similar to: Vignette for MiscPsycho Package

I'm finalizing development of a package that will include a vignette. Without the vignette, the package builds fine with no warnings and is ready for distribution. Now, I am following the directions for developing vignettes "Sweave, Part II: Package Vignettes" by Friedrich Leisch. I am using a windows XP machine (other session info below). Here is what I have done. 1) I add the

Version 1.5 of the MiscPsycho package had been uploaded to CRAN (should hit mirrors in a day or so). This package has a set of functions that may be useful for psychometric applications. The package has been updated to include the following: 1) All functions (where appropriate) now use standard formula arguments 2) All functions now use S3 print and summary methods 3) Help files have been

I have just submitted MiscPsycho to CRAN. MiscPsycho contains functions for miscellaneous psychometrics that may be useful for applied psychometricians. MML estimation already exists in the ltm package. Hence, a jml option is provided for users who prefer this method. The jml function gives back rasch difficulties and the same Infit and Outfit statistics as Winsteps. Also, jml is known to return

I have just submitted MiscPsycho to CRAN. MiscPsycho contains functions for miscellaneous psychometrics that may be useful for applied psychometricians. MML estimation already exists in the ltm package. Hence, a jml option is provided for users who prefer this method. The jml function gives back rasch difficulties and the same Infit and Outfit statistics as Winsteps. Also, jml is known to return

Version 1.1 of the MiscPsycho package had been uploaded to CRAN. The package has been updated to include the following: 1) The irt.ability() function that estimates examinee ability given a set of item parameters. The function is very general and can be used to estimate ability when there are only dichotomous items (1-, 2-, or 3PL), only polytomous items (generalized partial credit model), or a

Version 1.1 of the MiscPsycho package had been uploaded to CRAN. The package has been updated to include the following: 1) The irt.ability() function that estimates examinee ability given a set of item parameters. The function is very general and can be used to estimate ability when there are only dichotomous items (1-, 2-, or 3PL), only polytomous items (generalized partial credit model), or a

A revised version of the MiscPsycho package has been uploaded to CRAN. The fixes include: 1) A bug found in the class.acc() function that resulted in overflow errors when computing probabilities associated with polytomously scored items has been resolved. The function now integrates over the posterior distribution of an IRT model for dichotomously scored items, polytomously scored items, or a

An updated version of the Miscellaneous Psychometrics package has been updated to CRAN. The following updates are included in the package: 1) An implementation of the Stocking-Lord procedure for linking test scales. 2) An implementation of the Levenshtein algorithm for comparing character strings 3) stringProbs, a function for computing the probability of a given Levenshtein Distance 4) Three

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are

FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut some corners compared to identical(): > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE)) Unit: nanoseconds expr min lq mean median uq max neval identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100 isFALSE(FALSE) 713 761 1133.53 809.5 871.5

Quite possibly, and I?ll look into that. Aside from the work I was doing, however, I wonder if there is a way such that sapply could avoid the overhead of having to call the identical function to determine the conditional path. From: William Dunlap [mailto:wdunlap at tibco.com] Sent: Tuesday, March 13, 2018 12:14 PM To: Doran, Harold <HDoran at air.org> Cc: Martin Morgan <martin.morgan

All of my resources for numerical analysis show that the spectral decomposition is A = CBC' Where C are the eigenvectors and B is a diagonal matrix of eigen values. Now, using the eigen function in R # Original matrix aa <- matrix(c(1,-1,-1,1), ncol=2) ss <- eigen(aa) # This results yields back the original matrix according to the formula above ss$vectors %*% diag(ss$values) %*%

You?re right, it sure does. My suggestion causes it to fail when simplify = ?array? From: William Dunlap [mailto:wdunlap at tibco.com] Sent: Tuesday, March 13, 2018 12:11 PM To: Doran, Harold <HDoran at air.org> Cc: r-help at r-project.org Subject: Re: [R] Possible Improvement to sapply Wouldn't that change how simplify='array' is handled? > str(sapply(1:3,

Dear List: I have a question regarding an MDS procedure that I am accustomed to using. I have searched around the archives a bit and the help doc and still need a little assistance. The package isoMDS is what I need to perform the non-metric scaling, but I am working with similarity matrices, not dissimilarities. The question may end up being resolved simply. Here is a bit of substantive

Could your code use vapply instead of sapply? vapply forces you to declare the type and dimensions of FUN's output and stops if any call to FUN does not match the declaration. It can use much less memory and time than sapply because it fills in the output array as it goes instead of calling lapply() and seeing how it could be simplified. Bill Dunlap TIBCO Software wdunlap tibco.com On Tue,

I am glad to help. The pp program is the main tool to use to create the executable. -----Original Message----- From: "Duncan Murdoch" <murdoch at stats.uwo.ca> To: "Greg Snow" <Greg.Snow at intermountainmail.org> Cc: "Gabor Grothendieck" <ggrothendieck at gmail.com>; "Doran, Harold" <HDoran at air.org>; "r-help at

>>>>> Henrik Bengtsson <henrik.bengtsson at gmail.com> >>>>> on Tue, 13 Mar 2018 10:12:55 -0700 writes: > FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut > some corners compared to identical(): > > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE)) > Unit: nanoseconds > expr

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

In the near future I will release MiscPsycho, a package that contains various functions useful for applied psychometricians. I would like to include some data sets for distribution in the package, but have not created any of these on my own, but have used data distributed in other packages such as the LSAT data in the ltm package. Is it appropriate for me to distribute a data set in the package I

Martin In terms of context of the actual problem, sapply is called millions of times because the work involves scoring individual students who took a test. A score for student A is generated and then student B and such and there are millions of students. The psychometric process of scoring students is complex and our code makes use of sapply many times for each student. The toy example used