similar to: Factor Loadings in Vegan's PCA

Dear Friends, Using package Vegan, I need to calculate Shannons Diversity index and Pielou's Evenness for a set of 20 study areas. Each area is represented by a matrix of 25 sample plots x tree species. The code is as following, where data stands for the data matrix of any of the 20 areas: S <- specnumber(data) H <- diversity(data) J <- H/log(S) I indexed the 20 areas by a

Dear friends, I am sudying the mvpart package, that implements Multivariate Regression Trees, aiming at applying it to a biogeographical dataset of tree speces in southern South America. My doubt is how to access plot identities after the tree is produced. For us it is rather important, but I could not find them with neither 'summary(fit)'[where fit is the object containing the

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

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

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

hi, I have two questions: #first (SPSS vs. R): I just compared the output of different PCA routines in R (pca, prcomp, princomp) with results from SPSS. the loadings of the variables differ vastly! in SPSS the variables load constantly higher than in R. I made sure that both progr. use the correlation matrix as basis. I found the same problem with rotated values (varimax rotation and rtex=T

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

I start a clean session of R 1.9.1 on Windows and I run the following code: > library(MASS) > data(painters) > pca.painters <- princomp(painters[ ,1:4]) > loadings(pca.painters) Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Composition 0.484 -0.376 0.784 -0.101 Drawing 0.424 0.187 -0.280 -0.841 Colour -0.381 -0.845 -0.211 -0.310 Expression 0.664 -0.330 -0.513

Hi all I?m trying to tests the significance of loadings from a ordination of 46 variables (caategorical, ordinal and nominal). I used dudi.mix from ade4 for the ordination. A years ago Jari Oksanen wrote this script implementing Peres-Neto et al. 2003 (Ecology) bootstraping method: netoboot <- function (x, permutations=1000, ...) { pcnull <- princomp(x, cor = TRUE, ...) res

May be a simple question, but not understanding why in princomp I get different results for loadings and summary for my eigenvectors and eigenvalues. When I use pc.cr$loadings using the USArrests dataset the proportion of variance is equal for each of the components, but when summary(pc.cr) is used the proportion of variance is showing different proportions. My question is why do they differ? I

I'm attempting to modify how biplot draws its red vectors (among other things). This is how I've started: Biplot <- function(xx, comps = c(1, 2), cex = c(.6, .4)) { ## Purpose: Makes a biplot with princomp() object to not show arrows ## ---------------------------------------------------------------------- ## Arguments: xx is an object made using princomp() ##

Dear list, I have a question concerning the above mentioned methods in the pls package with respect to the loadings matrix produced by the call. In some work I am doing I have found that the values produced are nearly of the same magnitude but of opposite sign. When I use the example data (sensory) I find this result reproduced. I am prepared to work this through but I have a feeling that

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- 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]]

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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 R users, I am looking for some advice on the proper construction of a mixed model in R, using the difference in means as the response and treating within-means residuals as a random effect. I have a dataframe (my own, a snippet of which is given below) that is composed of observations of pollen viability in flowers along tree branches. Flowers (1 to 3 per position) were collected from

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 have a data frame with two columns, one of which is a factor (Species) and the other is numeric (BA, which stands for basal area). Here's a sample: Species BA ACSA 55.7632696 FRAM 122.9933524 ACSA 67.54424205 ACSA 89.22123136 ACSA 82.46680716 ACSA 22.46238747 ACSA 19.94911335 ACSA 20.42035225 ACSA 19.00663555 ACSA 21.67698931 ACSA 57.80530483 ACSA 30.31636911 Dead 43.98229715 Dead

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