similar to: Getting group-wise standard scores of a vector

Hi, I have a dataset of historical monthly temperature data that is grouped by weather station. I want to create z-scores of the monthly data using a base period of a subset of years. I subset the dataset first to include only data from the years (V2) that make up the base period so I could calculate the appropriate means and standard deviations V1 V2 V3 V12 V15 V16 V19 84

Hi all, I've become quite enamored of R lately, and have decided to try to teach some of its basics (reading in data, manipulation and classical stats analyses) to my fellow grad students at the University of Toronto. I sent out a mass email and have already received some positive responses. One student, however, wanted to know what differentiates the routines that R uses, from those

Hi, I'm new to R so please fogive if I write someting silly ... I need to recode a series of responses from a number of questionnaires. The data is read via ODBC from a database where all responses are coded as tables of the form (id, question, score). After dealing with recoding of missing values, I need to "invert" the scores of some questionnaire's item in the form x <-

Hi there, I want to figure out how to plot means, with 2 decimal places, of any Y variable on a scatterplot according to any X variable (which obviously should have limited scope). I already figured out how to plot the means, but without limiting their precision to 2 decimal places. This is the code I used once I had the scatterplot drawn: text(c(1990, 1992, 1994, 1996, 1998, 1999, 2000,

Hi I have used fa() to perform a factor analysis of a psychological battery which is thought to have 11 factors. I can identify which factors the loadings relate to easily enough because I can see which items are loading onto each of the columns in the $loading output. However, how can I identify which items or loadings are being used to create each of the columns in the $scores output? I have

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

Hi, First I should note I am relatively new to R so I would appreciate answers that take this into account. I am trying to perform an MDS ordination using the function ?metaMDS? of the ?vegan? package. I want to ordinate species according to a set of functional traits. ?Species? here refers to ?sites? in traditional vegetation analyses while ?traits? here correspond to ?species? in such

Dear List! I am trying to calculate the distance between original data points and their position in the PLS model. In order to do this, I tried to predict the scores using the predict.mvr function and calculate the corresponding positions in variable space. The prediction of scores works perfectly: ------ data(trees) # build model t<-plsr(Volume~.,data=trees) # predict scores for training

The function factor.scores is used with package ltm and others to estimate IRT type scores for various models. It inherits objects of class grm, gpcm and a few others. What I would like to do is to use the factor.scores function, but feed it my own item parameters (from a bifactor model where the 2PL parameters are adjusted for the bifactor structure). Does anybody have an idea of how this might

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

Hello, I need help in performing a Van_der_Waerden normal scores test in R. I have two arrays of scores(final on therapy scores from drug and placebo) and want to use the normal scores procdeure to test for significance. (observations are unequal in number - due to dropouts). Could you please help me out with the coding or let me know if there is a package that can be used (for example,

Hello, Let's say I've drawn a random sample (sample1.df) from a large data frame (main.df), and I want to create a second random sample (sample2.df) where the values in its ID column *are not* in the equivalent ID column in the first sample (sample1.df). How would I go about doing this? In other words: The values in sample2.df$ID *are not found* in sample1.df$ID, and both samples are

Hi Stuart, I often use this small function standardize <- function(x) ( x - mean(x, na.rm=T) ) / sqrt(var(x, na.rm=T)) to standardize variables. You should be able to use this to do what you want by splitting the data frame into sections based on the factor level, using standardize() to create a new variable in each section, then paste the data frame back together. Something like: #

I am sure there is a simple way to do the following, but i haven't been able to find it. I am hoping a merciful soul on R-help could point me in the right direction. I am doing a factor analysis on survey data with missing values. to do this, I run: FA1<-factanal(na.omit(DATA), factors = X, rotation = 'oblimin', scores = 'regression') Now that I have my factors and

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

hello, I am doing PCA in R using some habitat factors, and I used the function result1=rda() and result2=princomp(),then pick up scores of the result1 and result2 using scores(),but the scores are significantly different,i do not know the meaning of it. Best wishes! Cheng

Hi all, I have a difficulty to calculate the PCA scores. The PCA scores I calculated doesn't match with the scores generated by R, mypca<-princomp(mymatrix, cor=T) myscore<-as.matrix(mymatrix)%*%as.matrix(mypca$loadings) Does anybody know how the mypca$scores were calculated? Is my formula not correct? Thanks a lot! Phoebe [[alternative HTML version deleted]]

Hi, I checked the ODG (Objective Difference Grade) scores for a few reference vectors using the PQEvalAudio Tool and found that some of them show ODG scores as high as -3.5 If we look at the range as described in the link below, it looks unacceptable. http://www-mmsp.ece.mcgill.ca/documents/Software/Packages/AFsp/PQevalAudio.html Am I missing something or are these scores valid? Thanks and

Hi Rhishi, PQevalaudio is very unreliable and buggy. I have compared to PEAQ and - as a result - now I am not using it anymore. With best regards, Christian Hoene Von: opus-bounces at xiph.org [mailto:opus-bounces at xiph.org] Im Auftrag von Rhishikesh Agashe Gesendet: Freitag, 4. Oktober 2013 12:35 An: opus at xiph.org Cc: Rasmi Mishra Betreff: [opus] ODG (Objective Difference

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 <-