similar to: PCA prcomp problem

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

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

Dear all, I've used the 'prcomp' command to calculate the eigenvalues and eigenvectors of a matrix(gg). Using the command 'principal' from the 'psych' package  I've performed the same exercise. I got the same eigenvalues but different eigenvectors. Is there any reason for that difference? Below are the steps I've followed: 1. PRCOMP #defining the matrix

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

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

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

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

Hi ! I've used the example given in the documentation for the prcomp function both in R and SPAD to compare the results obtained. Surprisingly, I do not obtain the same results for the coordinates of the principal composantes with these two softwares. using USArrests data I obtain with R : > summary(prcomp(USArrests)) Importance of components: PC1 PC2

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.

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: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

The following seems to be an bug in prcomp(): > test <- ts( matrix( c(NA, 2:5, NA, 7:10), 5, 2)) > test Time Series: Start = 1 End = 5 Frequency = 1 Series 1 Series 2 1 NA NA 2 2 7 3 3 8 4 4 9 5 5 10 > prcomp(test, scale.=TRUE, na.action=na.omit) Erro en svd(x, nu = 0) : infinite or missing values in 'x'

Hello, It might be a trivial question but I just wanted to find out the relationship between sdev and proportion of variance generated by prcomp. I got the following result from my data set ???????????????????????????? PC1????? PC2????? PC3 Standard deviation???? 104.89454 15.40910 9.012047 Proportion of Variance?? 0.52344? 0.01130 0.003860 Cumulative Proportion??? 0.52344? 0.53474 0.538600

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

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

In the manual page for prcomp(), it says that sdev is "the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)." ?However, this is not what I'm finding. ?The values appear to be the standard deviations of a reprojection of

Please could someone point me in the right direction as I appear to be having a total mental block with fairly basic PCA problem! I have a large dataframe where rows represent independent observations and columns are variables. I am wanting to perform PCA sequentially on blocks of nrows at a time and produce a graphical output of the loadings for the first 2 EOFs for each variable. I'm sure

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

Hi Listers, I am trying to reflect a PCA biplot in the x-axis (i.e. PC1) but am not having much success. In theory I believe all I need to do is multiply the site and species scores for the PC1 by -1, which would effectively flip the biplot. I am creating a blank plot using the plot command and accessing the results from a call to rda. I then use the calls to scores to obtain separate site and