[R] error message in cor.dist
Hello, I am trying to calculate pairwise Pearson correlation distances, and using biodist package, function "cor.dist". I start with a table of 4 rows and about 10 columns. (each of 4 samples, or rows have values in each of the 10 categories, no zeros or NAs). I am getting an error message: > cor.dist(dmatrixD) Error in cor(t(x)) : missing observations in cov/cor In addition: Warning message: In cor(t(x)) : NAs introduced by coercion Does anyone know what that means? Also, are there other functions for Pearson distances? Thanks! -- Tanya. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] candisc() error message
Hi, I am doing canonical discriminant analysis using candisc function from the candisc package. My input is a table of species distribution (columns = abundance of each species in each sample) in samples that are split by categories (rows), and I want to know whether each category is associated with a particular set of species and their abundances. I have 20 rows (samples) split into 6 categories, and 17 columns (species). I am getting the following error message, which I don't understand: > can<-candisc(mod, data=canIN) Error in linear.hypothesis.mlm(mod, hyp.matrix.2, SSPE = SSPE, V = V, : The error SSP matrix is apparently of deficient rank = 16 < 17 Does anyone has any experience in candisc? -- Tanya. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] PCA : Error in eigen(cv,
Hi all, I am doing bootstrap on a distance matrix, in which samples have been drawn with replacement. After that I do PCA on a resulted matrix, and these 2 steps are repeated 1000 times. pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from 1 to 1000 SampleD is a new matrix after resampling; I am getting the following error message, which I don't understand: +pca(x)<-princomp(SampleD[i,j]) + } Error in eigen(cv, symmetric = TRUE) : infinite or missing values in 'x' Should I maybe not use a vector, but matrix instead? Thanks! -- Tanya. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] PCA : Error in eigen(cv,
Ok, the mistake was in the pca(x)<-princomp(SampleD[i,j]), should've used pca(x)<-princomp(SampleD) instead. Now, is there anyway to keep track of the matrix index, so in the end of all PCAs, I can tell which score/loading belongs to which sample? Thanks everyone! On Mon, Jun 30, 2008 at 9:08 PM, Tanya Yatsunenko <[EMAIL PROTECTED]> wrote: > Hi all, > > I am doing bootstrap on a distance matrix, in which samples have been drawn > with replacement. After that I do PCA on a resulted matrix, and these 2 > steps are repeated 1000 times. > > pca(x) is a vector where I wanted to store all 1000 PCAs; and x is from 1 > to 1000 > SampleD is a new matrix after resampling; > > I am getting the following error message, which I don't understand: > > +pca(x)<-princomp(SampleD[i,j]) > + } > Error in eigen(cv, symmetric = TRUE) : infinite or missing values in 'x' > > Should I maybe not use a vector, but matrix instead? > Thanks! > > -- > Tanya. > > -- Tanya [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] coding for categorical variables with unequal observations
Hi, I am doing multiple regression, and have several X variables that are categorical. I read that I can use dummy or contrast codes for that, but are there any special rules when there're unequal #observations in each groups (4 females vs 7 males in a "gender" variable)? Also, can R generate these codes for me? THanks. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] coding for categorical variables with unequal observations
Also, since I just started to use R, I have trouble generating and understanding some of the codes, especially choosing the correct ones. Thanks! tanya On Thu, Apr 3, 2008 at 3:54 PM, Tanya Yatsunenko <[EMAIL PROTECTED]> wrote: > Hi, > I am doing multiple regression, and have several X variables that are > categorical. > I read that I can use dummy or contrast codes for that, but are there any > special rules when there're unequal #observations in each groups (4 females > vs 7 males in a "gender" variable)? > Also, can R generate these codes for me? > THanks. > > -- Tanya [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
