anova

I designed 3000 experiments, so that in one experiment there are 4 groups (treatment), in each group there are 50 individuals (subjects). For each experiment I do a standard one way ANOVA and proof if their p.values has a uni probability function under the null-hypothesis, but ks.test rejects this assumption and I cant see why? subject<-50 treatment<-4 experiment<-list() R<-3000 seed<-split(1:(R*subject),1:R) for(i in 1:R){ e<-c() for(j in 1:subject){ set.seed(seed[[i]][j]) e<-c(e,rmvnorm(mean=rep(0,treatment),sigma=diag(3,4),n=1,method="chol")) } experiment<-c(experiment,list(matrix(e,subject

Using a small R sample dataset and the ANOVA example from statsmodels , the degrees of freedom for one of the variables are reported differently, & the F-values results are also slightly different. Perhaps they have slightly different default approaches? Can I set up statsmodels to use R's defaults? import pandas as pd import statsmodels.api as sm from statsmodels.formula.api import ols ##R code on R sample dataset #> anova(with(ChickWeight, lm(weight ~ Time + Diet))) #Analysis of Variance Table # #Response: weight # Df Sum Sq Mean Sq F value Pr(>F) #Time 1 2042344 2042344 1576.460 < 2.2e-16 *

I have a data set that begins like this: > d.weight R N P C D.weight 1 1 0 0 GO 45.3 2 2 0 0 GO 34.0 3 3 0 0 GO 19.1 4 4 0 0 GO 26.6 5 5 0 0 GO 23.5 6 1 45 0 GO 22.1 7 2 45 0 GO 15.5 8 3 45 0 GO 23.4 9 4 45 0 GO 15.8 10 5 45 0 GO 42.9 ... and so on. R is replicate and there are 5 of them (1-5). N is nitrogen level, and there are 5 as well (0, 45, 90, 180, 360). P is phosphorus level, and there are 5 as well (0, 35, 70, 140, 280). C is plant combination, and there are 4 (GO, GB, LO, LB). D.weight is dry weight in grams. However, when I do an ANOVA I get the wrong degrees of freedom. I usually

If I have some data and do an ANOVA and post-hoc tests, how do I make a boxplot that adds the post-hoc classification automatically, rather than having to edit the figure outside of R? For example, here are some data to get started: install.packages("reshape", dependencies=T) library(reshape) x <- rnorm(30) y <- rnorm(30)+1 z <- rnorm(30)+0.5 data.1 <- data.frame(x, y, z) data.2 <- melt(data.1) Here's code for running a simple one way ANOVA and all of the unplanned comparision post-hoc tests: linear.model <- lm(value~variable, data=data.2) anova(linear.model) # Analysis of Variance Table #

I have a problem. I want to run the scipy.stats f_oneway() ANOVA in a script that loads a data-archive containing groups with numpy arrays like so: archive{'group1': array([ 1, 2, 3, ..., ]), 'group2': array([ 9, 8, 7, ..., ]), ...} Now my problem is that the number of groups is not fixed for different data-archives. In other words, I don't know beforehand, how many groups there are in an archive (and also not necessarily what their names are). The scipy implementation of a oneway ANOVA only accepts comma delimited arrays as input like so: a = array([ 1, 2, 3, ..., ]) b = array([ 9, 8, 7, ...,