similar to: adding factor scores back to an incomplete dataset...

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

Hi, I wonder why factor scores produced by factanal are correlated, and I'd appreciate any hints from people that may help me to get a deeper understanding why that's the case. By the way: I'm a psychologist used to SPSS, so that question my sound a little silly to your ears. Here's my minimal example: *********************************************** v1 <-

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

Hi, I have a couple questions about fitting environmental (land use factors, plant species presence-absence, and soil variables) constraints to my CCA biplot. 1. After successfully plotting species and site scores in my CCA, I have been trying to insert the biplot arrows of the environmental constraints in my data set using the text() function. When I do that, the plot changes completely. Is there

Greetings, all: I am using factanal in R. When I enter a matrix or a formula, the print method winds up with something like this: Test of the hypothesis that 6 factors are sufficient. The chi square statistic is 28.1 on 22 degrees of freedom. The p-value is 0.172 But when I enter a covmat, the print method winds up with something like this: The degrees of freedom for the model is 22 and the

Dear expeRts, sorry for such a newbie question - in PCA/factor analysis e.g. in SPSS it is possible to save scores from the factors. Is it analogously possible to "save" the implied scores from the latent variables in a measurement model or structural model e.g. using the sem or lavaan packages, to use in further analyses? Best wishes Steve Powell www.promente.org | skype

We have a project on which we need to compare various methods for computing factor scores. Are there any R routines available that do parametric bootstrap or compute factor scores?

Hello R-user community, I am applying the function metaMDS. However, I would like to know if there is any option to export the data I got from the axis as a data frame. I have tried as.data.frame.list but is not working. Any suggestion? Thank you in advance for your help, Lineth [[alternative HTML version deleted]]

I'm working with a data collected through complex survey design. My goal is to conduct a factor analysis to extract two a priori, known factors, and to get factor scores for these factors. Unfortunately, the "svyfactanal" procedure from the Survey package does not allow for the calculation of either Thompson regression scores or Bartlett scores. So, I found several sources that say

I''m running a loop (illustrated below) that retrieves records from a table based on an id that might be different with each iteration. The loop uses the array scrns (composed of IDs) to find the related records in the Screens table. I''m attempting to store the returned results in an instance variable so I can use that data in a view. However, this code simply is overwriting

Hello, I am using vegan to do an NMDS plot and I would like to suppress the labels for the loading vectors. Is this possible? Alternatively, how can I avoid overlap? Many thanks for the help. Example code: #perform NMDS using metaMDS() function spe.nmds<-metaMDS(data, distance='bray',k=2 , engine = "isoMDS", autotransform=F, trymax=1000) #calculate the loading (i.e.,

In R factanal output, I can't find a function to give me residuals e. I mannually got it by using x -lamda1*f1 -lamda2*f2 - ... -lamdan*fn, but the e I got are not uncorrelated with all the f's. What did I do wrong? Please help. Yijun ____________________________________________________________________________________ Be a better friend, newshound, and

Dear friends, I'm using R to produce the following Factor Analysis: > matriz.cor<-hetcor(matrix(as.factor(data), ncol=variables, byrow=T))$correlations > factanal(x=data, factors=2, covmat=matriz.cor, scores='regression') Then the screen output shows the following message: Error en factanal(x = data, factors = 2, covmat = matrix, : requested scores without

--=-hiYzUeWcRJ/+kx41aPIZ Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: 8bit Hi, on March 10 I filed a wishlist bug report asking for the inclusion of some changes to factanal() and the associated print method. The changes were originally proposed by John Fox in 2005; they make print.factanal() display factor correlations if factanal() is called with rotation =

Dear R users Happy New year to all for a start. Below is some data that I ran a factor analysis on. Using $score prints the scores for each of the three factors. However, I would like to access those factors as variable for new computations. How do I do that? In SPSS we just call fact1_1, fact2_1 etc.. Thanks for your suggestions. V ============ v1 <- c(1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,4,5,6) v2

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 am having difficulty obtaining the scores from my principal component analysis. I have used this method before and have had no problems. The data set that I am using this time is similar to what I have used in the past. What do I need to do to my dataset in order for me to obtain these scores? R screen says the following message Error in factanal(covmat = pasa.cov, factors = 4) :

> Does factanal() force the user to use the formula interface if they > wish to specify an na.action? Short answer: yes. Long answer: The handling of na.action is a built in part of the formula processing functions, so it's automatic when dealing with a formula. There are also downstream effects on predict() and resid() that are worked out for the formula case, but aren't clear

hi, Does factanal() force the user to use the formula interface if they wish to specify an na.action? v1 <- c(1,1,1,1,1,1,1,1,NA,1,3,3,3,3,3,4,5,6) v2 <- c(1,2,1,1,1,1,2,1,2,1,3,NA,3,3,3,4,6,5) v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6) v4 <- c(3,3,4,NA,3,1,1,2,1,1,1,1,2,NA,1,5,6,4) v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5) v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4) m1

Dear Contributors, I am asking help on the way how to solve a problem related to loops for that I always get confused with. I would like to perform the following procedure in a compact way. Consider that p is a matrix composed of 100 rows and three columns. I need to calculate the sum over some rows of each column separately, as follows: fa1<-(colSums(p[1:25,])) fa2<-(colSums(p[26:50,]))