similar to: sapply puzzlement

Hello all! I have a problem with ggplot2 library. I want to do an heat map and the y variables are the year months. If I use the following code, he y values are in alphabetical order, but I want it in month order. The code is: library(reshape) library(ggplot2) library(scales) p <- ggplot(data.m, aes(variable, Month)) + geom_tile(aes(fill = value),

below is my data frame. I would like to compute summary statistics for mgl for each river mile (mean, median, mode). My apologies in advance- I would like to get something like the SAS print out of PROC Univariate. I have performed an ANOVA and a tukey LSD and I would just like the summary statistics. thanks stephen RM mgl 1 215 0.9285714 2 215 0.7352941 3 215 1.6455696 4 215

Hello, I have a matrix (class matrix) composed of GridCell (row and column). The matrix value is the beta diversity index value between two grids. Now I would like to get the average value of each GridCell. Please kindly advise how to make the calculation. Thank you. Elaine The matrix looks like (cited from Michael Friendly) I would like to get the average value of each color. Obs

Hi, Suppose i have generated an object using the following : fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) And when i print fit, i get the following : n= 81 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 81 17 absent (0.7901235 0.2098765) 2) Start>=8.5 62 6 absent (0.9032258 0.0967742) 4) Start>=14.5 29 0 absent (1.0000000

Hi all, I am a bit mystified by this error message that I get when I try to apply lmer() to a simple dataset with one between factor (age) and one within factor (item): "$ operator is invalid for atomic vectors" I'll just provide the code, because I don't see where the problem is: library(lme4) options(contrasts=c("contr.helmert","contr.poly")) data =

Hi, I am trying to do a lasso regression using the lars package with the following data (see attached): FastestTime WinPercentage PlacePercentage ShowPercentage BreakAverage FinishAverage Time7Average Time3Average Finish 116.90 0.14 0.14 0.29 4.43 3.29 117.56 117.77 5.00 116.23 0.29 0.43 0.14 6.14 2.14 116.84 116.80 2.00 116.41 0.00 0.14 0.29 5.71 3.71 117.24

I would like to perform partial mantel tests on only within group values, with "between group" values assigned to NA. This is possible in package ncf partial.mantel.test, however this sues a different permutation to that used in ecodist.ecodist will not accept data with NA values, returning a "matrix is not square error. is it possible to perform this test in ecodist? many thanks

Hi, When I look at the summary of an rpart object run on my data, I get 7 nodes but when I plot the rpart object, I get only 3 nodes. Should the number of nodes not match in the results of the 2 functions (summary and plot) or it is not always the same? Look forward to your reply, Carol -------------------------------------------- ?summary(rpart.res) Call: rpart(formula = mydata$class ~ ., data

Hi I have the problem with fitting curve to data with lm and glm. When I use polynominal dependiency, fitted values from model are OK, but I cannot recive proper values when I use coefficents to caltulate this. Let me present simple example: I have simple data.frame: (dd) a: 1 2 3 4 5 6 b: 3 5 6 7 9 10 I try to fit it to model: model=glm(b~poly(a,3),data=dd) I have following data

Dear R experts I have the follwoing data V1 V2 1 -5.8000000 0 2 -4.8000000 0 3 -2.8666667 0 4 -0.8666667 0 5 -0.7333333 0 6 -1.6666667 0 7 -0.1333333 1 8 1.2000000 1 9 1.3333333 1 and I want to know, whether V1 can predict V2: of course it can, since there is a perfect separation between cases 1..6 and 7..9 How can I test, whether this conclusion (being able to assign an

I was noticing mainly sign differences amoung the solutions to QR decomposition. For example R: > x <- matrix(c(12,-51,4,6,167,-68,-4,24,-41),nrow=3,byrow=T) > x [,1] [,2] [,3] [1,] 12 -51 4 [2,] 6 167 -68 [3,] -4 24 -41 > r <- qr(x) > r$qr [,1] [,2] [,3] [1,] -14.0000000 -21.0000000 14 [2,] 0.4285714 -175.0000000 70 [3,]

(I'm sending to r-bugs because rpart is one of the recommended packages and is always installed. I'm also sending it directly to Dr. Ripley, as the maintainer.) rpart working as a classifier does not work (produces no splits) when the class indicator has no instances of one of the factor levels, as long as the factor level is not the final level. I have at least a partial fix, which I

Hi, xtabs() creates a table of counts. I want a table of proportions -- that is, I want to divide every vector (along a particular dimension) by its sum. The tiny example below does that. The call to xtabs() creates a matrix "A" with dimensions ("x1","x2","y"). I transform "A" using aperm() and aaply() to get the matrix "B". The

I'm using R on Windows v2.0.1 with the nlme package (v3.1-53) and am finding some unexpected discrepancies in the output of intervals.lme and coef.lme. I've included a toy dataset at the end, but briefly, the data are longitudinal data from couples in marital therapy. Each spouse's relationship satisfaction is measured 4 times; I've fit both linear and quadratic models to the

Hello, Sorry. It may be a stupid question. I have two vectors a<-c(9,3,5) b<-c(3,4,1) How can I get the variance-covariance matrix of these two vectors? I tried cov(a,b), I got a number not a matrix. I tried to transpose vector a and b as t(a) and t(b), it still cannot work. Any suggestions? Thank a lot! -- View this message in context:

Dear all, I am a R beginner, and I am looking for a way to do the same thing for all levels of a column in a table. Basically, I have a bunch of protein sequences composed of different amino acid residues, and each residue is represented by an uppercase letter. I want to calculate the ratio of different amino acid residues at each position of the proteins. Here is an example table: Proteins

Dear all, I have come across a very confusing matter regarding dist() supplied by the amap package: --- m is just a test matrix > library(amap) Loading required package: mva Warning message: package 'mva' has been merged into 'stats' > m a b c aa 0.1 0.2 0.3 bb 2.0 3.0 4.0 cc 2.0 4.0 6.0 dd 0.3 0.2 0.1 > ds<-dist(m,method="pearson") >

Hi -I am using R v 2.13.0. I am trying to use the aggregate function to calculate the percent at length for each Trip_id and CommonName. Here is a small subset of the data. Trip_id Vessel CommonName Length Count 1 230 Sunlight Shad,American 19 1 2 230 Sunlight Shad,American 20 1 3 230 Sunlight Shad,American 21

useR's, I have several matrices of size 4x4 that I want to calculate means of their respective positions with. For example, consider I have 3 matrices given by the code: mat1 <- matrix(sample(1:20,16,replace=T),4,4) mat2 <- matrix(sample(-5:15,16,replace=T),4,4) mat3 <- matrix(sample(5:25,16,replace=T),4,4) The result I want is one matrix of size 4x4 in which position [1,1] is the

See ?rpart.control. I get: > golf.rp = rpart(Outlook ~ ., golf, control=rpart.control(minsplit=1)) > golf.rp n= 14 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 14 9 rain (0.2857143 0.3571429 0.3571429) 2) Temperature< 71.5 6 2 rain (0.1666667 0.6666667 0.1666667) 4) Temperature< 64.5 1 0 overcast (1.0000000 0.0000000 0.0000000) * 5)