similar to: Princomp(), prcomp() and loadings()

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

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

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) # =^=

I am installing R 2.15.1 onto RHEL 6, using gcc 4.7.0 with Intel MKL 10.3.7 and the following environment: export BLAS_LIBS="-Wl,--start-group /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_gf_lp64.a /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_sequential.a /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_core.a -Wl,--end-group -lpthread" export LAPACK_LIBS="-Wl,--start-group

Hello, For basic linear regression lm() does the job well, for datasets that are larger than memory biglm() seems to work. I'm working on a parallel implementation of multiple linear regression for datasets that are too large for memory. Currently I am working over least squares: calculating: t(X) %*% X and t(X) %*% y separately in parallel on each node This generates a

Hi everyone, I am trying to get the factor score for each individual case from a principal component analysis, as I understand, both princomp() and prcomp() can not produce this factor score, the principal() in psych package has this option: scores=T, but after running the code, I could not figure out how to show the factor score results. Here is my code, could anyone give me some advice please?

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

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)

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

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

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 = "")

hi, I have two questions: #first (SPSS vs. R): I just compared the output of different PCA routines in R (pca, prcomp, princomp) with results from SPSS. the loadings of the variables differ vastly! in SPSS the variables load constantly higher than in R. I made sure that both progr. use the correlation matrix as basis. I found the same problem with rotated values (varimax rotation and rtex=T

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,

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: >

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.

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 again- Thanks to Prof. Ripley for responding to my previous question. I would like to clarify my question using sample code. I will use some sample code taken from ?prcomp Again, I would like to compare the % variance explained by each PC before and after rotation. < code follows > data(USArrests) pca = prcomp(USArrests, scale = TRUE) # proportion variance explained by each

How does one rotate the loadings from a principal component analysis? Help on function prcomp() from package mva mentions rotation: Arguments retx a logical value indicating whether the rotated variables should be returned. Values rotation the matrix of variable loadings (i.e., a matrix whose olumns contain the eigenvectors). The function princomp returns this in the element

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

I have the following problem in getting the "sqlSave" function from the example code in the R package RODBC to work with MySQL as ODBC server: - a new database is created, but no data is written to it - the example code works just fine when I use MS Access as ODBC server. ----------- offending code and output ------------------- > library(RODBC); > channel <-