similar to: PCA on high dimentional data

Hi R-community, I am doing a PCA and I need plots for different combinations of axes (e.g., PC1 vs PC3, and PC2 vs PC3) with the arrows indicating the loadings of each variables. What I need is exactly what I get using biplot (pca.object) but for other axes. I have plotted PC2 and 3 using the scores of the cases, but I don't get the arrows proportional to the loadings of each variables on

Hi everyone, I am doing PCA with labdsv package. I was trying to create a biplot graphs in order to observe arrows related to my variables. However when I run the script for this graph, the console just keep saying: *Error in nrow(y) : element 1 is empty; the part of the args list of 'dim' being evaluated was: (x)* could please someone tell me what this means? what i am doing

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 everybody,   I am doing principal component analysis (PCA) using "prcomp' function. When i did "Biplot", i did not found interesting result and it is based on Principal component (PC) 1 vs PC2. Now, i want to see"Biplot" in combination of either PC1 vs PC3 or PC2 vs PC 3. I did not get the ideas. Does any one have ideas ? I am optimistic on getting some idea.

Dears, I've been learning biplot (Gabriel, 1971) and I found the function 'biplot', inside of the package 'stats', useful but, a bit limited. So, I'm thinking to start a colaborative package to enhance this methods to other multivariate methods. In this way, I would like to start it, making public a new function (biplot.pca, still in development, but running) that make

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, 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 all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

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 All, The basic premise of what I want to do is the following: I have 20 "entities" for which I have ~500 measurements each. So, I have a matrix of 20 rows by ~500 columns. The 20 entities fall into two classes: "good" and "bad." I eventually would like to derive a model that would then be able to classify new entities as being in "good

Dear Sir/Madam, I am trying to do PCA analysis with "iris" dataset and trying to interpret the result. Dataset contains 150 obs of 5 variables Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa

Hello R-friends, i have a question to the "text"-function. a little test-dataset for better understanding: -the dataset was imported with read.table(....,header=TRUE) s1-s10 are the samplenames var1 var2 var3 s1 1 1 2 s2 2 3 1 s3 2 2 3 s4 5 4 3 s5 4 2 3 s6 6 3 2 s7 8 5 4 s8 7 2 1 s9 9 3 2

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 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:

First, see the example at https://isezen.github.io/PCA/ > On 15 Sep 2017, at 13:43, Shylashree U.R <shylashivashree at gmail.com> wrote: > > Dear Sir/Madam, > > I am trying to do PCA analysis with "iris" dataset and trying to interpret > the result. Dataset contains 150 obs of 5 variables > > Sepal.Length Sepal.Width Petal.Length Petal.Width

Hello all, I wish to compute site scores using PCA (prcomp) on a matrix with missing values, for example: Drain Slope OrgL a 4 1 NA b 2.5 39 6 c 6 8 45 d 3 9 12 e 3 16 4 ... Where a,b... are sites. The command > pca<-prcomp(~ Drain + Slope + OrgL, data = t, center = TRUE, scale = TRUE, na.action=na.exclude) works great, and from

rm(list=ls()) yx.df<-read.csv("c:/MK-2-72.csv",sep=',',header=T,dec='.') dim(yx.df) #get X matrix y<-yx.df[,1] x<-yx.df[,2:643] #conver to matrix mat<-as.matrix(x) #get row number rownum<-nrow(mat) #remove the constant parameters mat1<-mat[,apply(mat,2,function(.col)!(all(.col[1]==.col[2:rownum])))] dim(yx.df) dim(mat1) #remove columns with numbers of

Hi there, I've a training dataset and a test dataset. My aim is to visually allocate the test data within the calibrated space reassembled by the PC's of the training data set, furthermore to keep the training data set coordinates fixed, so they can serve as ruler for measurement for additional test datasets coming up. Please find a minimum working example using the wine dataset below.

Dear Users, I ran factor analysis using R and SAS. However, I had different outputs from R and SAS. Why they provide different outputs? Especially, the factor loadings are different. I did real dataset(n=264), however, I had an extremely different from R and SAS. Why this things happened? Which software is correct on? Thanks in advance, - TY #R code with example data # A little

> 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