similar to: Difference scores

Brian Ripley pointed out that there is already an R function scale() that does the work of my standardize(). -Greg -----Original Message----- From: Warnes, Gregory R Sent: Friday, January 18, 2002 9:38 AM To: 'Stuart Leask' Cc: 'r-help at stat.math.ethz.ch' Subject: RE: z-scores for different factor levels Hi Stuart, I often use this small function standardize <-

In that case, can you please suggest a reliable tool? Thanks, Rhishi From: Christian Hoene [mailto:christian.hoene at symonics.com] Sent: Friday, October 04, 2013 17:30 To: Rhishikesh Agashe; opus at xiph.org Cc: Rasmi Mishra Subject: AW: [opus] ODG (Objective Difference Grade) scores for Opus Encoder using PQEvalAudio Tool Hi Rhishi, PQevalaudio is very unreliable and buggy. I have compared

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

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 everyone, I am trying to get the factor score for each individual case from a principal component analysis, as I understand, both princomp() and prcomp() can not produce this factor score, the principal() in psych package has this option: scores=T, but after running the code, I could not figure out how to show the factor score results. Here is my code, could anyone give me some advice please?

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

Hello! I'd like to create an assets-based economic indicator using data from a national household survey. The economic indicator is to be the first principal component from a principal components analysis, which (given the source of the data) I believe should take in consideration the sampling weights of the observations. After running the PCA with svyprcomp(), from the survey package, I

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 have reached the correlation section in a course that I teach and I hit upon the idea of using data from the weekly Bowl Championship Series (BCS) rankings to illustrate different techniques for assessing correlation. For those not familiar with college football in the United States (where "football" refers to American football, not what is called soccer here and football in most

Hello, I have three numerical variables and I would like to test if their correlation is significantly different. I have seen that there is a package that "Test the difference between two (paired or unpaired) correlations". [https://www.personality-project.org/r/html/paired.r.html] However, there is the need to convert the correlations to "z scores using the Fisher r-z

Hi, I used "princomp" for PCA analysis based on correlation matrix (cor=T). I would like to reproduce the scores for each observation by first standardizing the data matrix (mean=0, std err=1), and then multiplied by the loadings of each variable for each principle components. I get very close numbers, but not exactly the same. anything I missed here? tahnks

Dear R-help members, I'm trying to run LME model on some behavioral data and need confirmations about what I'm doing... Here's the story... I have some behavioral reaction time (RT) data (participants have to detect dome kind of auditory stimuli). the dependant variable is RT measured in milliseconds. 61 participants were tested separated in 4 age groups (unblanced groups,

Thank you, but this now sounds more difficult: what would be the point in having these ready-made functions if I have to do it manually? Anyway, How would I implement the last part? On Thu, Mar 23, 2023 at 1:23?AM Ebert,Timothy Aaron <tebert at ufl.edu> wrote: > > If you are open to other options: > The null hypothesis is that there is no difference. > If I have two equations

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

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, 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 <-

Hello- A basic question which has nonetheless floored me entirely. I have a dataset which looks like this: Type ID Date Value A 1 16/09/2020 8 A 1 23/09/2010 9 B 3 18/8/2010 7 B 1 13/5/2010 6 There are two Types, which correspond to different individuals in different conditions, and loads of ID labels (1:50)

First off, Marc Schwartz posted this link earlier today, read it. http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-are-p_002dvalues-not-di splayed-when-using-lmer_0028_0029_003f Second, your email is not really descriptive enough. I have no idea what OR is, so I have no reaction. Third, you're comparing estimates from different methods of estimation. lmer will give standard errors that

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 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,