similar to: Accessing the elements of summary(prcomp(USArrests))

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

Hello all, I was wondering if anyone knew how to get R to only spit out a certain portion of PRcomp data; namely, when I enter the following code, I get: > Summary(prcomp(USArrests)) Importance of components: PC1 PC2 PC3 PC4 Standard Deviation 83.732 14.212 6.489 2.483 Proportion of Variance 0.966 0.0278 0.0058 0.00085 Cumulative Proportion 0.966

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 listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Dear all, I have a very large dataset (1712351 , 20) and would like to plot only the arrows that represent the contribution of each variables. On the sample below I woild like to plot only the explanatory variables (Murder, Assault..) and not the sites. prcomp(USArrests) # inappropriate prcomp(USArrests, scale = TRUE) prcomp(~ Murder + Assault + Rape, data = USArrests, scale = TRUE)

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

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've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

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

Hello folks, it may be regarded as a user error to scale() your data prior to prcomp() instead of using its 'scale.' argument. However, it is a user thing that may happen and sounds a legitimate thing to do, but in that case predict() with 'newdata' can give wrong results: x <- scale(USArrests) sol <- prcomp(x) all.equal(predict(sol), predict(sol, newdata=x)) ## [1]

Hey guys, I am new to R and apologize for the basic question - I do not mean to offend. I have been using R to perform PCA on a set several hundred objects using a set of 30 descriptors. From the results generated by prcomp(), is there a way to print a matrix showing the contributions of the original variables to each PC? My hope is to identify which of the original 30 variables are the most

I've just starting using the prcomp function, and I want to be able to extract individual principal components (e.g. PC1, PC2) in vector format. I haven't been able to find any documentation that explains how to do this (or even if it is possible). Any help on the subject would be greatly appreciated. Many thanks Deirdre Toher Teagasc National Food Centre

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" >

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 = "")

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

Hi all, I'm having a problem once again, trying to do something very simple. Consider the following data frame: x <- read.table(textConnection("locus1 locus2 locus3 A T C A T NA T C C A T G"), header = TRUE) closeAllConnections() I am trying to make a new data frame, replacing "A" with "A/A", "T" with "T/T", "G" with

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'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 all, I have a rather complicated problem. I am trying to loop through making graphs, so that the graph-making process is fully automated. For each graph, I'd like to make sure the corresponding title is formatted properly. The titles will be a combination of a gene name and numerical position within the gene. The gene name should be italic-bold, whereas the gene position should be