search for: nfactors

...r 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 (!is.null(nfactors)) { if (nfactors <= 50) Letters[1:nfactors] else paste("F", 1:nfactors, sep = "")} else NULL, default.levels = c(-1, 1), generators = NULL, resolution = NULL, estimable = NULL, max.nfree2fis = FALSE, randomize = TRUE, seed = NULL,...

  Hello All,   I am working with dlm for the purpose of estimating and forecasting with a Kalman filter model. I have succesfully set up the model and started generating results. Of course, I need to somehow be sure that the results make sense. Without any apparent target to compare with, my natural selection is the results by odinary least square. The idea being that if I choose a diffuse prior,

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 output of scores using "principal" OR, is there a way to limit the number of factors that are included whe...

...w to do so if I want to use ?principal in 'psych' and any of the GPArotation methods. For example, require(psych) r <- cor(mtcars[,c("am","carb","cyl","disp","drat","gear","hp","mpg")]) pca <- principal(r, nfactors = 8, residuals = T, rotate="none") # or 'varimax' or any other GPArotation supported rotation pca I've turned the 'pca' object and ?principal help page upside down and I still cannot find anything that would resemble a 'scores' value. I'm pretty sure it...

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 R-2.6.2-win32.exe. Thanks alot for answering me. Hue University, VietNam. -- View this messa...

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 if i do not ask for the scores if i ask for the scores i get an error message "Error in scale(x.matrix): object 'x.matr...

...3 1 2 1 1 3 3 1 1 1 1 1 1 3 3 1 2 1 1 3 3 1 1 1 2 3 3 1 2 1 1 3 4 1 1 1 1 3 3 3 3 1 1 1 1 3 4 1 1 1 1 3 3 1 2 1 1 3 3 1 1 1 2 3 3 1 1 1 1 4 4 5 5 6 6 5 6 4 6 4 5 6 5 6 4 5 4 ; run; proc factor data=fact rotate=varimax method=p nfactors=3; var v1-v6; run; /* Output from SAS*/ The FACTOR Procedure Initial Factor Method: Principal Components Prior Communality Estimates: ONE...

...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 and R Integration Plugin *R Packages required: psych, polycor, GPArotation, nFactors, corpcor, ICS, R.utils. set printback off. Error in library(polycor) : there is no package called 'polycor' This means the very good utility does not run in the way I have installed both R and SPSS Does anyone know what I've done wrong and how to overcome this? Prof. Gavin T L Brown,...

...XXXXXXXXXXXXXXXXXXXXXXX. DATASET NAME DatenSet1 WINDOW=FRONT. DATASET ACTIVATE DatenSet1. SAVE OUTFILE='C:\XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX /COMPRESSED. *M?rio Basto, Jos? Manuel Pereira, IPCA *Required: SPSS 19 and R Integration Plugin *R Packages required: psych, polycor, GPArotation, nFactors, corpcor, ICS. set printback off. -- Till Below (PhD Student) Humboldt-Universit?t zu Berlin

...onses, smooth=TRUE,global=TRUE,polycor=F, ??????????? ML = FALSE,??std.err=FALSE,progress=TRUE) #Regular PCA using stat, psych and FactoMiner, respectively #There is no option for?including the matrix princomp(responses, cor=TRUE) #What kind of correlation is used here? ? principal(r = responses, nfactors = 3, rotate = "Promax") principal(r = W, nfactors = 3, rotate = "Promax") #Do not work PCA(responses, scale.unit=TRUE, ncp=3, graph=T) #How to conduct polychoric principal component analysis using either of #the above package and producing individual and variable factor maps a...

...,2] [1,] 1 0 [2,] 1 1 [3,] 4 2 pc=prcomp(gg,center=TRUE,scale=TRUE) # The eigenvectors pc$rotation PC1 PC2 [1,] 0.7071068 0.7071068 [2,] 0.7071068 -0.7071068 # The eigenvalues: > pc$sdev^2 [1] 1.8660254 0.1339746 2. PSYCH Package: > pp=principal(gg,nfactors=2) # The eigenvectors > pp$loadings Loadings: PC1 PC2 [1,] 0.966 -0.259 [2,] 0.966 0.259 # The eigenvalues pp$values 1] 1.8660254 0.1339746 Sincerely, Arlindo [[alternative HTML version deleted]]

...1011 1974 54 58 3 5 1013 1974 65 52 9 24 1001 1975 67 56 1 23 1003 1975 63 55 6 1 1005 1975 60 51 1 47 1007 1975 62 49 7 10 1009 1975 68 50 9 32 1011 1975 55 58 3 5 1013 1975 66 52 9 27"),head=TRUE)) library(psych) pcascores<-principal(pca[,-2], nfactors=1, score=TRUE) However, the scores should be based on the observations per year rather than the full set of observations. The final output should be a data frame with 7 rows (one for each id) and 4 columns (one for each year) filled with the Principal Component Scores per id per year. Any ideas on...

...swers)$r[1:2,1:3] princomp(answers, cor=FALSE)$l[1:2,1:3] eigen(cov(answers))$ve[1:2,1:3] # These 3 lines gives the same result prcomp(answers, center=TRUE, scale=TRUE)$r[1:2,1:3] princomp(answers, cor=TRUE)$l[1:2,1:3] eigen(cor(answers))$ve[1:2,1:3] # This gives another result principal(answers, nfactors=3, rotate="none")$l[1:2,1:3] Furthermore, I tried to use oblimin on the PCA loadings via prcomp and 'principal', but they give different results: # These 2 lines give different results oblimin(prcomp(answers, center=TRUE, scale=TRUE)$r[,1:3])$l[1:2,] principal(answers, nfactor...

...out-of-date version of R. See: ftp://cran.r-project.org/pub/R/src/contrib/Archive/polycor/ Good luck; David. > > DATASET ACTIVATE DataSet1. > *M?rio Basto, Jos? Manuel Pereira, IPCA > *Required: SPSS 21 and R Integration Plugin > *R Packages required: psych, polycor, GPArotation, nFactors, corpcor, ICS, R.utils. > set printback off. > Error in library(polycor) : there is no package called 'polycor' > > This means the very good utility does not run in the way I have installed both R and SPSS > Does anyone know what I've done wrong and how to overcome this?...

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") >

Thanks to Mr.Liviu Androvic and Mr.Richard Rowe helped me in PCA. Because I have just learn R language in a few day so I have many problem. 1) I don't know why PCA rotation function not run although I try many times. Would you please hepl me and explain how to read the PCA map (both of rotated and unrotated) in a concrete example. 2) Where I can find document relate: Plan S(A), S(A*B),

Hi there! To make a good choice for the estimation of the number of usefull factors. And I'd like to plot a graph like: http://de.wikipedia.org/wiki/Bild:Parallelanalyse.jpg where I don't need the green line (random values). But with str(factanal(data, factors=10)) I cannot figure out where I can find the values for plotting. Can you please help me in this question? Thanks, Martin

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...

...code is below. Please help. label=data[,"Urban.Rural"] indexU<-which(label=="U") indexR<-which(label=="R") collab<-rep(0,length(data[,1])) collab[indexU]<-"Blue" collab[indexR]<-"Green" library(psych) fit <- principal(mydata, nfactors = num.fac, rotate="varimax", scores = TRUE) z1 <- sum(fit2$loadings[,1]^2) ### need to scale scores and loadings by these factors z2 <- sum(fit2$loadings[,2]^2) biplot(fit$scores[,c(1,2)]/c(z1, z2), loadings(fit)[,c(1,2)]*c(z1, z2), xlabs=rep("", length(collab)), col =...

I have an exercise. With 3 kinds of yohourt a,b,c. There are 25 participatients estimate 3 norms: taste (va,vb,vc), structure (ca,cb,cc) and price (ga,gb,gc) and give the mark from 1 to 5. I don't know how to PCA this data. Please help me! I attached the data file follow: Va Vb Vc Ca Cb Cc Ga Gb Gc 4 2 4 5 5 5 4 4 2 2 2 4 3 2 5 4 5 1 2 2 1 2 3 3 3 1 4 1 1 2 2 3 3 4 3 2 3 4 4 4 3 1 2 1 2 1 1 1