similar to: R and S-Plus got the different results of principal component analysis from SAS, why?

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

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

Hi, i''m sort of testing a configuration and things are not working sa i planned. i have the following network diagram: PC1 to 7 cnneced on the same ethernet hub. PC1 PC2 PC 3 PC4 PC5 PC6 on network 192.168.5.0 PC6 and PC7 on network 192.168.1.0 so PC6 work as a router. in addition, PC6 is connected to both network on the same device eth0. now on PC6, put a tbf on dev eth0 root

Following from the R-help thread of March 22 on "Memory usage in prcomp", I've started looking into adding an optional 'rank.' argument to prcomp allowing to more efficiently get only a few PCs instead of the full p PCs, say when p = 1000 and you know you only want 5 PCs. (https://stat.ethz.ch/pipermail/r-help/2016-March/437228.html As it was mentioned, we already

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" >

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:

I agree with Kasper, this is a 'big' issue. Does your method of taking only n PCs reduce the load on memory? The new addition to the summary looks like a good idea, but Proportion of Variance as you describe it may be confusing to new users. Am I correct in saying Proportion of variance describes the amount of variance with respect to the number of components the user chooses to show? So

I am getting the following erro rmessage in ordistep. I have a number of similarly structured datasets using ordistep in a loop, and the message only occurs for some of the datasets. I cannot include a reproducible sample - the specific datasets where this is occur ing are fairly large and there are several pcnm's in the rhs of the formula. thanks for any pointers that may allow me to

> On 25 Mar 2016, at 10:41 am, peter dalgaard <pdalgd at gmail.com> wrote: > > As I see it, the display showing the first p << n PCs adding up to 100% of the variance is plainly wrong. > > I suspect it comes about via a mental short-circuit: If we try to control p using a tolerance, then that amounts to saying that the remaining PCs are effectively zero-variance, but

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

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

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

Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD

Hi All: I am running prcomp on a very large array, roughly [500000, 3650]. The array itself is 16GB. I am running on a Unix machine and am running ?top? at the same time and am quite surprised to see that the application memory usage is 76GB. I have the ?tol? set very high (.8) so that it should only pull out a few components. I am surprised at this memory usage because prcomp uses the SVD

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:

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

x86 pm : provide CC7/PC2 residency Sandy bridge introduces new MSR to get cc7/pc2 residency (core C-state 7/package C-state 2). Print the cc7/pc2 residency when on sandy bridge platform. Signed-off-by: Yang Zhang <yang.z.zhang@intel.com> diff -r 662dbf6ee71c tools/libxc/xc_pm.c --- a/tools/libxc/xc_pm.c Mon Oct 24 18:01:07 2011 +0100 +++ b/tools/libxc/xc_pm.c Fri Oct 28

Hello I am trying to install the R RPM for Suse 10.0 on an x86_64 PC. However I am failing a dependency for "libpng12.so.0" straight away PC5-140:/home/rmgzshd # rpm -i R-base-2.5.0-2.1.x86_64.rpm error: Failed dependencies: libpng12.so.0(PNG12_0)(64bit) is needed by R-base-2.5.0-2.1.x86_64 I do seem to have this file PC5-140:/home/rmgzshd # whereis libpng12.so.0