similar to: Fwd: helps in data analysis

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

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 R-list, I have a data set with plot locations and observations and want to label them based on locations. For example, I have GPS information (x and y) as follows: > x [1] -87.85092 -87.85092 -87.85092 -87.85093 -87.85093 -87.85093 -87.85094 [8] -87.85094 -87.85094 -87.85096 -87.85095 -87.85095 -87.85095 -87.85096 [15] -87.85096 -87.85096 -87.85096 -87.85088 -87.85088 -87.85087

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

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

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

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

I am using Samba v 1.9.18p10 on a RedHat v5.2 (Boxset - Server Install) I have set up to be a server on our small network (5 machines running win9x) Everything seems to be ok except i can only browse the network from one machine (pc 5) On all the other machines when i try to map the users drive (in /home/pc*) or another win9x machine on the network i am prompted for a password but no password is

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

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" >

Deare R users I am new to R. What I want to do is explained below;- I have table called States.Prob which is given below Prob of States Changes State1 State2 State3 State4 A Pa1 Pa2 Pa3 Pa4 B Pb1 Pb2 Pb3 Pb4 C Pc1 Pc2 Pc3 Pc4 D Pd1 Pd2 Pd3 Pd4 and I have a dataframe called

Might be of interest for some of you, especially: o HTB - detailed look into new QoS shaper - Martin Devera o Linux Packet Classification Performance - Jamal Hadi Salim o Status of IPv6 Implementations - Peter Bieringer o High Availability using Keepalived - Alexandre Cassen o Application Layer Fingerprinting - Hendrik Scholz

Hello, I'm willing to donate hardware, see the IRC log below for a few details. If you are interested, reply to "christoph at alte-pflasterei.de", I'll send an Open Document Spreadsheet then with the complete card list containing all details I know. -8<---- IRC chat log --------------------------------- (2019-09-04 16:50:54) chf: Hi, is any of the developers interested in

Hello list! I have a problem with the function 'persp'. I have a data set with the variables X -> The X coordenate of the insect Y -> The Y coordenate of the insect deep-> how deep the insect buried into the soil. I would like to have a plot with a surface simulating the way the insect buried. Is it possible? If I type persp(x,y,deep) R gives me this error message Error in

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

My mistake sir. I was literally engrossed in my stupid logic, and while doing so, overlooked the simple and very effective solution you had offered. Sorry once again sir and will certainly try to be very careful in future. Thanks again and have a great weekend sir. Regards Amelia --- On Fri, 7/1/11, Petr PIKAL <petr.pikal@precheza.cz> wrote: From: Petr PIKAL

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: