similar to: new package MLCM: Maximum Likelihood Conjoint Measurement

We announce the release of package MPDiR version 0.1-11 which contains data sets and functions to support the forthcoming book "Modeling Psychophysical Data in R" by K. Knoblauch and L. T. Maloney, Use R! Vol 32, Springer (expected publication date: September 2012). The package includes several data sets from published psychophysical experiments using detection and rating scale measures

Hi, I'm raising the issue, again, of those pesky thin lines that show up with a pdf graphics device, otherwise known as FAQ 7.36. I have read the FAQ and understand that I can eliminate them by adjusting the anti-aliasing option for viewing them on the screen, but this doesn't necessarily eliminate them for a print device or for transferring them into a powerpoint or similar type of

Peter Thank you for the information. I accidentally deleted Ken's post without having read it. Ken' s thought is great but as you said awful to implement I thought that capture.output would come in handy some time when I first saw it on an unrelated reply. Just thought :- the latex listings package may have alternatives If I remember correctly it has wrapping and other goodies but I

I've started to use the parallel package and it works very well speeding things up. Thank you for making this easy to do. Should I have expected that parCapply would return a vector when parApply returns a matrix? library(parallel) x <- matrix(rnorm(8), nc = 2) apply(x, 2, function(y) y) [,1] [,2] [1,] -0.9649685 0.91339851 [2,] -1.4313140 0.13457671 [3,] 1.0499248

Hello everyone, I am new to this list and the R-Project, so I hope my question is not trivial or has been answered before. I searched the FAQs and the mailing list archives and I could not find anything about Conjoint Analysis. I am especially interested in Choice-based Conjoint, resp. discrete choice models. Is there a function / module that handles this issue? Or can the multinomial logit

Hi, I'm trying to use an offset with glm. According to the glm man page offset ... This should be NULL or a numeric vector of length either one or equal to the number of cases. ... but with the following example, I get an error if the offset is of length 1 c1 <- structure(list(Contr = c(0.028, 0.043, 0.064, 0.097, 0.146, 0.219 + ), Correct = c(34L, 57L, 94L, 152L, 160L, 160L),

Hi, I'm getting an error that I don't understand when updating an nls object. Here is a toy example. dd <- structure(list(Contrast = c(0.00376, 0.03759, 0.12782, 0.25564, 0.50376, 1), Response = c(0.29915, 6.13248, 29.01709, 30.0641, 29.46581, 27.67094)), .Names = c("Contrast", "Response"), class = "data.frame", row.names = c(NA, -6L)) m1 <-

Hi, In the Sweave output for summary for several types of model objects and also for the comparison of models with anova, I find that that the display of the call(s) or formula does not obey the width option, even with keep.source=TRUE set, so that a long formula will overshoot the margins in the document. I would like to know if there is a good way to correct that. Looking at the print.summary

I'm having a problem with dotted lines (lty = 3) in the pdf output in documents generated with Sweave. In the displayed pdf, the dotted line does not show up and in the printed output, it is there but does not seem to respect the lwd argument, for example, it is very faint despite using lwd = 3. The dotted line is correct in a quartz window and if I save the Quartz window to a pdf and

Must packages in the Depends field of the DESCRIPTION file have NAMESPACES? I haven't seen this explicitly indicated anywhere. I am writing a small package and find that when I add the abind package to the list of the Depends field, I get an error in R CMD check of the build. * checking package name space information ... OK * checking package dependencies ... ERROR Packages required but not

We are in the process of analyzing data from a discrete-choice conjoint survey and are looking for a consultant who can help us assess willingness to pay (WTP). We are currently able to assess overall WTP using the coefficients from our conjoint analysis but would like assistance in determining if significant differences in WTP exist across a number of subgroups via a segmented analysis or some

Hi, I have run into another problem using offsets, this time with the predict function, where there seems to be a contradiction again between the behavior and the help page. On the man page for predict.lm, it says Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be. While it indicates nothings about

Hi, I'm trying to redefine the contrasts for a linear model. With a 2 level factor, x, with levels A and B, a two level factor outputs A and B - A from an lm fit, say lm(y ~ x). I would like to set the contrasts so that the coefficients output are -0.5 (A + B) and B - A, but I can't get the sign correct for the first coefficient (Intercept). Here is a toy example, set.seed(12161952) y

Hi, I would have thought that these two constructions would produce the same result but they do not. Resp <- rbinom(10, 1, 0.5) Stim <- rep(0:1, 5) mm <- model.matrix(~ Stim) Xb <- mm %*% c(0, 1) ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) pnorm(as.vector(Xb), lower.tail = Resp, log.p = TRUE) > ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) [1] -0.6931472 -1.8410216

New packages psyphy and MLDS are available on CRAN: psyphy ncludes an assortment of functions useful in analyzing data from pyschophysical experiments. It includes functions for calculating d' from several different experimental designs, links for mafc to be used with the binomial family in glm (and possibly other contexts) and selfStart functions for estimating gamma values

New packages psyphy and MLDS are available on CRAN: psyphy ncludes an assortment of functions useful in analyzing data from pyschophysical experiments. It includes functions for calculating d' from several different experimental designs, links for mafc to be used with the binomial family in glm (and possibly other contexts) and selfStart functions for estimating gamma values

Hello all, I am trying to apply a conjoint analysis in order to determine the best profile that captures the most preferred combination of levels of given categorical factors. For this a set of factors is given and initially a fractional factorial design has to be produced as a subset of all possible factor levels combinations, sufficient to estimate the main effects utilities. Then the

L.S. We would like to generate fractional design for a conjoint analysis. For example we would like to reduce 2x2x2x2x3 --> 12-16 profiles. We tried the R-package "conjoint" and we found an article which describes how to make fractional design for these kinds of studies with Algdesign: http://tolstoy.newcastle.edu.au/R/e10/help/att-8876/DCE_with_R.pdf However, the generated

Pls advise how I can use R in conjoint analysis?? regds Faisal Afzal Siddiqui Karachi, Pakistan ____________________________________________________________________________________ Looking for last minute shopping deals? ----- TO GET MORE DETAILS CLICK HERE -- View this message in context: http://r.789695.n4.nabble.com/Conjoint-Analysis-in-R-tp4651650.html Sent from the R help

Dear Group I was referring to a conjoint analysis scenario using R from the paper referred below: Agricultural Information Research 17(2),2008,86-94 available online at www.jstage.jst.go.jp/ This paper describes the data modelling of a conjoint study design based on conditional logit procedure. I understand that Heirarchical Bayes is asymptotically equivalent to Conditionallogit. However it