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Dear Rusers, I have met a difficult problem on explaining the differences of principal component analysis(PCA) between R,S-PLUS and SAS/STATA/SPSS, which wasn't met before. Althought they have got the same eigenvalues, their coeffiecients were different. First, I list my results from R,S-PLUS and SAS/STATA/SPSS, and then show the original dataset, hoping sb. to try and explain it.

Hello I am creating a linear model with the command net5 = lm( X[,1] ~ PrinComp[,1:5]) where my vector PrinComp looks like this > head(PrinComp[,1:5]) PC1 PC2 PC3 PC4 PC5 [1,] 1.8626055 -3.34190998 -0.5448889 2.8296751 0.3994096 [2,] 3.1124144 -1.68113572 1.7187314 -2.0162583 -0.2935675 [3,] 3.3538049 -0.05471002 -2.9385065 0.6921495 -2.2743761 [4,]

Hello, I am using the predict function for VAR in r obtaining the following object for the predictions with the following command PronFac <- predict(VARFactores,n.ahead=1) > PronFactores$fcst $PC1 fcst lower upper CI PC1.fcst 2.284497 -0.8033048 5.3723 3.087802 $PC2 fcst lower upper CI PC2.fcst -0.938333 -4.346927 2.470261 3.408594 $PC3

Greets to the list! I am aware that this topic has been discussed several times. And I've read quite some related posts [1]. Yet, can't seem to give a solution to my problem. I have 6 data frames consisting of 6 rows x 7 columns put together from other data.frames. Something like: a b c d e f g v1 # # # # # # # v2 # # # # # # # v3 # # # # # # # v4 # # # # # # # v5 # # # # # # # v6

Dear R user, I want to do sparse principal component analysis (spca). I am using elastic net package for this and spca() and the code is following from the example. My question is How can I decide the *K =? *and *para=c(7,4,4,1,1,1)) . So, here k=6 i.e the no of Principal Components. and each pcs say , * ** pc1 number of non zero loading is 7 pc2 number of non zero loading

Hi, Suppose I have the following data frame. __BEGIN__ > library(MASS) > data(crabs) > crab.pca <- prcomp(crabs[,4:8],retx=TRUE) > crab.pca$rotation PC1 PC2 PC3 PC4 PC5 FL 0.2889810 0.3232500 -0.5071698 0.7342907 0.1248816 RW 0.1972824 0.8647159 0.4141356 -0.1483092 -0.1408623 CL 0.5993986 -0.1982263 -0.1753299 -0.1435941 -0.7416656 CW

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 list, I am not a frequent user of R. Recently I used R in principal component analysis and got the result as a class, which has information like standard deviation and principal components from 1 to 10. How is it possible to extract the column corresponding to first principal component and write it to a file the out from prcomp command is something like this Standard deviations:

Hi all, I think I may be confused by different people/programs using the word rotation differently. Does prcomp not perform rotations by default? If I understand it correctly retx=TRUE returns ordinated data, that I can plot for individual samples (prcomp()$x: which is the scaled and centered (rotated?) data multiplied by loadings). What does it mean that the data is rotated from the

Dear friends, what is this error message in gam???? I cannot understand what it means .... is it a bug? gam_bray_scot24_pc_0505<gam(bray~s(PC1,PC2,PC3,PC4,PC5, PC1.1,PC2.1,PC3.1,PC4.1,PC5.1),data=dist_scot24_vector_with_climate) Error in if (length(data) != vl) { : missing value where TRUE/FALSE needed Calls: gam ... smooth.construct -> smooth.construct.tp.smooth.spec -> array In

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

Hey, I need some help. I want to obtain a cross validation for a regression model (binary response) but I got an error with CVbinary. Well I did this: fit <- lm(resp ~ PC1 + PC2 + PC3 + PC4 + PC5 + PC6 + PC7 + PC8 + PC9+PC10+PC11+PC12+PC13+PC14+PC15+PC16+PC17+PC18+PC19+PC20+PC21+PC22+PC23+PC24+PC25+PC26+PC27+PC28, data = dexp.cp, family=binomial()) CVbinary(fit) Error in sample(nfolds, m,

Hi, I saved as a matrix a summary of a PCA analysis and I've used barplot to plot the PCA variances. I would like to print on the same graphic the values of my matrix m1 - in other words the summary of my PCA analysis. I can do it very painstaking with text for each row and make sure that everything aligns and so on but i wonder if there is a better method than that. My summary follows:

Dear List, I posted this in R-mixed and did not receive any feedback. I might post it in the wrong place. I re-post in R-help and hope to receive any suggestions and\or thoughts regarding data analysis. The objective of the study is to investigate effects of soil properties on insect outbreaks. There are four study fields (or sites). Data were collected from 1996 through 2009. Below is

Dear R-list users, I'm new to principal components and factor analysis. I thought this method can be very useful for me to find relationships between several variables (which I know there is, only don't know which variables exactly and what kind of relation), so as a structure detection method. Now, I'm experimenting with the function prcomp from the mva package. In my source code

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, 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 All, I'd like to build a package for the community that replicates the output produced by SAS "proc varclus". According to the SAS documentation, the first few steps are: 1. Find the first two principal components. 2. Perform an orthoblique rotation (quartimax rotation) on eigenvectors. 3. Assign each variable to the rotated component with which it has the higher squared

Martin, I fully agree. This becomes an issue when you have big matrices. (Note that there are awesome methods for actually only computing a small number of PCs (unlike your code which uses svn which gets all of them); these are available in various CRAN packages). Best, Kasper On Thu, Mar 24, 2016 at 1:09 PM, Martin Maechler <maechler at stat.math.ethz.ch > wrote: > Following from

Hi I have a script which is designed to gather data from individual columns from a file, which is an output from an instrument. the file has multiple sections and each a section has data under each column (vars), I am using the name of the column as a variable to gather the column ID using vidx<-grep(vars[vi],gsub("[[:punct:]]","",strrl1[[datbeg-1]]),ignore.case=T) the