similar to: A bug in princomp(), perhaps?

I am trying to run PCA on a matrix (the first column and row are headers). There are several cells with NA's. When I run PCA with the following code: ______________________________________ setwd("I:/PCA") AsianProp<-read.csv("Matrix.csv", sep=",", header=T, row.names=1) attach(AsianProp) AsianProp AsianProp.pca<-princomp(AsianProp, na.omit)

Hi, I run the example for princomp for R211 I got the following error for biplot > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <http://pc.cr> <- princomp(USArrests)) # inappropriate Erreur dans cov.wt(z) : 'x' must contain finite values only > princomp(USArrests, cor = TRUE) # =^=

Full_Name: Jerome Asselin Version: 1.7.1 OS: Red Hat Linux 7.2 Submission from: (NULL) (24.77.125.119) Setting the parameter na.action=na.omit should remove incomplete records in princomp. However this does not seem to work as expected. See example below. Sincerely, Jerome Asselin data(USArrests) princomp(USArrests, cor = TRUE) #THIS WORKS USArrests[1,3] <- NA princomp(USArrests, cor =

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

... I checked and Brian and I are both right (see bottom for prior mail exchange). Let me explain: ============================================================= 1. Indeed, in principle, princomp allows data matrices with are wider than high. Example: > x1 [,1] [,2] [,3] [,4] [1,] 1 1 2 2 [2,] 1 1 2 2 > princomp(x1) Call: princomp(x = x1) Standard deviations:

When I run example(princomp) I get the following error message: prncmp> ## The variances of the variables in the prncmp> ## USArrests data vary by orders of magnitude, so scaling is appropriate prncmp> (pc.cr <- princomp(USArrests)) # inappropriate Error in cov.wt(z) : 'x' must contain finite values only Seth Roberts -- blog.sethroberts.net www.shangriladiet.com

Hi, I am generating large Kac matrices (also known as Clement matrix). This a tridiagonal matrix. I was wondering whether there is a vectorized solution that avoids the `for' loops to the following code: n <- 1000 Kacmat <- matrix(0, n+1, n+1) for (i in 1:n) Kacmat[i, i+1] <- n - i + 1 for (i in 2:(n+1)) Kacmat[i, i-1] <- i-1 The above code is fast, but I am curious about

Hello! I am running principle components analysis using princomp function in pacakge psych. mypc <- princomp(mydataforpc, cor=TRUE) Question: I'd like to use pairwise deletion of missing cases when correlations are calculated. I.e., I'd like to have a correlation between any 2 variables to be based on all cases that have valid values on both variables. What should my na.action be in

Hi, While using R CMD check I get the following Latex error message which occurs when creating PDF version of manual: LaTex error: File `inconsolata.sty' not found I am using Windows 7 (64-bit) and R 3.0.1. I have MikTex 2.9. I see that the incosolata.sty is present under \doc\fonts folder. How can I eliminate this problem? Thanks, Ravi [[alternative HTML version deleted]]

I am attempting to run principal components analysis on a dataset of spectral reflectance (6 decimal places). I imported the data using read.table and there are both column and row headers. When I run princomp I receive the following error: Error in cov.wt(z) : 'x' must contain finite values only Where am I going wrong? Ross

Hello fellow Rers, I have a no-doubt simple question which is turning into a headache so would be grateful for any help. I want to do a principal components analysis directly on a correlation matrix object rather than inputting the raw data (and specifying cor = TRUE or the like). The reason behind this is I need to use polychoric correlation coefficients calculated with John Fox's

More principled would be to use a lasso-type approach, which combines selection and estimation in one fell swoop! Ravi ________________________________ From: Ravi Varadhan Sent: Tuesday, June 6, 2017 10:16 AM To: r-help at r-project.org Subject: Subject: [R] glm and stepAIC selects too many effects If AIC is giving you a model that is too large, then use BIC (log(n) as the penalty for adding

Hi, I am trying to do the following computation: p <- rep(0, n) coef <- runif(K+1) U <- matrix(runif(n*(2*K+1)), n, 2*K+1) for (i in 0:K){ for (j in 0:K){ p <- p + coef[i+1]* coef[j+1] * U[,i+j+1] } } I would appreciate any suggestions on how to perform this computation efficiently without the "for" loops? Thank

Try this: Age_log_model = glm(Arthrose ~ Alter, data=x, start=c(-1, 0), family=quasibinomial(link = log)) Ravi Ravi Varadhan, Ph.D. Assistant Professor The Center on Aging and Health Division of Geriatric Medicine & Gerontology Johns Hopkins University rvaradhan@jhmi.edu<mailto:rvaradhan@jhmi.edu> 410-502-2619 [[alternative HTML version deleted]]

If AIC is giving you a model that is too large, then use BIC (log(n) as the penalty for adding a term in the model). This will yield a more parsimonious model. Now, if you ask me which is the better option, I have to refer you to the huge literature on model selection. Best, Ravi [[alternative HTML version deleted]]

Hi, I think that the documentation for the biplot function `biplot.princomp' is inconsistent with what it actually does. Here is what the documentation states: pc.biplot If true, use what Gabriel (1971) refers to as a "principal component biplot", with lambda = 1 and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner products between

I am curious to know what algorithm R's mean function uses. Is there some reference to the numerical properties of this algorithm? I found the following C code in summary.c:do_summary(): case REALSXP: PROTECT(ans = allocVector(REALSXP, 1)); for (i = 0; i < n; i++) s += REAL(x)[i]; s /= n; if(R_FINITE((double)s)) { for (i = 0; i < n; i++) t += (REAL(x)[i] -

I am moving this to r-devel. The problem and solution below posted on r-help could have been a bit slicker if %*% worked with multidimensional arrays multiplying them so that if the first arg is a multidimensional array it is mulitplied along the last dimension (and first dimension for the second arg). Then one could have written: Tbar <- tarray %*% t(wt) / rep(wti, each = 9) which is a bit

R version: 1.7.1 OS: Red Hat Linux 7.2 When "covmat" is supplied in princomp(), the output value "center" is all NA's, even though the input matrix was indeed centered. I haven't read anything about this in the help file for princomp(). See code below for an example: pc2$center is all NA's. Jerome Asselin x <- rnorm(6) y <- rnorm(6) X <- cbind(x,y)

dear R experts--- I have (many) unidimensional root problems. think loc.of.root <- uniroot( f= function(x,a) log( exp(a) + a) + a, c(.,9e10), a=rnorm(1) ) $root (for some coefficients a, there won't be a solution; for others, it may exceed the domain. implied volatilities in various Black-Scholes formulas and variant formulas are like this, too.) except I don't need 1 root, but a