similar to: LDA with previous PCA for dimensionality reduction

Can't make sense of calculated results and hope I'll find help here. I've collected answers from about 600 persons concerning three variables. I hypothesise those three variables to be components (or indicators) of one latent factor. In order to reduce data (vars), I had the following idea: Calculate the factor underlying these three vars. Use the loadings and the original var

Dear all I'm unable to find an example of extracting the rotated scores of a principal components analysis. I can do this easily for the un-rotated version. data(mtcars) .PC <- princomp(~am+carb+cyl+disp+drat+gear+hp+mpg, cor=TRUE, data=mtcars) unclass(loadings(.PC)) # component loadings summary(.PC) # proportions of variance mtcars$PC1 <- .PC$scores[,1] # extract un-rotated scores of

Dear R users, i would like to apply a PCA on image data for data reduction. The image data is available as three matrices for the RGB values. At the moment i use x <- data.frame(R,G,B)#convert image data to data frame pca<-princomp(x,retx = TRUE) This is working so far. >From this results then i want to create a new matrix from the first (second..) principal component. Here i stuck.

Hi There, I'm attempting to plot 10 values on a three-dimensional PCA with text labels next to each point. While i have no trouble doing this on 2D plots using the 'text' or 'textxy' function, I cannot find a function to do this on a 3D plot. I am using princomp for my PCA: >PCA<-princomp(eucdata, cor=TRUE) >PCA$scores [,1:3] # the three principal components i

Hi all, I am using the vegan package to run a prcincipal components analysis on forest structural variables (tree density, basal area, average height, regeneration density) in R. However, I could not find out how to extract factor loadings (correlations of each variable with each pca axis), as is straightforwar in princomp. Do anyone know how to do that? Moreover, do anyone knows

Hi all, I have a difficulty to calculate the PCA scores. The PCA scores I calculated doesn't match with the scores generated by R, mypca<-princomp(mymatrix, cor=T) myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings) Does anybody know how the mypca$scores were calculated? Is my formula not correct? Thanks a lot! Phoebe [[alternative HTML version deleted]]

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

Hi, I created a principal component plot using the first two principal components. I used the function princomp() to calculate the scores. now, I would like to superimpose an ellipse representing the center and the 95% confidence interval of a series of points in my plot (as to illustrate the grouping of my samples). I looked at the ellipse() function in the ellipse package but can't get it

Dear all, I have a question about the 'sdev' value returned by the princomp function (which does principal components analysis). On the help page for princomp it says 'sdev' is 'the standard deviations of the principal components'. However, when I calculate the principal components for the USArrests data set, I don't find this to be the case: Here is how I

Hi all: I am trying to do PCA on the following matrix. N1 N2 A1 A2 B1 B2 gene_a 90 110 190 210 290 310 gene_b 190 210 390 410 590 610 gene_c 90 110 110 90 120 80 gene_d 200 100 400 90 600 200 >dataf<-read.table("matrix") >

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

I am running a PCA, but would like to rotate my data and limit the number of factors that are analyzed. I can do this using the "principal" command from the psych package [principal(my.data, nfactors=3,rotate="varimax")], but the issue is that this does not report scores for the Principal Components the way "princomp" does. My question is: Can you get an

Hi- I'm trying to make my PCA (princomp) colored. In my csv excel sheet, I have the first column numbered according to the groupings I want to assign to the PCA. I've played around with trying to set this first column as the color vector, but haven't had any luck. Any suggestions? Thanks, Hillary [[alternative HTML version deleted]]

Hi, I have a problem with making PCA plots that are readable. I would like to set different sympols instead of the numbers of my samples or their names, that I get plotted (xlabs). How is this possible? With points, i don´t seem to get the right data plotted onto the PCA plot, as I do not quite understand from where it is taken. I dont know how to plot the correct columns of the prcomp

Hi All, I have been trying to get to plot my PCA legend outside of the PCA plot, but success still alludes me. Can you guys please advise how I can achieve this. I used locater() to obtain coordinates for below the Comp.1 axis. Using these coordinates the legend disappears. Below is the code for the PCA and legend. Thanks in advance for the help. Regards Tinus r.cols <-

Hi, I was trying as well as looking for an answer without success (a bit strange since it should be an easy problem) and therefore I will appreciate you help: My simple script is: # Loadings data of 5 columns and 100 rows of data data1<-read.csv("C:/?/MyPCA.csv") pairs(data1[,1:4]) pca1 <- princomp(data1[,1:4], score=TRUE, cor=TRUE) biplot(pca1) The biplot present the data

Hello, I am trying to complete a PCA on a set of standardized ring widths from 8 different sites (T10, T9, T8, T7, T6, T5, T3, and T2). The following is a small portion of my data: T10 T9 T8 T7 T6 T5 T3 T2 1.33738 0.92669 0.91146 0.98922 0.9308 0.88201 0.92287 0.91775 0.82181 1.05319 0.92908 0.97971 0.95165 0.98029 1.14048 0.77803 0.88294 0.96413 0.90893 0.87957 0.9961 0.74926 0.71394 0.70877

Dear Folks I need to rotate PCA loadings and scores using R. I have run a pca using princomp and I have rotated PCA results with varimax. Using varimax R gives me back just rotated PC loadings without rotated PC scores. Does anybody know how I can obtain/calculate rotated PC scores with R? Your kindly help is appreciated in advance Francesca [[alternative HTML version deleted]]

Hi, there: I am wondering if anyone here can provide an example using pca doing dimension reduction for a dataset. The dataset can be n*q (n>=q or n<=q). As to dimension reduction, are there other implementations for like ICA, Isomap, Locally Linear Embedding... Thanks, weiwei -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context: