similar to: small scales in fwdmsa

I'm using the fwdmsa package to identify deviant cases in a Mokken scale analysis. I've run into a problem., separate from the one I posted previously. The problem comes with items that are "easy" by IRT standards. A good scale should include a range of difficulties; yet when I include "easy" items in a forward search I continuously run into the problem that these items

An updated version of the package plink has been uploaded to CRAN. This is a major revision that now includes multidimensional models and methods. plink is a package for conducting unidimensional and multidimensional IRT-based test linking using separate calibration methods for multiple groups for single-format or mixed-format common items. The package supports sixteen IRT models and eleven

An updated version of the package plink has been uploaded to CRAN. This is a major revision that now includes multidimensional models and methods. plink is a package for conducting unidimensional and multidimensional IRT-based test linking using separate calibration methods for multiple groups for single-format or mixed-format common items. The package supports sixteen IRT models and eleven

I was wondering if someone could point in the direction of a package that could generate not heatmaps, but something like a unidimensional heat map. I might be mistaken, but it seems like image and heatmap are an overkill for such a simple task. For example, if I have a data frame: x<-data.frame(myname=paste("value",1:10,sep=""),a=1:10,b=sample(1:10,10,replace=T)) I'd

I'm trying to put together an R routine to conduct unidimensional unfolding scaling analysis using maximum likelihood. My problem is that ML optimization will get stuck at latent scale points that are far from optimal. The point optimizes on one of the observed variables but not others and for ML to move away from this 'local optimum', it has to move in a direction in which the

Dear R community, I was trying to estimate density at point zero of a multivariate distribution (9 dimensions) and for this I was using a multinormal approximation and the function dmvnorm , gtools package. To have a sense of the error I tried to look the mismatch between a unidimensional version of my distribution and estimate density at point zero with function density, dmvnorm and dnorm. At

Hi all, Why subscripting a one column matrix drops one dimension? > x<- matrix(rnorm(100), ncol=1) > str(x) num [1:100, 1] -0.413 -0.845 -1.625 -1.393 0.507 ... > str(x[20:30,]) num [1:11] -0.315 -0.693 -0.771 0.448 0.204 ... > str(x[20:30]) num [1:11] -0.315 -0.693 -0.771 0.448 0.204 ... This breaks: > cov(x) [,1] [1,] 0.9600812 >

dear R experts--- I have (many) unidimensional root problems. think loc.of.root <- uniroot( f= function(x,a) log( exp(a) + a) + a, c(.,9e10), a=rnorm(1) ) $root (for some coefficients a, there won't be a solution; for others, it may exceed the domain. implied volatilities in various Black-Scholes formulas and variant formulas are like this, too.) except I don't need 1 root, but a

This is more of a conceptual/methodological question than "how to do it in R", so anyone who cares to reply might want to do it off list. I have censored rank order data .. electors have been asked to rank the 4 most important issues out of a list of 20. For each individual we therefore have a vector of 20 measurements 1..5, where 1..4 are ranks and 5 = not ranked/less important than

The first version of the package plink has been uploaded to CRAN. plink is a package for conducting unidimensional IRT scaling and chain linking for multiple groups for single-format or mixed-format common items. The package supports eight IRT models and four calibration methods. Dichotomous Models: 1PL, 2PL, 3PL Polytomous Models: -Graded response model -Partial credit model -Generalized

The first version of the package plink has been uploaded to CRAN. plink is a package for conducting unidimensional IRT scaling and chain linking for multiple groups for single-format or mixed-format common items. The package supports eight IRT models and four calibration methods. Dichotomous Models: 1PL, 2PL, 3PL Polytomous Models: -Graded response model -Partial credit model -Generalized

CRAN (and crantastic) updates this week New packages ------------ * allan (1.0) Alan Lee http://crantastic.org/packages/allan Automates Large Linear Analysis Model Fitting * andrews (1.0) Jaroslav Myslivec http://crantastic.org/packages/andrews Andrews curves for visualization of multidimensional data * anesrake (0.3) Josh Pasek http://crantastic.org/packages/anesrake This

I am trying to calculate monthly means by year of phosphates and nitrates from a multi year data set. Can anybody help me out with the most effective way to do this? My data looks like this: Collection_Date                   Test.Name              Value 2000-01-24 17:00:00            Phosphate               0.108 2000-01-24 17:00:00            Nitrate                     0.037 2001-11-12

Alternatively, just type debug(optim) before using it, then step through it by hitting enter repeatedly... When you're done, do undebug(optim). Andy > From: Spencer Graves > > Have you looked at the code for "optim"? If you > execute "optim", it > will list the code. You can copy that into a script file and walk > through it line by line to

Hi, I am having trouble understanding how to approach a simulation: I have a sample of n=250 from a population of N=2,000 individuals, and I would like to use either permutation test or bootstrap to test whether this particular sample is significantly different from the values of any other random samples of the same population. I thought I needed to take random samples (but I am not sure how

Hi, this is merely FYI, but i would appreciate if someone had any comments or further information on the topic. We were using the following setup : cryptocard easyradius with RB-1 hardware tokens (hex or decimal display, synchronous (quicklog) mode) f-secure ssh with pam radius authentication This worked fine until we updated to openssh 2.9p2. Then all authentications where the response

Estimados una consulta me encuentro graficando un histograma cuyos datos no provienen de una distribución clásica como la normal exponenial, poisson, etc, Lo que necesito es colocar una curva no paramétrica que permita evidenciar el ajuste de los datos a esa curva ya que son muchos (alrededor de 80000). Muchas gracias [[alternative HTML version deleted]]

En cualquier caso, para nada necesitas normalidad en las variables. De las distribuciones que presentas, y abundando en lo que te indica Olivier, ¿sería interesante considerar sólo 2 grupos (?normal? y ?enhorabuena?) en las variables 2 y 3? Todo esto sin saber de lo que estamos tratando, claro? XD Un saludo, Isidro De: Olivier Nuñez [mailto:onunez en unex.es] Enviado el: martes, 30 de

> -----Original Message----- > From: Robert Adkins II [mailto:radkins@impelind.com] > I have read a few more of your responses. It appears that you > believe wholeheartedly that your more advanced questions are going > unanswered simply because of the volume of lower skilled questions. I see it go both ways. The really "interesting" problems that uncover new bugs in

Dear useRs, new and updated features in eRm 0.9-6 for extended Rasch modeling: - infit and outfit mean-square statistics added to functions personfit() and itemfit(). - new method: plotPImap() plots a person-item map (cf. Bond & Fox, 2007), i.e., a map of locations of item (and threshold) parameters, and the distribution of person parameters. - new options in plotGOF(): 'conf':