similar to: prcomp : plotting only explanatory axis arrows

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) # =^=

Dear All, when I'm running a PCA with prcomp(USArrests, scale = TRUE) I get the right principal components, but with the wrong sign infront Rotation: PC1 PC2 PC3 PC4 Murder 0.5358995 -0.4181809 0.3412327 0.64922780 Assault 0.5831836 -0.1879856 0.2681484 -0.74340748 UrbanPop 0.2781909 0.8728062 0.3780158 0.13387773 Rape 0.5434321 0.1673186 -0.8177779 0.08902432 instead of PC1 PC2 PC3 PC4

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

Dear R-developers, I finally got around to install R 1.1.0 but had problems at the `make check' stage. After compiling the released R 1.1.0 version the `make check' stage stopped while checking the examples in base. There was some problem with the quantile function and the check stopped complaining that NA's are not allowed. But I assume that this problem is already known because

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 = "")

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

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,

Hi guys, I am new to R, so sorry if my problem seems trivial. Sometimes I encounter some lists, which I cannot index their components with [ . ] For instance the prcomp() function returns a 'prcomp' object whose components are some 'lists'. the second component is a list that comtains the following: > mylist <- churn[2] > class(mylist) [1] "list" >

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: >

Greetings- I have a strange problem displaying a graph from R (1.3.1, linux) in a LaTeX document of documentclass seminar. I'm using graphicx to include the file: \usepackage{graphicx} ... \resizebox{\textwidth}{\textheight}{\includegraphics{crime.eps}} When I do this, the entire slide (including the page number) is rotated 180 degrees. Any ideas why this happens? The graph was created

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

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

Hello again, I was hoping one of you could help me with this problem. Consider the sample data from R: > summary(prcomp(USArrests)) Importance of components: PC1 PC2 PC3 PC4 Standard deviation 83.732 14.2124 6.4894 2.48279 Proportion of Variance 0.966 0.0278 0.0058 0.00085 Cumulative Proportion 0.966 0.9933 0.9991 1.00000 How do I access the

Hi ! I've used the example given in the documentation for the prcomp function both in R and SPAD to compare the results obtained. Surprisingly, I do not obtain the same results for the coordinates of the principal composantes with these two softwares. using USArrests data I obtain with R : > summary(prcomp(USArrests)) Importance of components: PC1 PC2

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

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

Hello all, I'm currently using R to do PCA Analysis, and was wondering if anyone knew the specific R Code that could limit the output of the PCA Analysis so that you only get the Principal Component features as your output and none of the extraneous words or numbers that you don't want. If that was unclear, let me use linear regression as an example: "lm(y~x)" is the normal

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

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]]