: 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: joachim.audenaert at pcsierteelt.be | W: www.pcsierteelt.be

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