similar to: package relimp

I am trying to use Rcmdr 1.5_4 with R-2.11.1 (order to run to run the new version of misdist-0.5.3 which is built under R-2.11.1). however relimp is required for Rcmdr and the version of relimp_1.0.1 downloaded from CRAN will not work with the latest version of Rcmdr (I get error message telling me to reload it). Is there any way round this problem or will there be a new version relimp that is

Dear All: I am calculating the relative importance of a regressor in a linear model. Does anyone know how I can obtain/install the 'pmvd' computation type? I am a US user. Regards, Y -- View this message in context: http://r.789695.n4.nabble.com/calc-relimp-pmvd-for-US-R-user-tp3808752p3808752.html Sent from the R help mailing list archive at Nabble.com.

Hello! I am trying test my observed data against the null-hypothesis that different items from a psychological questionnaire contribute equally to the metric dependent variable that measures problems (sum score of a questionnaire). That is, I am interested in relative strength of the predictors. Predictor items of the questionnaire are on a scale from 0-3, and technically ordinal, although most

Dears, Unfortunatelly, the packages relaimpo and relimp do not seem to work with plm function (plm package) or gls function (in nlm package). I've been studying on how to adapt one of them for this pourpose. In that sense, I have two questions regarding to this work: 1) have anyone hard of any workaround for those incompatibilities, or at least of any ideas on that - especially for plm? 2)

R-Experts: Does anyone know if there are R functions to perform the Freeman-Tukey double arcsine transformation and then backtransform it? Thanks, Brant Inman Mayo Clinic

Hi STATISTICA has a function called "Two-way joining" (see http://www.statsoft.com/TEXTBOOK/stcluan.html#twotwo) and the reference material states that this is based on the method as published by Hartigan (found this paper: http://www.jstor.org/pss/2284710 through wikipedia). What is the relationship (if any) between the "heatmap" function in R and this technique? Is there an

Hello everyone, I will like to know if there is an R package to compute exact confidence intervals for the ratio of two binomial proportions. Tony. [[alternative HTML version deleted]]

I found this paper on ANOVA on unequal error variance. Has this be incorporated to any R package? Is there any textbook that discuss the problem of ANOVA on unequal error variance in general? http://www.jstor.org/stable/2532947?cookieSet=1

I've noticed that the implementation of Kendall's Tau in R is O(N^2). The following reference describes how it can be done in O(N log N): A Computer Method for Calculating Kendall's Tau with Ungrouped Data William R. Knight Journal of the American Statistical Association, Vol. 61, No. 314, Part 1 (Jun., 1966), pp. 436-439 http://www.jstor.org/pss/2282833 I'm interested in

Hello, While doing some enrichment tests using chisq.test() with simulated p-values, I noticed some strange behaviour. The computed p-value was extremely small, so I decided to dig a little deeper and debug chisq.test(). I noticed then that the simulated statistics returned by the following call tmp <- .Call(C_chisq_sim, sr, sc, B, E) were all the same, very small numbers. This, at first,

Hallo everyone, excuse me if this is not a genuine R question but I do not know where to ask else. Referring to e.g. https://stat.ethz.ch/pipermail/r-help/2004-December/062114.html I wonder if these measurements of 3000 criminals (raw data) are available anywhere. At least I didn't find them in the R datasets package or by means of Google. What I did find was a table of frequencies of

It is not about "really arge total number of observations", but: set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) A11 0 1 2 166483 666853 166664 There are three possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being most common. Cheers, Jari

Given this example #start code a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) wilcox.test(a, b, paired=FALSE) #sum of rank for first sample sum.rank.a <-

Hi all, Is there any code to run fixed effects Tobit models in the style of Honore (1992) in R? (The original Honore article is here: http://www.jstor.org/sici?sici=0012-9682%28199205%2960%3A3%3C533%3ATLALSE%3E2.0.CO%3B2-2) Cheers David [[alternative HTML version deleted]]

I'm not sure if this is the right mailing list. My apologies if I'm sending the question to the wrong mailing list. (The r-packages list moderator suggested using the R-help list.) My question is about the "chickwts" data in the datasets package. According to help(chickwts), the source of the data is ?Anonymous (1948) Biometrika, 35, 214. But this reference seems irrelevant.

Greetings, I'm working on some analyses where I need to calculate wilcox tests for paired samples. In my current literature search I've found a few papers on sample size determination for the wilcox test notably: Sample Size Determination for Some Common Nonparametric Tests Gottfried E. Noether Journal of the American Statistical Association

Dear all, I would like to run a simple regression model y~x1+x2+x3+... The problem is that I have a lot of independent variables (xi) -- around one hundred -- and that some of them are categorical with a lot of categories (like, for example, ZIP code). One straightforward way would be to (a) transform all categorical variables into 1/0 dummies and (b) enter all the variables into an lm model.

Hi everybody! I'm new to this list and also to the R program. I'd like to know if there is a function able to convert results into Fractional form like my scientific calculator have. For example: > 1/3 [1] 0.3333333 > function_that_return_a_fraction_from_numbers(0.3333333) [1] 1/3 Thanks Mauro -- Man, he is constantly growing and when he is bound by a set pattern of ideas

Hi, I am trying to replicate Lambert (1992)'s simulation with zero-inflated Poisson models. The citation is here: @article{lambert1992zero, Author = {Lambert, D.}, Journal = {Technometrics}, Pages = {1--14}, Publisher = {JSTOR}, Title = {Zero-inflated {P}oisson regression, with an application to defects in manufacturing}, Year = {1992}} Specifically I am trying to recreate Table 2. But my

Dear R users, I'm wondering if the gls function reports pseudo R. I do not see it by summary(). If the package does not report, can I calculate it in this way? Adjusted pseudo R squared = 1 - [(Loglik(beta) - k ) / Loglik(null)] where k is the number of IVs. Thanks! Gary [[alternative HTML version deleted]]