similar to: Clarification on generic functions and methods

I've defined the following for objects of a class called jml summary.jml <- function(object, ...){ tab <- cbind(Estimate = coef(object), StdError = object$se, Infit = object$Infit, Outfit = object$Outfit) res <- list(call = object$call, coefficients = tab, N = nrow(object$Data), iter = object$Iterations) class(res) <- "summary.jml" res }

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

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

While building a package, I see the following: * checking R code for possible problems ... NOTE cheat.fit: no visible binding for global variable 'Zobs' plot.jml: no visible binding for global variable 'Var1' I see the issue has come up before, but I'm having a hard time discerning how solutions applied elsewhere would apply here. The entire code for both functions is below,

Suppose I have the following function myFun <- function(formula, data){ f <- formula(formula) dat <- model.frame(f, data) dat } Applying it with this sample data yields a new dataframe: qqq <- data.frame(grade = c(3, NA, 3,4,5,5,4,3), score = rnorm(8), idVar = c(1:8)) dat <- myFun(score ~ grade, qqq) However, what I would like is for the resulting dataframe (dat) to include

Hello R, I am looking for non-parametric simulation in IRT. Is there any IRT package that does non-parametric simulation? helen L [[alternative HTML version deleted]]

I believe this may be more related to analysis than it is to R, per se. Suppose I have the following function that I wish to integrate: ff <- function(x) pnorm((x - m)/sigma) * dnorm(x, observed, sigma) Then, given the parameters: mu <- 300 sigma <- 50 m <- 250 target <- 200 sigma_i <- 50 I can use the function integrate as: > integrate(ff, lower= -Inf, upper=target)

I recently moved a function 'subset.with.warning' into the 'mvbutils' package (a version not yet on CRAN). When I tried RCMD CHECK, I got this warning: * checking S3 generic/method consistency ... WARNING subset: function(x, ...) subset.with.warning: function(x, cond, mess.head, mess.cond, row.info, sub) See section 'Generic functions and methods' of the

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I have the following function for which I need to find the root of a: f <- function(R,a,c,q) sum((1 - (1-R)^a)^(1/a)) - c * q To give context for the problem, this is a psychometric issue where R is a vector denoting the percentage of students scoring correct on test item i in class j, c is the proportion correct on the test by student k, and q is the number of items on the test in total. I

I'd like to suggest that whenever there is no S3 method implementation available for a particular class, that the error message would also report the class structure of the object dispatched on. Example: foo <- function(...) UseMethod("foo") foo.ClassA <- function(object, ...) { cat("foo() for ClassA called.\n") } > foo(structure(1, class="ClassA"))

Greetings, all... Hoping someone can point me in the right direction. I've set up a Linux PXE host with a menu choice to install Windows. This option boots a WinPE image, which then uses \\net use... to mount the appropriate ISO image. Works fine. However, I would like to pass the iso name and directory into WinPE from the menu via append= arguments. Thus, I can set up a separate menu option

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

Hi, I have the following function that I want to apply to a list of 14 matrices (1536 x 170) of binary data: DRes <- function(x, nr = 10000, metric = "mixed", ...) { require(analogue) require(ade4) m <- c() for (i in 1:nr) { set.seed(i) x1 <- x[, sample(dimnames(x)[[2]], length(x[1,])/2)] x2 <- x[, !dimnames(x)[[2]] %in% dimnames(x1)[[2]]] d1 <-

Dear all, Let's say I have the following: # Loading the boot package # install.packages(boot) library(boot) # Generating data set.seed(123) x <- rnorm(100) # Bootstrap for the sample mean bmean <- boot(x, function(x,d) mean(x[d]), R=1000) bmean # #ORDINARY NONPARAMETRIC BOOTSTRAP # # #Call: #boot(data = x, statistic = function(x, d) mean(x[d]), R = 1000) # # #Bootstrap Statistics : #

Hello: How can I work around the conflict between the S3 and S4 illustrated in the example below? I'm developing a package that requires a function in 'stats4', but when 'stats4' is attached, it breaks my AIC function. I could give my AIC function another name so it no longer uses the generic dispatch, but I wonder if there is another way. Thanks,

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

Full_Name: Michael Jacob Version: 1.4.1 OS: Windows 2000 SP2 Submission from: (NULL) (195.27.237.226) Hi, there seems to be a bug in julian(): > Sys.getlocale() [1] "LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=C;LC_NUMERIC=C;LC_TIME=English_United States.1252" > julian(Sys.time()) Error in fromchar(x) : character string is not in a

Dear all, Could anyone help me figure out why bootstrap works for one of similar models but not for the other and how I can solve it? I am using R 2.11.1 in Windows and would like to get confidence intervals for my models A and B by bootstrapping. However, bootstrap gives expected output for the model A but not for B, which I found was puzzling because the structure of the models is