similar to: OT : sensible analysis of censored rank data

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'm using the fwdmsa package to identify deviant cases in a Mokken scale analysis. I've run into a problem. When I use scales comprising a few items, iI tend to get an error: Error in y[order(res[-msamp])][1:(length(samp) + 1 - length(msamp))] : only 0's may be mixed with negative subscripts I understand that the error is triggered when the algorithm is fetching cases to enter

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

In an Sweave slide, I want to use sem::read.moments() and sem::specify.model(), which work by using scan() to read the following lines, up to the first blank line. However, Sweave throws an error: > Sweave("sem-thurstone.Rnw") Writing to file sem-thurstone.tex Processing code chunks ... 1 : term hide (label=arrests-setup) 2 : echo term hide (label=thurstone-data) Error:

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, I hope some one can help. I need to compute Thurston's case 5 on a large set of data. I have gotten as far as computing the proportional preference matrix but the next math is beyond me. Here us my matrix 0.500 0.472 0.486 0.587 0.366 0.483 0.496 0.434 0.528 0.500 0.708 0.578 0.633 0.554 0.395 0.620 0.514 0.292 0.500 0.370 0.557 0.580 0.615 0.329 0.413 0.422 0.630 0.500 0.783 0.641 0.731

Hi all, I caught a passing reference to PMML on the WEKA list (I think) .. an XML way of communicating 'data mining models'. I am of the opinion that XML is a 'good thing' and I would for instance like to see a standard XML way of communicating the content and context of datasets (eg of timeseries) but it has been a long day and my internet connection is slow and so I have

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

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

I'm trying to learn about the tcltk package and its uses. Floundering around a bit ... Have discovered Peter Dalgaard's articles in R-News, which should help. Also James Wettenhall's suite of examples look like they might be enlightening, even though the indications are that they are Windoze oriented. Be that as it were, I decided to fool around a bit with Tcl/Tk *outside* of R to

Hi all, I am a biostatistician and I have developed my own ranking system for clinical data. I would like to test the efficiency of it w.r.t. to other ranking systems. I would like to simulate the data and after assigning ranks to my observed scores(after neglecting dropouts), observe the type I error. If I want to do a Kruuskal Wallis type of test, what test statistic should I use to test for a

Hi All, I want to give ranks to elements in a column so I used: total_list$field1.rank <- rank(total_list$field1,ties.method="min") But this gives me the rank in increasing order. How do I get the ranks in decreasing order? I know decreasing = FALSE is not a legal argument here. Thanks. Jiong The email message (and any attachments) is for the sole use of the intended recipient(s)

Hi, I've kind of a tricky question, which I don't know how to solve yet: I get multiple dataframes loaded (readRDS) in a loop function. Each loaded dataframe contains two columns one with a var-name and one with a value. The rownumber (order) is very important as it is a value of the rank (1:x). A example with a similar looped structure: df1 <-

SAS proc rank has ties options of high and low that would allow producing ranks of the type found in the sports pages, e.g., rank (c(1,1,2,2,2,2,3)) == 1 1 3 3 3 3 7 Could R support these ties.methods?

>From the following: basin.map <- readAsciiGrid("c:/temp/area.asc", colname="area") I have a SpatialGridDataFrame which has the x and y cordinate of a cell, and the drainage area of that cell. There are many cells with a low drainage area (in my case, 33000 with an area of 37.16) and one cell with the highest drainage area (again, in my case, a drainage area of of

Hello, What is the best way to get ranks for a vector of values, limit the range of rank values and create equal count in each group? I call this uniform ranking...uniform count/number in each group. Here is an example using three groups: Say I have values: x = c(3, 2, -3, 1, 0, 5, 10, 30, -1, 4) names(x) = letters[1:10] > x a b c d e f g h i j 3 2 -3 1 0 5 10 30 -1 4 I