similar to: How to reconcile Kalman filter result (by package dlm) with linear regression?

  Hi, All,   This is the first time I seriously use this package. However, I am confused that the result is quite unstable. Maybe I wrote something wrong in the code? So could anybody give me some hint? Many thanks.   My test model is really simple. Y_t = X_t * a_t + noise(V)，(no Intercept here) a_t = a_{t-1} + noise(W)   I first run the following code: (I shall provide data at the end of the

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

Hi all, I''m looking to resolve foreign keys for forms -- similar to the way "streamlined" does it. Although the actual value stored in an object is the foreign key integer, I''d like the actual name of the item displayed. I''m *sure* it''s easy. Thoughts? Regards. --~--~---------~--~----~------------~-------~--~----~ You received this message because

Is there a way to generate identical random samples using R's runif function and SAS's ranuni function? I have assigned the same seed values in both software packages, but the following results show different results. Thanks! R === > set.seed(6) > random <- runif(10) > random [1] 0.6062683 0.9376420 0.2643521 0.3800939 0.8074834 0.9780757 0.9579337 [8] 0.7627319 0.5096485

Dear R-devel list members, Ben Fairbank draw it to my attention that factanal() (in the stats package) doesn't report factor correlations for oblique rotations. Looking at the source, I see that factanal also doesn't save the factor-transformation (rotation) matrix from which these correlations can be computed. I've modified the source, attached below, so that the transformation

I need to look more into it, but before I start the long and arduous "googling my life away" process, I figured someone might know the answer. I've read the man pages several times and they didn't change! :-( As normal, while looking at one thing, something else bites my butt. I tuned on the swap field in top and sort on it. Here's an edited snippet of the results. Mem:

Dear all, I just noticed that the 0.9 update for FrF2 did not work out for Mac OS due to an error in an example that ran without error on all other platforms. I do not find any reason for this. In the past, umlauts or tab characters have sometimes been an issue, but I didn't find any of these. The function definition is FrF2(nruns = NULL, nfactors = NULL, factor.names = if

I have data set with binary responses. I would like to conduct polychoric principal component analysis (pPCA). I know there are several packages used in PCA but I could not find one that directly estimate pPCA and graph the individuals and variables maps. I will appreciate any help that expand these reproducible scripts. #How to conduct polychoric principal component analysis pPCA using #either

A bit of a newbee to R and factor rotation I am trying to understand factor rotations and their implementation in R, particularly the GPArotation library. I have tried to reproduce some of the examples that I have found, e.g., I have taken the values from Jacksons example in "Oblimin Rotation", Encyclopedia of Biostatistics

Dear All, In a package, I want to use PCA function. The structure I used follow this page: http://www.statmethods.net/advstats/factor.html. fit<-principle(mydata, nfactors=9, rotation=TRUE) or: result<-PCA(mydata) But I don't known why R language in my computer noticed: "not found principle", "not found PCA". I download and installed

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

Haj i try to perform a principal component analysis by using a tetrachoric correlation matrix as data input tetra <- tetrachoric (image_na, correct=TRUE) t_matrix <- tetra$rho pca.tetra <- principal(t_matrix, nfactors = 10, n.obs = nrow(image_na), rotate="varimax", scores=TRUE) the problem i have is to compute the individual factor scores from the pca. the code runs perfect

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

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

Hi, I am trying to calculate Principal Component Scores per id per year using the psych package. The following lines provide the scores per obeservation pca = data.frame(read.table(textConnection(" id year A B C D 1001 1972 64 56 14 23 1003 1972 60 55 62 111 1005 1972 57 51 10 47 1007 1972 59 49 7 10 1009 1972 65 50 9 32 1011 1972 52 58 3 5 1013

Dear all, I am trying to conduct an enhanced version of factor analysis with a SPSS interface that allows to use R. This approach has been suggested in the recent article: Basto, M. and J.M. Pereira An SPSS R-Menu for Ordinal Factor Analysis. Journal of Statistical Software 46, pp. 1-29. My variables are ordinal-type and the tool of Basto allows to run polychoric correlations in the SPSS

I am not an R-Head, hence I use nice utilities that integrate R into SPSS I have SPSS v24, R3.20 and R3.40 I have run IBM SPSS R Integration which requires linking to R3.20 I have installed R Factor v2.4.2 This package requires 'polycor' library Unfortunately, 'polycor' does not exist in R3.20 DATASET ACTIVATE DataSet1. *M?rio Basto, Jos? Manuel Pereira, IPCA *Required: SPSS 21

> On Aug 5, 2017, at 7:02 PM, Gavin Brown <gt.brown at auckland.ac.nz> wrote: > > I am not an R-Head, hence I use nice utilities that integrate R into SPSS > I have SPSS v24, R3.20 and R3.40 > I have run IBM SPSS R Integration which requires linking to R3.20 > I have installed R Factor v2.4.2 > This package requires 'polycor' library > Unfortunately,

Dear R-List, I have carried out a factor analysis using fa (psych) with nfactors=2, rotation="oblimin" and fm="pa". Now I have to report both pattern AND structure matrix. As I have understood R-Documentation, this can be obtained by setting the "oblique.scores" argument TRUE (structure matrix) or FALSE (pattern matrix), respectively. However both produced the same

I'm using R for PCA and? factor analysis. I want to create biplots of varimax rotated factors that color-code points by their classification. My research is on streams that are urban and rural. So, I want to color code them by this classification. If you just do a biplot from prcomp or princomp, you cannot add this color. So, I have used some code developed by a graduate student in our