similar to: About princomp

Dear R users, When I tried to use princomp() from stats packages to do Principal Components Analysis, I am not very clear what is the "scale". And the scores are different from "PROC PRINCOMP" procedure from SAS. Using the example data from this package: restpc <- princomp(USArrests, cor = TRUE) > restpc$scale Murder Assault UrbanPop Rape 4.311735 82.500075

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

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'm attempting to modify how biplot draws its red vectors (among other things). This is how I've started: Biplot <- function(xx, comps = c(1, 2), cex = c(.6, .4)) { ## Purpose: Makes a biplot with princomp() object to not show arrows ## ---------------------------------------------------------------------- ## Arguments: xx is an object made using princomp() ##

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

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I

According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402

Dear all, i wonder if there?s a command to obtain the actual values of a principal component or a factor (not as.factor, but factanal) . test=princomp(USArrests, cor = TRUE) summary(test) just outputs, standard deviation, Prop of Variance and cumulative proportion of variance. test$loadings offers yet another proportion of variance scheme. why is that? Apart from that: Is there a

Hi, I am running the RODBC examples form the help guide. I am trying to UPDATE a table in an Access data base but I am having an error. library(RODBC) library(termstrc) path = getwd() setwd(getwd()) dbName = "data.mdb" pathdbname = paste(path,"/",dbName,sep="") accesChannel = odbcConnectAccess(pathdbname, uid = "", pwd = "")

I'd like to search for a particular string in an SQLite database using RSQLite, but I'm running into problems constructing the query properly, because of embedded quotes and parens in the string. Is there a function that escapes these for me, or some other fixup that would let me do the queries below? In the real situation I don't have control over what strings get searched for.

Dear list, Being new to both the postgres database, ODBC and the RODBC interface, I am somewhat confused by some of the problems I am experiencing trying to connect R to the database. Whai I am trying is basically the example part of the help file for the sqlSave function: > library(RODBC) > odbcConnect("theodor") -> channel > data(USArrests) > sqlSave(channel,

Peter, As I understand your Q. You probably have data that is similar to each other like stock Prices for all RHS variable. In that case the difference between corr and cov is not significant; however, if your RHS contains totally dissimilar variables it matters a great deal. If x1 income, x2 job type, x3 Education level, etc..., then taking cov of these variables would not be desireable

Dear R-helpers, I tried the playwith packages for the first time, and it crashed R: > require(playwith) Loading required package: playwith Loading required package: lattice Loading required package: grid Loading required package: gWidgets Loading required package: gWidgetsRGtk2 Loading required package: RGtk2 Loading required package: cairoDevice > sessionInfo() R version 2.7.1

I am developing an R package which includes datasets. The build and install works correctly. However, when I access the dataset ("BowRiver"), I get: > data(BowRiver) > BowRiver Error: object "BowRiver" not found. However, I can access the dataset from > data Example R datasets (such as USArrests) are loaded and can be accessed by the dataset name: >

Which is the parameter that is used to decrease the size of ylabs plotted in biplot? I tried playing with cex and cex.lab I am not getting it right pc <- princomp(USArrests) biplot(pc, xlabs = rep("", nrow(USArrests)),ylabs=(colnames(USArrests))) Thanks../Murli [[alternative HTML version deleted]]

Dear list, I want to perform a biplot, using customized titels for the x and y axis. Setting xlab="" and ylab="" resulted in an error, e.g.: > data(USArrests) > biplot(princomp(USArrests),xlab="",ylab="") Error in biplot.default(t(t(scores[, choices])/lam), t(t(x$loadings[, : length of dimnames[1] not equal to array extent > How do I

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

Hi everyone. I performing a simple PCA using the princomp function. Then, I use the biplot function to show it. However, the function use line number to represent samples. I would like to know if there's a way to use a dot (point) instead of the line number when using the biplot function. With regards, Phil -- View this message in context:

Dear R-helpers, Using the package Lattice, I performed a PCA. For example pca.summary <- summary(pc.cr <- princomp(USArrests, cor = TRUE)) The Output of "pca.summary" looks as follows: Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.5748783 0.9948694 0.5971291 0.41644938 Proportion of Variance 0.6200604

So as often there is more to it than you first think. Let's consider this an RFC (for experienced long time R users) : >>>>> Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Wed, 9 Aug 2017 10:45:56 +0200 writes: >>>>> William Dunlap via R-devel <r-devel at r-project.org> >>>>> on Tue, 8 Aug 2017 11:59:45