search for: unidimensionality

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

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

...1 0 1 1 5 1 1 1 1 0 1 0 1 1 1 1 0 1 1 0 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 When I run the fwd.MSA(by364.data) on the full data set "works" fine. However, with shorter (for reasons of unidimensionality) scales, I continuously run into the error above. Any suggestions? Robert -- View this message in context: http://r.789695.n4.nabble.com/small-scales-in-fwdmsa-tp4493479p4493479.html Sent from the R help mailing list archive at Nabble.com.

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

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 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

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]]

..., loess, or kernel). 'ask': interactive turning over of plots can be switched off (only useful if automated figure export is in effect, e.g., when using Sweave). - simulation module for 0-1 response matrices: sim.rasch() for Rasch homogeneous data, sim.2pl() for 2-PL data, sim.xdim() for unidimensionality violation, and sim.locdep() for locally dependent item responses. - package vignette "eRmvig" added. Best, Patrick _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

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

..., loess, or kernel). 'ask': interactive turning over of plots can be switched off (only useful if automated figure export is in effect, e.g., when using Sweave). - simulation module for 0-1 response matrices: sim.rasch() for Rasch homogeneous data, sim.2pl() for 2-PL data, sim.xdim() for unidimensionality violation, and sim.locdep() for locally dependent item responses. - package vignette "eRmvig" added. Best, Patrick _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages

...items in 43 individuals with possible scores 0,1,2,3. Attempting either a PCM or RSM model fit simply hangs - no error messages but no return to command prompt and a continuously spinning "work in progress" indicator. The scores are from an instrument whose relevant psychometrics (e.g unidimensionality) have been established in other clinical populations: I'm studying a new disease group. I strongly suspect the problem relates to undesirable properties of the dataset: a significant missing data rate (about 20%) and a rather uneven distribution amongst the possible scores with relative paucity...

...de: * The new functions person.fit() and item.fit() compute p-values for person- and item-fit statistics for IRT models for dichotomous data. The `simulate.p.value' argument enables the computation of p-values based on a Monte Carlo procedure. * The new function unidimTest() checks the unidimensionality assumption for dichotomous data IRT models, using a Modified Parallel Analysis. * The new function testEquatingData() prepares data-sets for test equating by common items. In particular, two types of common item equating are included: alternate form equating (where common and unique items a...

...de: * The new functions person.fit() and item.fit() compute p-values for person- and item-fit statistics for IRT models for dichotomous data. The `simulate.p.value' argument enables the computation of p-values based on a Monte Carlo procedure. * The new function unidimTest() checks the unidimensionality assumption for dichotomous data IRT models, using a Modified Parallel Analysis. * The new function testEquatingData() prepares data-sets for test equating by common items. In particular, two types of common item equating are included: alternate form equating (where common and unique items a...

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

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

Hola Isidro: También había sopesado esa posibilidad pero por una parte me parecía "complicar" el proceso y por otra tengo mis dudas acerca de que en que el análisis cluster pueda prescindir de la hipótesis de Normalidad en las dos variables "raras" que tengo. De cualquier forma muchas gracias por tu idea. Un saludo {In Archive} RE: [R-es] Clasificacion de