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## : automated levene test and other tests for variable datasets

 Hello all, I am writing a script for statistical comparison of means. I'm doing many field trials with plants, where we have to compare the efficacy of different treatments on, different groups of plants. Therefore I would like to automate this script so it can be used for different datasets of different experiments (which will have different dimensions). An example dataset is given here under, I would like to compare if the data of 5 columns (A,B,C,D,E) are statistically different from each other, where A, B, C, D and A are different treatments of my plants and I have 5 replications for this experiment dataset <- structure(list(A = c(62, 55, 57, 103, 59), B = c(36, 24, 61, 19, 79), C = c(33, 97, 54, 48, 166), D = c(106, 82, 116, 85, 94), E = c(32, 16, 9, 7, 46)), .Names = c("A", "B", "C", "D",    "E"), row.names = c(NA, 5L), class = "data.frame") 1) First I would like to do a levene test to check the equality of variances of my datasets. Currently I do this as follows: library("car") attach(dataset) y <- c(A,B,C,D,E) group <- as.factor(c(rep(1, length(A)), rep(2, length(B)),rep(3, length(C)), rep(4, length(D)),rep(5, length(E)))) leveneTest(y, group) Is there a way to automate this for all types of datasets, so that I can use the same script for a datasets with any number of columns of data to compare? My above script only works for a dataset with 5 columns to compare 2) For my boxplots I use boxplot(dataset) which gives me all the boxplots of each dataset, so this is how I want it 3) To check normality I currently use the kolmogorov smirnov test as follows ks.test(A,pnorm) ks.test(B,pnorm) ks.test(C,pnorm) ks.test(D,pnorm) ks.test(E,pnorm) Is there a way to replace the A, B, C, ... on the five lines into one line of entry so that the kolmogorov smirnov test is done on all columns of my dataset at once? 4) if data is normally distributed and the variances are equal I want to do a t-test and do pairwise comparison, currently like this pairwise.t.test(y,group,p.adjust.method = "none") if data is not normally distributed or variances are unequal I do a pairwise comparison with the wilcoxon test pairwise.wilcox.test(y,group,p.adjust.method = "none") But again I would like to make this easier, is there a way to replace the y and group in my datalineby something so it works for any size of dataset? 5) Once I have my paiwise comparison results I know which groups are statistically different from others, so I can add a and b and c to different groups in my graph. Currently I do this on a sheet of paper by comparing them one by one. Is there also a way to automate this? So R gives me for example something like this A: a B: a C: b D: ab E: c All help and commentys are welcome. I'm quite new to R and not a statistical genious, so if I'm overseeing things or thinking in a wrong way please let me know how I can improve my way of working. In short I would like to build a script that can compare the means of different groups of data and check if they are statistically diiferent Met vriendelijke groeten - With kind regards, Joachim Audenaert onderzoeker gewasbescherming - crop protection researcher PCS | proefcentrum voor sierteelt - ornamental plant research Schaessestraat 18, 9070 Destelbergen, Belgi� T: +32 (0)9 353 94 71 | F: +32 (0)9 353 94 95 E: [hidden email] | W: www.pcsierteelt.be   Heb je je individuele begeleiding bemesting (CVBB) al aangevraagd? | Het PCS op LinkedIn Disclaimer | Please consider the environment before printing. Think green, keep it on the screen!         [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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## Re: : automated levene test and other tests for variable datasets

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## Re: : automated levene test and other tests for variable datasets

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