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Hi R-community, I am doing a PCA and I need plots for different combinations of axes (e.g., PC1 vs PC3, and PC2 vs PC3) with the arrows indicating the loadings of each variables. What I need is exactly what I get using biplot (pca.object) but for other axes. I have plotted PC2 and 3 using the scores of the cases, but I don't get the arrows proportional to the loadings of each variables on

I work on Windows, R version 2.4.1 I need to leave out many rows and columns when reading a matrix. The 'skip' commands in read.table, seem to be just for skipping rows, if I understand well, but I need to skip many columns as well. I tried something very simple and left one row and one column out, but I don't know how to do it for many rows and columns at a time. Example: zzz #

Hi all, Can anyone please tell me how to start R from Tinn-R's "Toogle start/close Rgui" button, after I've updated to a new version of R? It seems like Tinn-R keeps looking for the previous version of R. I have updated R twice already since I started using Tinn-R and I haven't been able to make that button work again. It tells me "The specified file was not

Hi all, I am trying to plot the results of a discriminant analysis done with lda(MASS) but my groups appear in two different plots (in the same graphics device) and I want to combine them in one plot. My code looks like: BirdTrain.lda <- lda(Bdisperser~., data=BirdTrain.mx) predict(BirdTrain.lda) plot(BirdTrain.lda) I have two types of Bdisperser, so I only get one linear discriminant

Hi, I am trying to do a PCA on my data but I keep getting the error message svd(x, nu=0) infinite or missing values >From the messages posted on the subject, I understand that the NAs in my data might be the problem, but I thought na.omit would take care of that. Less than 5% of my cells are missing data. However, the NAs are not regularly distributed across my matrix: certain cases and

I am trying to get a random matrix based on an original matrix called disperser.mx, with dimensions 30x73 When I write the following code: >scramble = sample (disperser.mx) >newmat = matrix(scramble, nrow=30) I get the following warning message and a very weird matrix with 30 rows but only 3 columns shown below: Warning message: data length [73] is not a sub-multiple or multiple of the

I use Windows, R version 2.4.1. I have a dataset in which columns 1-3 are replicates, 4-6, are replicates, etc. I need to calculate an average for every set of replicates (columns 1-3, 4-6, 7-9, etc.) AND each set of replicates should be averaged every 14 rows (for more detail, to measure fruit color using a spectrometer, I recorded three readings per fruit -replicates- that I need to average to

I work with Windows, R version 2.4.1 I'm trying to do a discriminant analysis and, in trying to figure out how to do it following the example from R help, I'm getting an error that says 'subscript out of bounds'. I don't know what this means and how to solve it (I'm very new with R) I'm doing everything in this made-up test matrix: group var1 var2 var3 1 1

I work with Windows and use R version 2.4.1. I am JUST starting to learn this program... I get this warning message 'NAs introduced by coercion' while trying to build a distance matrix (to be analyzed with NMDS later) from a 336 x 100 data matrix. The original matrix has lots of zeros and no missing values, but I don't think this should matter. I searched this forum and people have

I work on Windows, R version 2.4.1. I'm very new with R! I am trying to build a classification tree using rpart but, although the matrix has 108 variables, the program builds a tree with only one split using one variable! I know it is probable that only one variable is informative, but I think it's unlikely. I was wondering if someone can help me identify if I'm doing something

I use Windows, R version 2.5.1 When I try to run stepclass (klaR) I get an error message/warning saying: 1: error(s) in modeling/prediction step in: cv.rate(vars = c(model, tryvar), data = data, grouping = grouping, ... Actually, I look 16 warnings of this type. Can anyone tell me what this means? Also, it returns only 2 out of the 79 variables as important, however these variables

Dear all, I did a Kruskall-Wallis test for a comparison of a variable of interest between 10 sites and I get a significant result (p=0.0019). however, when I perform a post-hoc test using kruskalmc from the pgirmess package, I get no difference between any of the paired comparisons. I cannot find anything in the internet, not in my stats books about how to explain this contradictory result. I

I work with Windows, R version 2.4.1 I am trying to plot the results of a discriminant analysis done using the lda function in the MASS library. The discriminant. analysis goes like this: data.tb<-read.table('C:\\Documents and Settings\\silvia\\Desktop\\dicrim_test.txt', header=T) ## the actual made-up test matrix is pasted below train<-sample (1:36, 15) table

I use Widows, R version 2.4.1 I have 4 questions on lda (MASS) (code is pasted below): 1st. How can I obtain the statistics and p-value associated with discriminant analysis? Am I supposed to calculate that manually by squaring the svd value and looking the p value up in a table? I am writing the following code: training.mx<-read.table('C:\\Documents and Settings\\silvia\\My

I work with Windows, R 2.4.1. I'm a beginner with R! After doing a Discriminant Function Analysis, I am trying to run manova to get a measure of significance of my lda results. I want to predict groups 1 through 4 using 78 variables (bad group/var ratio, I know, but I'm just exploring the possibilities right now). I've tried with a test matrix and I get my results fine, so I think

Hi R community, I am trying to obtain a sample from a matrix but sample(my.matrix) doesn't do what I need. I have a matrix of 1287 interactions between the species in columns and the species in rows and I want to obtain a smaller matrix with say, 800 interactions, that may or may not have the same number of columns and/or rows (i.e., some interactions may not be retrieved in a smaller sample).