the example)start Starting time of the linear bite of dstsend Ending time of the linear bite of dstsDetailsThe function lyap_k estimates the largest Lyapunov exponent of a given scalar time series using the algorithm of Kantz.The function lyap computes the regression coefficients of a user specified segment of the sequence given as input.Valuelyap_k gives the logarithm of the stretching factor in time.lyap gives the regression coefficients of the specified input sequence.Author(s)Antonio,Fabio Di Narzo8mutualReferencesHegger,R.,Kantz,H.,Schreiber,T.,Practical implementation of nonlinear time series methods: The TISEAN package;CHAOS9,413-435(1999)M.T.Rosenstein,J.J.Collins,C.J.De Luca,A practical method for calculating largest Lyapunov exponents from small data sets,Physica D65,117(1993)See Alsomutual,false.nearest for the choice of optimal embedding parameters.embedd to perform em-bedding.Examplesoutput<-lyap_k(lorenz.ts,m=3,d=2,s=200,t=40,ref=1700,k=2,eps=4)plot(output)lyap(output,0.73,2.47)mutual Average Mutual InformationDescriptionEstimates the average mutual information index(ami)of a given time series for a specified number of lagsUsagemutual(series,partitions=16,lag.max=20,plot=TRUE,...)Argumentsseries time seriespartitions number of binslag.max largest lagplot logical.If’TRUE’(the default)the ami is plotted...further arguments to be passed to the plot methodDetailsEstimates the mutual information index for a specified number of lags.The joint probability distri-bution function is estimated with a simple bi-dimensional density histogram.ValueAn object of class"ami",which is a vector containing the estimated mutual information index for each lag between0and lag.max.plot.ami9 Author(s)Antonio,Fabio Di NarzoReferencesHegger,R.,Kantz,H.,Schreiber,T.,Practical implementation of nonlinear time series methods: The TISEAN package;CHAOS9,413-435(1999)Examplesmutual(lorenz.ts)plot.ami Plotting average mutual information indexDescriptionPlotting method for objects inheriting from class’"ami"’.Usage##S3method for class amiplot(x,main=NULL,...)Argumentsx’"ami"’objectmain,...additional graphical argumentsDetailsPlots the ami for each lag in x.Author(s)Antonio,Fabio Di NarzoSee Alsomutual10plot.false.nearest plot.d2Plotting sample correlation integralsDescriptionPlotting method for objects inheriting from class’"d2"’.Usage##S3method for class d2plot(x,...)Argumentsx’"d2"’object...additional graphical argumentsDetailsPlots the sample correlation integrals in x in log-log scale,as a line for each considered embedding dimension.Author(s)Antonio,Fabio Di NarzoSee Alsod2plot.false.nearest Plotting false nearest neighbours resultsDescriptionPlotting method for objects inheriting from class’"false.nearest"’.Usage##S3method for class false.nearestplot(x,...)Argumentsx’"false.nearest"’object...additional graphical argumentsprint.d211 DetailsPlots the results of false.nearest.Author(s)Antonio,Fabio Di NarzoSee Alsofalse.nearestprint.d2Printing sample correlation integralsDescriptionPrinting method for objects inheriting from class’"d2"’.Usage##S3method for class d2print(x,...)Argumentsx’"d2"’object...additional arguments to’print’DetailsSimply calls plot.d2.Author(s)Antonio,Fabio Di NarzoSee Alsoplot.d2,d212recurr print.false.nearest Printing false nearest neighbours resultsDescriptionPrinting method for objects inheriting from class’"false.nearest"’.Usage##S3method for class false.nearestprint(x,...)Argumentsx’"false.nearest"’object...additional arguments to’print’DetailsPrints the table of results of false.nearest.Author(s)Antonio,Fabio Di NarzoSee Alsoplot.false.nearest,false.nearestrecurr Recurrence plotDescriptionRecurrence plotUsagerecurr(series,m,d,start.time=start(series),end.time=end(series),...)rossler.syst13Argumentsseries time seriesm embedding dimensiond time delaystart.time starting time window(in time units)end.time ending time window(in time units)...further parameters to be passed to filled.contourDetailsProduces the recurrence plot,as proposed by Eckmann et al.(1987).White is maximum distance, black is minimum.warningBe awared that number of distances to store goes as n^2,where n=length(window(series, start=start.time,end=end.time))!Author(s)Antonio,Fabio Di NarzoReferencesEckmann J.P.,Oliffson Kamphorst S.and Ruelle D.,Recurrence plots of dynamical systems,Euro-phys.Lett.,volume4,973(1987)Examplesrecurr(lorenz.ts,m=3,d=2,start.time=15,end.time=20)rossler.syst Roessler system of equationsDescriptionRoessler system of equationsDetailsTo be used with sim.cont.Author(s)Antonio,Fabio Di Narzo14sim.cont rossler.ts Roessler simulated time series,without noiseDescriptionRoessler simulated time series,without noise.Of each state of the system,we observe thefirstcomponent.DetailsRoessler simulated time series,without noise,obtained with the call:rossler.ts<-sim.cont(rossler.syst,start=0,end=650,dt=0.1,start.x=c(0,0,0),parms=c(0.15,0.2,10))Author(s)Antonio,Fabio Di Narzosim.cont Simulates a continuous dynamic systemDescriptionSimulates a dynamic system of provided ODEsUsagesim.cont(syst,start.time,end.time,dt,start.x,parms=NULL,obs.fun=function(x)x[1]) Argumentssyst ODE systemstart.time starting timeend.time ending timedt time between observationsstart.x initial conditionsparms parameters for the systemobs.fun observed function of the stateDetailsSimulates a dynamic system of provided es lsoda in odesolve for numerical integrationof the system.ValueThe time series of the observed function of the system’s stateAuthor(s)Antonio,Fabio Di NarzoSee Alsolorenz.syst,rossler.syst,duffing.systExamplesrossler.ts<-sim.cont(rossler.syst,start=0,end=650,dt=0.1,start.x=c(0,0,0),parms=c(0.15,0.2,10))stplot Space-time separation plotDescriptionSpace-time separation plotUsagestplot(series,m,d,idt=1,mdt)Argumentsseries time seriesm embedding dimensiond time delayidt observation steps in each iterationmdt number of iterationsDetailsProduces the space-time separation plot,as introduced by Provenzale et al.(1992),which can be used to decide the Theiler time window t,which is required in many other algorithms in this package.It plots the probability that two points in the reconstructed phase-space have distance smaller than epsilon in function of epsilon and of the time t between the points,as iso-lines at levels10%,20%, ...,100%.Valuelines of costant probability at10%,20%,...,100%.Author(s)Antonio,Fabio Di NarzoReferencesKantz H.,Schreiber T.,Nonlinear time series analysis.Cambridge University Press,(1997)Provenzale A.,Smith L.A.,Vio R.and Murante G.,Distiguishing between low-dimensional dy-namics and randomness in measured time series.Physica D.,volume58,31(1992)See Alsofalse.nearest,d2,lyap_kExamplesstplot(rossler.ts,m=3,d=8,idt=1,mdt=250)Index∗datagenduffing.syst,4lorenz.syst,6rossler.syst,13sim.cont,14∗datasetslorenz.ts,6rossler.ts,14∗hplotplot.ami,9plot.d2,10plot.false.nearest,10∗manipembedd,4∗tsC2,2d2,3false.nearest,5Lyapunov exponent,7mutual,8print.d2,11print.false.nearest,12recurr,12stplot,15C2,2d2,2,3,3,10,11,16 duffing.syst,4,15 embedd,4,8false.nearest,5,8,11,12,16 lorenz.syst,6,15lorenz.ts,6lyap(Lyapunov exponent),7 lyap_k,16lyap_k(Lyapunov exponent),7 Lyapunov exponent,7mutual,8,8,9plot.ami,9plot.d2,10,11plot.false.nearest,10,12print.d2,11print.false.nearest,12recurr,12rossler.syst,13,15rossler.ts,14sim.cont,4,6,13,14stplot,1517。

5.打开光源和设置您的采集参数，以便达到峰值水平的建议。

然后按一下next。

第三页的向导。

6.如果您尚未这样做的话，一个样品的溶剂成盆地采取参考谱。

你必须采取什么样的参考频谱测量吸光度面前。

Note不要把自己的样品中的路径时，参考谱，只有溶剂。

7.点击店铺参考谱（）图标在屏幕上。

此命令只商店参考频谱的记忆。

您必须点击保存谱（）工具栏上的图标或选择文件|保存|保存光谱采集从菜单栏中永久保存参考谱到磁盘。

然后按一下next 。

第四页的向导。

8.阻断光路的光谱仪，取消塔尔博特/灯启用方块，或把光源关闭。

然后，采取暗谱按一下。

你必须采取什么样的黑暗之前频谱测量吸光度。

此命令只商店黑暗频谱在内存中。

您必须点击保存谱（）工具栏上的图标或选择文件|保存|保存光谱采集从菜单栏中永久保存到磁盘的频谱。

Note如果可能的话，不要关闭光源时，黑暗的频谱。

如果您必须关闭光源储存黑暗谱，留出足够的时间对灯的热身再继续您的实验。

后灯暖和起来，存储一个新的参考。

9.把样品到位，确保光路是明确的。

然后，按一下finish。

如果您已经采取的一个或多个吸光度测量，则会出现一个对话框，要求您指定是否要显示新的数据在一个新的图形，或在现有的图形。

请注意以下几点变化在屏幕上：实验模式中列出的数据来源和数据次数窗格改变吸光度模式。

这些单位的上图窗格改变吸光度（OD值）10.要永久保存到磁盘上的频谱，点击保存谱（）工具栏上的图标。

note如果您更改任何采样变量（积分时间，平均，平滑，纤维大小，等等），您必须保存一个新的黑暗和参考谱。

浓度实验发现未知物质浓度的溶液中，您必须首先采取光谱扫描的一系列解决方案，不同的已知浓度的同一物质。

你开始这一进程，同时考虑了吸收光谱的解决方案，最高的浓度。

虽然经历精灵，当问及存储参考谱，把你的盆地充分溶剂中的光谱仪的盆地持有。

有三种方法可以用来计算浓度样品：@使用比尔定律（见计算浓度的比尔定律）@校准从解决已知浓度（见标定从解决方案的已知浓度）@载入中先前存储校准从文件（见载荷校准从现有的档案）的浓度测量安装向导，您可以选择其中一个，这三个方法。

UNICAM原子吸收光谱仪SOLAAR32 UNICAM 原子吸收 SOLLAAR32 软件常规手册目录1.引言1.1视窗Windows 951.2视窗Windows 95 帮助文件1.2.1显示视窗Windows 95 帮助信息2.SOLLAAR32程序的安装2.1软件的保护2.2原子光谱仪所用的软件2.3 GFTV(石罴炉进样监控系统)软件2.4软件的安装2.4.1SOLLAR32 数据库软件2.4.2安装升级的SOLAAR32 AutoQC(自动质量控制)软件2.4.3安装GFTV软件和接口组件2.5 SOLLAR 原子吸收组2.5.1阅读Read Me 信息(系统程序的介绍)2.6SOLLAAR32程序的删除2.7SOLLAAR32程序及打印机2.8SOLLAAR32应用程序的启动2.8.1SOLLAAR32的启动2.8.2为SOLLAAR32建立启动菜单快捷键2.8.3为SOLLAAR32建立桌面快捷键2.8.4用视窗Windows 95自动启动SOLLAAR322.9连接SOLAAR32程序3.SOLAAR32软件3.1概述3.1.1方法3.1.2步骤3.1.3样品的说明3.1.4结果3.1.5软件的安全保密性3.2 SOLAAR32的使用3.2.1 SOLAAR32状态窗口3.2.2 SOLAAR32特性页面3.2.3 SOLAAR32对话框3.2.4 SOLAAR32菜单1UNICAM原子吸收光谱仪SOLAAR322 3.2.5 SOLAAR32工具栏3.3.5 SOLAAR32状态栏3.3在线帮助3.3.1 主题3.3.2 学会使用在线帮助系统3.3.3 显示SOLAAR32帮助主题:对话内容表3.3.4 为对话框或特性页快速显示特定文本帮助3.3.5 为状态窗口快速显示特定文本帮助3.3.6为数据输入字段快速显示特定文本帮助4.开始4.1一次简单的火焰分析过程4.1.1开始前的工作4.1.2建立方法4.1.3设定分析顺序4.1.4建立分析样品的详细资料4.1.5安装空心阴极灯4.1.6校准空心阴极灯4.1.7点火,调整燃烧头和雾化器撞击球位置4.1.8进行分析4.1.9审查结果4.1.10打印结果4.2一次简单的石墨炉分析过程4.2.1开始前的工作4.2.2建立方法4.2.3设定分析顺序和样品的详细资料4.2.4安装和校准空心阴极灯4.2.5安装和校准空石墨炉4.2.6调整石墨炉自动进样器4.2.7点火,调整燃烧头位置和雾化器撞击球4.2.8进行分析4.2.9审查并打印结果4.3一次简单的气体分析过程4.3.1开始前的工作4.3.2建立方法4.3.3设定分析顺序和样品的详细资料4.3.4安装和校准空心阴极灯UNICAM原子吸收光谱仪SOLAAR323 4.3.5安装SOLAAR VP90 连续气体流动系统4.3.6安装SOLAAR EC90 电热原子吸收池附件4.3.7准直SOLAAR VP90 原子吸收池4.3.8准直SOLAAR VP90 载气流量，稳定化时间及基线延迟参数4.3.9点火4.3.10没定SOLAAR EC90 石墨炉温度参数4.3.11进行分析4.3.12审查并打印结果5.用户测试软件5.1概述5.2用户检测5.2.1光度计/数据站之间通讯的检测5.2.2光度计/自动进样器之间通讯的检测5.2.3光度计/石墨炉之间通讯的检测5.2.4气体箱泄漏的检测5.2.5光度计模拟电路的检测5.2.6光度计软件版本号的检测5.2.7光度计设置的检测5.2.8新氘(D2)灯5.2.9读数传感器的检测5.3 SOLAAR32 客户检测软件的使用5.3.1客户检测菜单5.3.2客户检测工具条5.3.3操作指导信息窗口5.3.4检测窗口5.3.5在开始之前5.3.6启动客户检测软件5.3.7进行测试5.3.8测试结果6 本系列仪器的异同1 引言祝贺您购卖Unicam SOLAAR 原子吸收光谱仪和SOLAAR32软件.我们竭尽全力为您提供一台功能强大,易于使用的光谱仪. 满足您进行元素分析的所有需求,我们衷心希望您对您的仪器操作及尽快地熟悉起来.UNICAM原子吸收光谱仪SOLAAR32此手册要为您提供SOLAAR32数据工作站软件的安装,使用的指导.本软件应与仪器所配的SOLAAR 用户手册一起使用,特别是:. 硬件手册 - 该手册叙述了光谱仪及其附件的使用和各项功能.. 分析方法手册- 该手册叙述了原子吸收光谱技术的物理和化学原理.同时就样品制备和相关的课题给予实际的指导.. SOLAAR32 在线帮助系统 - 此系统为该软件提供了详细使用说明,而且还包括在线“菜谱”,并给出了所有可用原子吸收光谱测定元素的基本参数.这将在下面的3.3节中进行详细的说明.对SOLAAR969系统也可用Local Controller 而不是Data Station进行设定. Local Controller 软件的使用将在该光谱仪操作手册中予以说明,在此不予讨论.1.1视窗Windows95您的SOLAAR32数据处理工作站软件是在Windows95系统下工作的.该软件是具有强大图形功能新一代帮助操作的系统,它的设计最大限度发挥现代微处理器功能和速度.它可在工作中为您提供许多有用和方便在使用solaar32应用程序之前，你应熟悉和会使用计算机及操作系统。

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变更摘要目录前言 (1)警报消息 (1)第一章NANOSAT 便携卫星站简介 (2)1.1N ANO S AT便携卫星站功能特点 (2)1.2基本参数 (3)第二章NANOSAT便携卫星站使用 (7)2.1天线结构说明 (7)2.2便携卫星站场地要求 (8)2.3便携站的固定 (9)2.4面板接口说明 (10)2.5设备启动及自检 (11)2.6天线面安装 (12)2.7天线收藏 (13)第三章NANOSAT COMPASS说明 (15)3.1界面说明 (15)3.2设备连接 (17)3.2.1设备IP说明 (17)3.2.2设备连接说明 (18)3.3寻星操作 (18)3.3.1对星参数设置表格 (18)3.3.2寻星操作 (19)3.4编码设置 (21)3.4.1H.264编码格式设置 (21)3.4.2MPEG2编码格式设置 (21)3.5调制设置 (22)3.6M UX配置（O PINION） (24)3.7D ATA配置（O PINION） (24)3.8BISS配置 (25)3.9预设参数说明 (25)第四章常见问题及应急处理 (26)4.1设备硬件问题 (26)4.2软件问题 (26)前言感谢您购买Cogent NanoSat 便携卫星地面站。

Package‘semnova’October14,2022Type PackageTitle Latent Repeated Measures ANOV AVersion0.1-6Author Benedikt Langenberg[aut,cre],Axel Mayer[ctb]Imports lavaan,Matrix,parallel,MASS,stats,methodsSuggests testthat,knitr,rmarkdownDepends R(>=3.4.0)Description Latent repeated measures ANOV A(L-RM-ANOV A)is a structuralequation modeling based alternative to traditional repeated measures ANOV A.L-RM-ANOV A extends the latent growth components approach byMayer et al.(2012)<doi:10.1080/10705511.2012.713242>and introduceslatent variables to repeated measures analysis.Maintainer Benedikt Langenberg<*****************************>License GPL(>=2)Encoding UTF-8LazyData trueRoxygenNote7.1.0VignetteBuilder knitrNeedsCompilation noRepository CRANDate/Publication2020-06-1905:50:07UTCR topics documented:anova,lgc-method (2)create_mmodel (2)lgc (3)lgc-class (5)semnova (5)semnova_test_data (7)summary,lgc-method (7)12create_mmodel Index8 anova,lgc-method Comparing thefit of LGC objects.DescriptionComparing thefit of LGC objects.Usage##S4method for signature lgcanova(object,...)Argumentsobject lgc object.An lgc object to be compared against other lgc objects....lgc object.More lgc objects to be compared.create_mmodel Specifying a measurement model.DescriptionSpecifying a measurement model.Usagecreate_mmodel(...,list=NULL,lv_scaling="effect",invariance=NULL) Argumentsd arguments each representing a latent variable.The arguments are char-acter vectors containing the variable names the latent variables are measuredby.list List.Each list element represents a latent variable.List elements are character vectors containing the variable names the latent variables are measured by.lv_scaling Character vector.Defines the strategy for latent variable scaling.Default is lv_scaling="effect".Possible strategies are:c("effect","referent").invariance Not yet implemented.ValueObject of classe mmodel.lgc3 Examplesmmodel<-create_mmodel(A1B1="var1",A2B1="var2",A3B1="var3",A1B2="var4",A2B2="var5",A3B2="var6",lv_scaling="referent")lgc General function to specify a general latent growth components model.DescriptionGeneral function to specify a general latent growth components model.Usagelgc(data,mmodel,C_matrix,hypotheses=NULL,covariates=NULL,groups=NULL,append=NULL,verbose=FALSE,compound_symmetry=FALSE,sphericity=FALSE,multiv_tests=c("wilks","wald"),univ_tests=NULL,randomization=list(ncores=1,nsamples=1000),...)Argumentsdata Dataframe.Data object to be passed to lavaan.mmodel Object of class mmodel.If not provided,manifest variables from the formula object will be used.Otherwise,use create_mmodel()to specify measurementmodel.C_matrix Contrast matrix.Must be invertible.4lgchypotheses List of numeric vectors.Each list element represents a hypothesis.For each hy-pothesis,the contrasts indicated by the elements of the vectors are tested againstzero.covariates Not implemented yet.groups Not implemented yet.append Character.Syntax that is to be appended to lavaan syntax.verbose Boolean.Print details during procedure.compound_symmetryBoolean.When set to TRUE,compound symmetry is assumed.sphericity Boolean or formula.When set to TRUE,sphericity is assumed for all effects.multiv_tests Character vector.Multivariate test statistics that are to be computed.Possiblestatistics are:c("wilks","wald").Default is multiv_tests=c("wilks","wald").univ_tests Character vector.Univariate test statistics that are to be computed.Possiblestatistics are:c("F").Default is univ_tests=NULL.randomization Not yet supported....Additional arguments to be passed to lavaan.ValueFunction returns an lgc e summary(object)to print hypotheses.Otherwise use object@sem_obj to get access to the underlying lavaan object.Examplesset.seed(323412431)data("semnova_test_data",package="semnova")mmodel<-create_mmodel(A1B1="var1",A2B1="var2",A3B1="var3",A1B2="var4",A2B2="var5",A3B2="var6",lv_scaling="referent")hypotheses<-list(Intercept=c(1),A=c(2,3),B=c(4),AB=c(5,6))C_matrix<-matrix(lgc-class5 c(1,1,0,1,1,0,1,0,1,1,0,1,1,-1,-1,1,-1,-1,1,1,0,-1,-1,0,1,0,1,-1,0,-1,1,-1,-1,-1,1,1),nrow=6)fit_lgc<-lgc(data=semnova_test_data,mmodel,C_matrix,hypotheses)summary(fit_lgc)lgc-class LGC Class.DescriptionLGC Class.semnova Latent repeated-measures ANOVA using the LGC approachDescriptionFunction specifies an LGC model.The idata object is used to create the contrast matrix that is passed to the lgc()function.Typical hypotheses are specified as well.Usagesemnova(formula,idesign,idata,data,mmodel=NULL,covariates=NULL,groups=NULL,append=NULL,icontrasts=c("contr.poly","contr.sum"),verbose=FALSE,compound_symmetry=FALSE,sphericity=FALSE,multiv_tests=c("wilks","wald"),univ_tests=c("F"),randomization=list(ncores=1,nsamples=1000),...)6semnovaArgumentsformula Formula.idesign Formula.Within-subjects design formula.idata Dataframe.The dataframe contains the factorial design.data Dataframe.Data object to be passed to lavaan.mmodel Object of class mmodel.If not provided,manifest variables from the formulaobject will be used.Otherwise,use create_mmodel()to specify measurementmodel.covariates Not implemented yet.groups Not implemented yet.append Character vector.Syntax that is to be appended to lavaan syntax.icontrasts Character e this argument to select the type of contrasts to be used.Default is c("contr.sum","contr.poly")(not ordered,ordered).verbose Boolean.Print details during procedure.compound_symmetryBoolean.When set to TRUE,compound symmetry is assumed among depen-dent variables.sphericity Boolean or formula.When set to TRUE,sphericity is assumed for all effects.multiv_tests Character vector.Multivariate test statistics that are to be computed.Possiblestatistics are:c("wilks","wald").Default is multiv_tests=c("wilks","wald").univ_tests Character vector.Univariate test statistics that are to be computed.Possiblestatistics are:c("F").Default is univ_tests=NULL.randomization Not yet supported....Additional arguments to be passed to lavaan.ValueFunction returns an lgc e summary(object)to print hypotheses.Otherwise use object@sem_obj to get access to the underlying lavaan object.Examplesset.seed(323412431)data("semnova_test_data",package="semnova")idata<-expand.grid(A=c("A1","A2","A3"),B=c("B1","B2"))mmodel<-create_mmodel(A1B1="var1",A2B1="var2",A3B1="var3",A1B2="var4",semnova_test_data7 A2B2="var5",A3B2="var6",lv_scaling="referent")fit_semnova<-semnova(formula=cbind(A1B1,A2B1,A3B1,A1B2,A2B2,A3B2)~1,data=semnova_test_data,idata=idata,idesign=~A*B,mmodel=mmodel)summary(fit_semnova)semnova_test_data This data set serves for examples and tests.DescriptionThis is a simulated data set that100observation of six normally distributed variables with mean= 0,variance=1and covariance0.5.Usagesemnova_test_dataFormatA data frame with100rows and6variables:summary,lgc-method Printing the summary for an LGC object.DescriptionPrinting the summary for an LGC object.Usage##S4method for signature lgcsummary(object,...)Argumentsobject lgc object.The object to get a summary about....Additional arguments.Currently none supported.Index∗datasetssemnova_test_data,7anova,lgc-method,2create_mmodel,2lgc,3lgc-class,5semnova,5semnova_test_data,7summary,lgc-method,78。

v1.0© 2022 Eaton All Rights ReservedPage 1 of 3OverviewA security vulnerability was discovered in the Foreseer software. Foreseer connects an operation’s vast array of devices to assist in the reduction of energy consumption and avoid unplanned downtime caused by the failures of critical systems.Vulnerability DetailsCVE-2022-33859CVSS v3 Base Score – 8.1 CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:C/C:L/I:H/A:HCWE-434: Unrestricted file uploadA threat actor may upload arbitrary files using the file upload feature and execute a remote code. Remediation & MitigationMitigation InstructionsThe vulnerability may be mitigated by disabling the file upload feature. A Foreseer user with administrative access to the Foreseer server is required to perform the task. A server restart will also be required. This change will not affect the general use of the software.Customers should follow the steps in the file below to disable the feature.Foreseer- Steps to Disable File Upload.pdfCustomers should also use only Eaton Signed software executables for updates (right Click on executable properties and check for Eaton signature in the digital signatures tab).RemediationEaton is currently developing software version 7.6 to allow for secure use of the ‘Update Server’ (File upload) feature without having to disable the functionality. Customers are strongly encouraged to upgrade to the latest version of Foreseer and Foreseer Reporting Service (FRS) as soon as the release is available. Eaton strongly recommends that customers continue to follow the general security best practices listed below to ensure the security of their systems.Eaton Vulnerability AdvisoryETN-VA-2022-1007: Foreseer EPMS VulnerabilitiesGeneral Security Best Practices•Restrict exposure to external network for all control system devices and/or systems and ensure that they are not directly accessible from open internet.•Deploy control system networks and remote devices behind barrier devices(e.g. firewall, data diode), and isolate them from the business network.•Remote access to control system network shall be made available only on need to use basis.Remote access shall use secure methods, such as Virtual Private Networks (VPNs), updated to the most current version available.•Regularly update/patch software/applications to latest versions available, as applicable.•Enable audit logs on all devices and applications.•Disable/deactivate unused communication channels, TCP/UDP ports and services (e.g., SNMP, FTP, BootP, DCP, etc.) on the devices.•Create security zones for devices with common security requirements using barrier devices(e.g.firewall, data diode).•Change default passwords following initial startup. Use complex secure passwords.Perform regular security assessments and risk analysis of your control systems.For more information on Security best practices refer to the following:•Cybersecurity Considerations for Electrical Distribution Systems (WP152002EN) Additional Support and InformationFor additional information, including a list of vulnerabilities that have been reported on our products and how to address them, please visit our Cybersecurity web site /cybersecurity, or contact us at ***************; **************************.Legal Disclaimer:TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, INFORMATION PROVIDED IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT WARRANTY OF ANY KIND. EATON, ITS AFFILIATES, SUBSIDIARIES, AND AUTHORIZED REPRESENTATIVES HEREBY DISCLAIM ALL WARRANTIES AND CONDITIONS OF ANY KIND EITHER EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE, INCLUDING, BUT WITHOUT LIMITATION, ANY IMPLIED WARRANTIES AND/OR CONDITIONS OF SECURITY, COMPLETENESS, TIMELINESS, ACCURACY, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR REVIEWING THE USER MANUAL FOR YOUR DEVICES AND GAINING KNOWLEDGE ON CYBERSECURITY MEASURES. YOU SHOULD TAKE NECESSARY STEPS TO ENSURE THAT YOUR DEVICE OR SOFTWARE IS PROTECTED, INCLUDING CONTACTING AN EATON PROFESSIONAL. SOME JURISDICTIONS DO NOT ALLOW THE EXCLUSION OF IMPLIED WARRANTIES OR LIMITATIONS, SO THE ABOVE LIMITATIONS MAY NOT APPLY. TO THE EXTENT PERMITTED BY LAW, IN NO EVENT WILL EATON OR ITS AFFILIATES, OFFICERS, DIRECTORS, AND/OR EMPLOYEES, BE LIABLE FOR ANY LOSS OR DAMAGE OF ANY KIND WHATSOEVER, INCLUDING, BUT NOT LIMITED TO, ANYDIRECT, INDIRECT, INCIDENTAL, SPECIAL, STATUTORY, PUNITIVE, ACTUAL, LIQUIDATED, EXEMPLARY, CONSEQUENTIAL OR OTHER DAMAGES, EVEN IF EATON HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THE USE OF THIS NOTIFICATION, INFORMATION CONTAINED HEREIN, OR MATERIALS LINKED TO IT ARE AT YOUR OWN RISK. EATON RESERVES THE RIGHT TO UPDATE OR CHANGE THIS NOTIFICATION AT ANY TIME AND AT ITS SOLE DISCRETION.About Eaton:Eaton is a power management company. We provide energy-efficient solutions that help our customers effectively manage electrical, and mechanical power more efficiently, safely, and sustainably. Eaton is dedicated to improving the quality of life and the environment through the use of power management technologies and services. Eaton has approximately 85,000 employees and sells products to customers in more than 175 countries.Revision Control:Office:Eaton, 1000 Eaton BoulevardCleveland, OH 44122, United States。

Title Intro—Introduction to the Mata manualContents Description Remarks and examples ReferencesAlso seeContentsSection Description[M-1]Introduction and advice[M-2]Language definition[M-3]Commands for controlling Mata[M-4]Categorical guide to Mata functions[M-5]Alphabetical index to Mata functions[M-6]Mata glossary of common termsDescriptionMata is a matrix programming language that can be used by those who want to perform matrix calculations interactively and by those who want to add new features to Stata.The Mata Reference Manual is comprehensive.If it seems overly comprehensive and too short on explanation as to why things work the way they do and how they could be used,we have a suggestion.See The Mata Book by William Gould(428pages)or An Introduction to Stata Programming by Christopher Baum(412pages).Baum’s book assumes that you are familiar with Stata but new to programming.Gould’s book assumes that you have some familiarity with programming and goes on from there.The books go well together.Remarks and examples This manual is divided into six sections.Each section is organized alphabetically,but there is an introduction in front that will help you get around.If you are new to Mata,here is a helpful reading list.Start by reading[M-1]First Introduction andfirst session[M-1]Interactive Using Mata interactively[M-1]How How Mata works12Intro—Introduction to the Mata manualYou mayfind other things in section[M-1]that interest you.For a table of contents,see[M-1]Intro Introduction and adviceWhenever you see a term that you are unfamiliar with,see[M-6]Glossary Mata glossary of common termsNow that you know the basics,if you are interested,you can look deeper into Mata’s programming features:[M-2]Syntax Mata language grammar and syntax[M-2]Syntax is pretty dense reading,but it summarizes nearly everything.The other entries in[M-2] repeat what is said there but with more explanation;see[M-2]Intro Language definitionbecause other entries in[M-2]will interest you.If you are interested in object-oriented programming, be sure to see[M-2]class.Along the way,you will eventually be guided to sections[M-4]and[M-5].[M-5]documents Mata’s functions;the alphabetical order makes it easy tofind a function if you know its name but makes learning what functions there are hopeless.That is the purpose of[M-4]—to present the functions in logical order.See[M-4]Intro Categorical guide to Mata functionsMathematical[M-4]Matrix Matrix functions[M-4]Solvers Matrix solvers and inverters[M-4]Scalar Scalar functions[M-4]Statistical Statistical functions[M-4]Mathematical Other important functionsUtility and manipulation[M-4]Standard Functions to create standard matrices[M-4]Utility Matrix utility functions[M-4]Manipulation Matrix manipulation functionsStata interface[M-4]Stata Stata interface functionsDate and time[M-4]Dates Date and time functionsString,I/O,and programming[M-4]String String manipulation functions[M-4]IO I/O functions[M-4]Programming Programming functionsIntro—Introduction to the Mata manual3 ReferencesBaum,C.F.2016.An Introduction to Stata Programming.2nd ed.College Station,TX:Stata Press.Gould,W.W.2018.The Mata Book:A Book for Serious Programmers and Those Who Want to Be.College Station, TX:Stata Press.Also see[M-1]First—Introduction andfirst session[M-6]GlossaryStata,Stata Press,and Mata are registered trademarks of StataCorp LLC.Stata andStata Press are registered trademarks with the World Intellectual Property Organization®of the United Nations.Other brand and product names are registered trademarks ortrademarks of their respective companies.Copyright c 1985–2023StataCorp LLC,College Station,TX,USA.All rights reserved.。

Package‘AnanseSeurat’November11,2023Title Construct ANANSE GRN-Analysis SeuratVersion1.2.0Description Enables gene regulatory network(GRN)analysis on single cell clusters, using the GRN analysis software'ANANSE',Xu et al.(2021)<doi:10.1093/nar/gkab598>.Export data from'Seurat'objects,for GRN analysis by'ANANSE'implemented in'snakemake'.Finally,incorporate results for visualizationand interpretation.License Apache License(>=2)BugReports https:///JGASmits/AnanseSeurat/issuesURL https:///JGASmits/AnanseSeurat/Encoding UTF-8RoxygenNote7.2.3Imports dplyr,ggplot2,ggpubr,magrittr,patchwork,png,purrr,rlang,Seurat,stringr,utils,Suggests covr,knitr,rmarkdown,Signac,testthat(>=3.0.0)Config/testthat/edition3VignetteBuilder knitrDepends R(>=3.50)NeedsCompilation noAuthor Jos Smits[aut,cre,cph](<https:///0000-0001-5858-3905>), Huiqing Jo Zhou[aut,fnd,cph](<https:///0000-0002-2434-3986>)Maintainer Jos Smits<*****************.nl>Repository CRANDate/Publication2023-11-1121:43:17UTC12config_scANANSE R topics documented:config_scANANSE (2)DEGS_scANANSE (3)export_ATAC_maelstrom (4)export_ATAC_scANANSE (5)export_CPM_scANANSE (5)Factor_Motif_Plot (6)import_seurat_maelstrom (7)import_seurat_scANANSE (8)Maelstrom_Motif2TF (9)per_cluster_df (10)Index11 config_scANANSE config_scANANSEDescriptionThis functions generates a samplefile and configfile for running Anansnake based on the seurat objectUsageconfig_scANANSE(seurat_object,output_dir,min_cells=50,cluster_id="seurat_clusters",genome="./scANANSE/data/hg38",additional_contrasts=c())Argumentsseurat_object seurat objectoutput_dir directory where thefiles are outputtedmin_cells minimum of cells a cluster needs to be exportedcluster_id ID used forfinding clusters of cellsgenome genomepy name or location of the genome fastqfileadditional_contrastsadditional contrasts to add between clusters within cluster_IDValueNone,outputs snakemake configfile in the output directoryDEGS_scANANSE3Examplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat )) config_scANANSE(sce_small,min_cells=2,output_dir=tempdir())DEGS_scANANSE DEGS_scANANSEDescriptionCalculate the differential genes needed for ananse influenceUsageDEGS_scANANSE(seurat_object,output_dir,min_cells=50,cluster_id="seurat_clusters",genome="./scANANSE/data/hg38",RNA_count_assay="RNA",additional_contrasts="None")Argumentsseurat_object seurat objectoutput_dir directory where thefiles are outputtedmin_cells minimum of cells a cluster needs to be exportedcluster_id ID used forfinding clusters of cellsgenome path to the genome folder used for the anansnake configfileRNA_count_assayassay containing the RNA dataadditional_contrastsadditional contrasts to add between clusters within cluster_IDValueNone,outputs DEGfiles in the output directoryExamplessce_small<-readRDS(system.file("extdata","sce_obj_tiny.Rds",package= AnanseSeurat )) DEGS_scANANSE(sce_small,min_cells=2,output_dir=tempdir())4export_ATAC_maelstrom export_ATAC_maelstrom export_seurat_MaelstromDescriptionnormalize and export the peak table of a seurat object based on clustersUsageexport_ATAC_maelstrom(seurat_object,output_dir,min_cells=50,ATAC_peak_assay="peaks",cluster_id="seurat_clusters",select_top_rows=TRUE,n_top_rows=1e+05)Argumentsseurat_object objectoutput_dir directory where thefiles are outputtedmin_cells minimum of cells a cluster needs to be exportedATAC_peak_assayassay of the seurat object containing the peaks and peakcounts cluster_id ID used forfinding clusters of cellsselect_top_rowsonly output the top variable rows,or all rows if falsen_top_rows amount of variable rows to exportValueNone,outputs maelstrom peak counts table in the output directoryExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat )) config_scANANSE(sce_small,min_cells=2,output_dir=tempdir())export_ATAC_scANANSE5export_ATAC_scANANSE export_ATAC_scANANSEDescriptionThis functions exports ATAC values from a seurat objectUsageexport_ATAC_scANANSE(seurat_object,output_dir,min_cells=50,ATAC_peak_assay="peaks",cluster_id="seurat_clusters")Argumentsseurat_object objectoutput_dir directory where thefiles are outputtedmin_cells minimum of cells a cluster needs to be exportedATAC_peak_assayassay of the seurat object containing the peaks and peakcounts cluster_id ID used forfinding clusters of cellsValueNone,outputs ATAC peak countfile in the output directoryExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat )) export_ATAC_scANANSE(sce_small,min_cells=2,output_dir=tempdir())export_CPM_scANANSE export_CPM_scANANSEDescriptionThis functions exports CPM values from a seurat object6Factor_Motif_PlotUsageexport_CPM_scANANSE(seurat_object,output_dir,min_cells=50,RNA_count_assay="RNA",cluster_id="seurat_clusters")Argumentsseurat_object the seurat object used to export the CPM values fromoutput_dir directory where thefiles are outputtedmin_cells minimum of cells a cluster needs to be exportedRNA_count_assayassay of the seurat object containing the RNA count datacluster_id ID used forfinding clusters of cellsValueNone,outputs CPM and countsfiles in the output directoryExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat )) export_CPM_scANANSE(sce_small,min_cells=2,output_dir=tempdir())Factor_Motif_Plot Factor_Motif_PlotDescriptionplot both expression of a TF,and the motif accessibility of the associated motif.Finally,fetch the motif logo from the Maelstrom directory.UsageFactor_Motif_Plot(seurat_object,TF_list,assay_RNA="RNA",assay_maelstrom="MotifTFanticor",logo_dir="~/maelstrom/logos",col=c("darkred","white","darkgrey"),dim_reduction="umap")import_seurat_maelstrom7Argumentsseurat_object seurat objectTF_list list of TFs to plot the expression and linked motif Z-score forassay_RNA RNA_count_assay assay containing the RNA dataassay_maelstrommaelstrom assay used for zscore vizualization,often either TFcor or TFanticor logo_dir directory containing motif logos generated by gimme maelstromcol colours used for zscore vizualizationdim_reduction dimensionality reduction method to useValuepatchwork plot containing a expression dimreduction plot,a maelstrom motif score dimreduction plot,and a png image of the motifExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat )) logos_dir_path<-system.file("extdata","maelstrom","logos",package= AnanseSeurat ) sce_small<-Factor_Motif_Plot(sce_small,c( gene1 , gene2 ),dim_reduction= pca ,logo_dir=logos_dir_path)import_seurat_maelstromimport_seurat_MaelstromDescriptionload Maelstrom enriched motifsUsageimport_seurat_maelstrom(seurat_object,cluster_id="seurat_clusters",maelstrom_file="~/final.out.txt",return_df=FALSE)Argumentsseurat_object objectcluster_id ID used forfinding clusters of cellsmaelstrom_file maelstromfinal.out.txtfilereturn_df return both the seurat object and a dataframe with maelstrom scores as a list8import_seurat_scANANSEValueseurat object with the maelstrom motif scores addes as an assayExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat ))maelstromfile_path<-system.file("extdata","maelstrom","final.out.txt",package= AnanseSeurat ) sce_small<-import_seurat_maelstrom(sce_small,maelstrom_file=maelstromfile_path)import_seurat_scANANSEimport_seurat_scANANSEDescriptionimport the influences from a anansnake directory into a seurat objectUsageimport_seurat_scANANSE(seurat_object,cluster_id="seurat_clusters",anansnake_inf_dir="None",return_df=FALSE)Argumentsseurat_object seurat objectcluster_id ID used forfinding clusters of cellsanansnake_inf_dirinfluence directory generated by anansnakereturn_df return both the seurat object and a dataframe with influence scores as a listValueseurat object with the influence scores addes as an assayExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat ))infdir<-system.file("extdata","influence",package= AnanseSeurat )sce_small<-import_seurat_scANANSE(sce_small,anansnake_inf_dir=infdir)Maelstrom_Motif2TF9 Maelstrom_Motif2TF Maelstrom_Motif2TFDescriptioncreate motif-factor links&export tables for printing motif score alongside its binding factorUsageMaelstrom_Motif2TF(seurat_object,mot_mat=NULL,m2f_df=NULL,cluster_id="seurat_clusters",maelstrom_dir="./maelstrom/",combine_motifs="means",RNA_expression_assay="RNA",RNA_expression_slot="data",expr_tresh=10,cor_tresh=0.3,curated_motifs=FALSE,cor_method="pearson",return_df=FALSE)Argumentsseurat_object objectmot_mat motif_matrix,if not provided extracts one from the single cell object from the maelstrom assaym2f_df motif to factor dataframe,if not provided extracts from the maelstrom directory cluster_id ID used forfinding clusters of cellsmaelstrom_dir directory where the GimmeMotifs m2f table is storedcombine_motifs means(take mean multiple motifscores),max_var(take motif with highest vari-ance),or max_cor(take motif with best correlation to gene expression) RNA_expression_assaySeurat assay containing factor expression infoRNA_expression_slotslot within assay used for calculating mean factor expression per cluster expr_tresh minimum sum of gene counts over all cells in RNA_expression_assay tofilter genes bycor_tresh minimum value of tofilter the cor()output bycurated_motifs use only curated motifs(T),or all motifs in the database(F)cor_method specify one of the cor()methodsreturn_df return both the seurat object and two dataframes with maelstrom scores and expression values as a list10per_cluster_dfValueseurat object with two assays added,MotifTFcor for TFs with positive correlation to the linked motif,and MotifTFanticor for TFs with positive correlation to the linked motifExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat )) maelstrom_dir_path<-system.file("extdata","maelstrom",package= AnanseSeurat )sce_small<-Maelstrom_Motif2TF(sce_small,maelstrom_dir=maelstrom_dir_path)per_cluster_df per_cluster_dfDescriptiongenerate a table of the assay score averages per cluster identifier cellUsageper_cluster_df(seurat_object,assay="influence",cluster_id="seurat_clusters")Argumentsseurat_object seurat objectassay assay containing influence or motif scores generated from cluster pseudobulk cluster_id ID used forfinding clusters of cellsValuedataframe with assay scores,concatinating cells from each per clusterExamplessce_small<-readRDS(system.file("extdata","sce_small.Rds",package= AnanseSeurat )) df<-per_cluster_df(sce_small)Indexconfig_scANANSE,2DEGS_scANANSE,3export_ATAC_maelstrom,4export_ATAC_scANANSE,5export_CPM_scANANSE,5Factor_Motif_Plot,6import_seurat_maelstrom,7import_seurat_scANANSE,8Maelstrom_Motif2TF,9per_cluster_df,1011。

伽马谱软件包安全操作及保养规程伽马谱软件包是一款针对同位素测量和谱分析的软件包。

它可以对各种曲线进行拟合，计算样品活度、计算热力学参数等。

作为一款专业的软件工具，正确的操作和保养非常重要，不仅能够使软件的工作效率得到提高，而且还能延长软件的使用寿命。

本文主要介绍伽马谱软件包的安全操作规程及保养措施。

安全操作规程1. 入门教程操作伽马谱软件包之前，请先阅读软件的入门教程或使用说明书。

通过对软件的了解，可以避免一些常见的误操作或不必要的错误。

2. 启动和关闭软件启动伽马谱软件包时，请确定计算机中不存在病毒等安全隐患。

同时，关闭软件时请勿用强制关机或直接切断电源，以免影响已保存的数据和计算结果。

3. 软件操作在进行软件操作时，务必仔细的阅读软件提供的帮助文档或使用手册。

对于一些操作不熟悉的问题，可以先进行小规模的试验以了解运行情况。

另外，在进行批量处理时，应注意分批次操作，避免一次处理过多数据。

4. 数据备份所有的计算数据、结果和黑白名单等重要信息，应进行及时有效的备份。

备份时请确保备份介质的可靠性和安全性，如使用外置硬盘或网盘。

5. 安全升级在进行伽马谱软件包的升级时，务必使用官方手段进行升级，如使用官网下载链接。

同时，在升级前，必须备份好重要数据和结果。

保养措施正确的保养措施有助于软件的长期使用和不断的提升工作效率。

1. 硬件保护保护好计算机硬件环境是非常重要的。

如加装风扇，保证计算机有足够的散热条件。

同时，避免操作过程中的电源故障、网络故障等，可以避免数据的损失。

这些措施能够为软件的稳定运行提供更可靠的保障。

2. 软件更新及时将伽马谱软件包的更新包安装到电脑中，对于保持软件的更新和稳定也能够提供帮助。

3. 磁盘清理随着软件操作的频繁和数据处理的增多，磁盘会出现杂乱无章的碎片，会影响软件的运行速度。

因此，经常进行磁盘清理操作能放缓这些碎片的积累。

4. 定期检查定期检查伽马谱软件包的文件系统，确保软件的正常运行。

Tosmana中文使用手册Tosmana（版本1.52）使用手册》1.简介本手册简要介绍了Tosmana软件的使用方法。

虽然本手册描述了QCA的基本功能，即在Tosmana中实现的主要方法，但这并不是对该方法的一般介绍。

如果您想了解该软件的主要功能，请参阅网站上的TOSMANA入门指南。

目前，TOSMANA支持清晰的QCA（csQCA），多值QCA（mvQCA）和模糊集QCA（fsQCA）。

从版本1.5开始，XXX还包含了模糊集QCA。

Tosmana是我工作的副业产品，目前正在进行中。

因此，在发布之前，请检查使用该软件执行的所有计算与其他软件包。

您可以在软件部分（）的Compasss网站上找到可用于QCA分析的软件的概述。

随着版本1.4的发布，XXX的开发在几年后恢复了。

添加了一些功能，但从版本1.3到1.4的主要变化包括清除软件中的遗留问题和一般的代码清理。

版本1.4取代了主窗口并重新设计了数据处理，允许从电子表格直接导入数据。

1.5版向XXX引入了模糊集QCA。

2.使用TosmanaTosmana是一个ZIP文件，只需解压缩zip文件并运行名为tosmana.exe的文件即可。

请仅从网站（）下载该文件并检查校验和以避免下载错误。

3.构建数据集软件启动后，将显示主窗口，其中创建，加载和保存数据文件，以及调用计算窗口的位置。

加载和导入数据将文件加载到Tosmana是通过从主窗口的菜单栏中选择文件>打开来实现的。

只有使用Tosmana创建并以XML（扩展标记语言）文件格式保存的文件才能加载Open项。

如果您在早期版本的软件中保存了扩展名为tosmana的文件，则仍可以使用当前软件版本打开它们。

在Tosmana中包含数据的最简单方法是直接从电子表格程序中复制数据。

通过选择“文件”>“从剪贴板导入”，可以直接从电子表格程序粘贴数据，而无需将数据保存到文件中。

在Tosmana中，必须始终包含一个变量，该变量可用作数据集的Case-ID。

EXAMPLES使用简介此部分提供四个例题。

第一个例题是比较简单的题目，举例说明了如何从快速模面设计到零件成形性的分析。

第二个例题是使用Autostamp功能对简单的冲压过程进行模拟。

第三个例题是带领用户如何建立一个适合自己使用的macro-command。

第四个例题为较复杂的冲压过程模拟。

希望通过这四个练习能让用户对冲压模拟方法有初步的了解。

Example ConventionsÖ下文将用此Menu/Submenu形式表示选择子菜单的方法。

Ö在所有的练习当中，对话框名字，控制标签和按钮均有黑体表示。

Ö在所有的练习当中，参数名称将用斜体表示。

Ö在所有的练习当中，文件名和路径将用普通字体表示。

Ö所有练习例题均放置在PAM-STAMP 2G安装路径下的Example目录。

EXAMPLE 1: 快速模面设计和零件成形性分析此研讨部分所使用的是来自雷诺汽车公司（RENAULT）的 Kangoo 汽车翼子板。

此例题的目是为了通过对PAM-DIEMAKERH和PAM-QUIKSTAMP模块的使用，使用户能初步了解如何从零件的CAD数据文件进行快速模面设计到成形性的快速分析。

ÖCreation of the Die Mesh – Tool Design: 使用内置的 PAM-DIEMAKER 模块进行快速模面设计。

ÖSet-up (QuikStamp): 使用内置的PAM-QUIKSTAMP模块建立并进行成形性快速分析。

ÖResults: 后处理与结果分析。

几何数据文件保存在 ../Examples/Fender/data 目录下。

启动PAM-STAMP 2GÖ通过点击PAM-STAMP 2G专用快捷图标启动PAM-STAMP 2G系统。

Ö建立新的project：点击标准工具栏中的或在菜单中选择Project/New/Project 。