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Dear Wiza[R]ds, I have the following data in a data.frame. I need to do an anova with multiple comparison but I don't know how to factor the groups for analysis. There are 3 groups, 1,2 and 3 labelled in column 1. Help appreciated with thanks in advance. Group SI Sif SG Io I2 lol2 1 9.08e-05 9.08e+00 0.060842287 1.798556446 32.574500

Hi, I am trying to aggregate some data and I am confused by the results. I load a data frame "all" from a csv file, and then I do: (FOO,BAR,X,Y come from the header line in the csv file, BTW, how do I rename a column?) byFOO <- aggregate(list(all$BAR,all$QUUX,all$X/all$Y), by = list(FOO=all$FOO), FUN = mean); I expect a data frame with 4

Using the small reproducible example below, I'd like to know if one can somehow use the matrix "sig" (defined below) to add a black outline (with lwd=2) to all pixels with a corresponding value of 1 in the matrix 'sig'? So for example, in the ggplot2 plot below, the pixel located at [1,3] would be outlined by a black square since the value at sig[1,3] == 1. This is my first

Hi Eric, you can use an annotate-layer, eg ind<-which(sig>0,arr.ind = T) ggplot(m1.melted, aes(x = Month, y = Site, fill = Concentration), autoscale = FALSE, zmin = -1 * zmax1, zmax = zmax1) + geom_tile() + coord_equal() + scale_fill_gradient2(low = "darkred", mid = "white", high = "darkblue",

Windows 2000 . 5.00.2195 with Service Pack 1. R 1.5.1 Output from my split-split plot aov "alerted" me that I have done something wrong. I designed an experiment with all combinations of all levels of each treatment, but lost a little data (3 out of 192 plots). With the following data, I run the following model: > collim[c(1:6,187:192),c(1,3:6,9)] plot Litter Fert

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable