similar to: How does biplot.princomp scale its axes?

Hi, I run the example for princomp for R211 I got the following error for biplot > ## The variances of the variables in the > ## USArrests data vary by orders of magnitude, so scaling is appropriate > (pc.cr <http://pc.cr> <- princomp(USArrests)) # inappropriate Erreur dans cov.wt(z) : 'x' must contain finite values only > princomp(USArrests, cor = TRUE) # =^=

Dear list, I want to perform a biplot, using customized titels for the x and y axis. Setting xlab="" and ylab="" resulted in an error, e.g.: > data(USArrests) > biplot(princomp(USArrests),xlab="",ylab="") Error in biplot.default(t(t(scores[, choices])/lam), t(t(x$loadings[, : length of dimnames[1] not equal to array extent > How do I

Hi is there a way to plot only the loadings in a biplot (with the nice arrows), and to skip the scores? thanks christoph

Hi everyone. I performing a simple PCA using the princomp function. Then, I use the biplot function to show it. However, the function use line number to represent samples. I would like to know if there's a way to use a dot (point) instead of the line number when using the biplot function. With regards, Phil -- View this message in context:

Which is the parameter that is used to decrease the size of ylabs plotted in biplot? I tried playing with cex and cex.lab I am not getting it right pc <- princomp(USArrests) biplot(pc, xlabs = rep("", nrow(USArrests)),ylabs=(colnames(USArrests))) Thanks../Murli [[alternative HTML version deleted]]

x=rmvnorm(2000, rep(0, 6), diag(c(5, rep(1,5)))) x=scale(x, center=T, scale=F) pc <- princomp(x) biplot(pc) There are a bunch of red arrows plotted, what do they mean? I knew that the first arrow labelled with "Var1" should be pointing the most varying direction of the data-set (if we think them as 2000 data points, each being a vector of size 6). I also read from

Full_Name: Hinrich Göhlmann Version: 1.4.1 OS: Windows 2000 Submission from: (NULL) (148.177.130.121) Hi all, I've searched the known bugs and hopefully I did not oversee this one... When I have done a normal zz<-princomp(na.omit(raw.data)) and I then draw a biplot with biplot(zz,cex=c(.1,.7),col=c(5,1),choice=c(1,2),xlim=c(-2,2),ylim=c(-5,2)) the x axis gets set properly, but the y

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

In comparing the results of princomp and prcomp I find: 1. The reported standard deviations are similar but about 1% from each other, which seems well above round-off error. 2. princomp returns what I understand are variances and cumulative variances accounted for by each principal component which are all equal. "SS loadings" is always 1. 3. Same happens

I am doing a principle component analysis on a dataset with a lot of different variables and have constructed a biplot of the data. Unfortunately, as can be seen on the attached image, the biplot is very messy, cluttered, and hard to read. I have performed a few modifications including outlier removal from a few of the variables, which has made the plot better, however it still is too cluttered

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

Dear R-help fellows good afternoon. I am struggling in the attempt to impose some graphical conditions (changing point symbols, colors, etc) to biplot function (I am using it to visualize the results of princomp) but I can't apparently manage to change anything but the axis... and I have been browsing manuals and vignettes without finding any explicit suggestions on how to operate... Can

Hello, I have a graphical problem concerning a biplot that I was ask to insert in a scientific work to be published. I've performed a princomp analysis and produce the relative biplot but the labels of variables are out of margin (see attached file). When I try to wide the x axis by the function xlim=c(i:j), nothing happens if not a partial shift of the x range. Is there any specific

Dear all, i wonder if there?s a command to obtain the actual values of a principal component or a factor (not as.factor, but factanal) . test=princomp(USArrests, cor = TRUE) summary(test) just outputs, standard deviation, Prop of Variance and cumulative proportion of variance. test$loadings offers yet another proportion of variance scheme. why is that? Apart from that: Is there a

Dear R users, When I tried to use princomp() from stats packages to do Principal Components Analysis, I am not very clear what is the "scale". And the scores are different from "PROC PRINCOMP" procedure from SAS. Using the example data from this package: restpc <- princomp(USArrests, cor = TRUE) > restpc$scale Murder Assault UrbanPop Rape 4.311735 82.500075

Full_Name: Jerome Asselin Version: 1.7.1 OS: Red Hat Linux 7.2 Submission from: (NULL) (24.77.125.119) Setting the parameter na.action=na.omit should remove incomplete records in princomp. However this does not seem to work as expected. See example below. Sincerely, Jerome Asselin data(USArrests) princomp(USArrests, cor = TRUE) #THIS WORKS USArrests[1,3] <- NA princomp(USArrests, cor =

Dear R-helpers, The vectors in my biplot are completely obscured by the ~1400 labels R is printing on my biplot. I don't really care about the labels. How can I make the biplot without the annoying labels? See attached, if that helps you see my problem. Many thanks, Mark Na

Hi, I think that the documentation for the biplot function `biplot.princomp' is inconsistent with what it actually does. Here is what the documentation states: pc.biplot If true, use what Gabriel (1971) refers to as a "principal component biplot", with lambda = 1 and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner products between

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

[was sent a wrong R-help address; manually resent by MM] Hello I'am using the 'biplot' and 'biplot.pincomp' functions of the 'mva' package for my studies. The biplot represents both the observations and the variables of a matrix of multivariate data on the same plot. The observations are represented by their numbers (the line of the data matrix), but I would need to