similar to: for help about R--probit

I see that "case weights" can be optioned in multinom(). I wanted to make sure I understand what weights= is expecting. My weights (not really mine but I'm stuck with them) are noninteger, are not scaled to sum to the sample size, and larger weights are intended to increase influence. The description of various types of weights is a perennial confusion for me; sorry. STS

I'm trying to do the following: For each ordered pair of a data frame (D1) containing longitudes and latitudes and unique point IDs, calculate the distance to every point in another data frame (D2) also containing longitudes, latitudes and point IDs, and return to a new variable in D1 the point ID of the nearest element of D2. Dramatis personae (mostly self-explanatory): D1$long

R-users, (forgive my return adres) I've been breaking my head why R---which i find fabulous, by the way---does not pipe interactive output through a pager (more or less), like it does with help(), or like GNU Octave does with arrays with more than terminal height rows. Maybe it is my installation (Debian/GNU Linux). Maybe it is my configuration, but i don't think so because with

Dear R users, I am happy to announce the initial release of the "mvProbit" package on CRAN (version 0.1-0). This package provides tools for econometric analysis with Multivariate Probit Models. While these models can be estimated also by several other statistical software packages (e.g. LIMDEP/NLOGIT, STATA), "mvProbit" is much more flexible and powerful in calculating

Dear R users, I am happy to announce the initial release of the "mvProbit" package on CRAN (version 0.1-0). This package provides tools for econometric analysis with Multivariate Probit Models. While these models can be estimated also by several other statistical software packages (e.g. LIMDEP/NLOGIT, STATA), "mvProbit" is much more flexible and powerful in calculating

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

Dear All, Anyone know if there is a package that fits Generalized Linear Models(GAM) to data with ordered dependent variable(response) ? Simon Wood's mgcv has probit, logit,... other links, however, I could not find a way to do GAM *ordered *probit. Yee's VGAM claims to fit ordinal proportional odds model(cumulative logit model) (see: http://www.stat.auckland.ac.nz/~yee/VGAM/) but I

Dear all, I am using glm function in order to estimate a logit model i.e. glm(Y ~ data[,2] + data[,3], family = binomial(link = "logit")). I also created a function that estimates logit model and I would like it to compare it with the glm function. So, does anyone know what optimizer or optimization method glm uses in order to derive the result? Thank you Dimitris -- View this

Dear everybody! Are there algorithms for multinominal logit/probit available for R? Is it my fault that I cannot find these in CRAN? Has somebody programmed these? with best wishes Ott Toomet Ott.Toomet at mail.ee -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

I wonder if anyone is familiar with the priprobit software (Sakuma M. 1998. Probit analysis of preference data. Appl Entomol Zool. 33:339?34). I would like to replicate the same analysis and output in R. Of note, I want to determine the analytical sensitivity, defined as 95% cut-off value. j

Hi, I am wondering if anybody has already written a GHK Simulator function for an ordered probit model in R? We have a system of three equations where the variables can take ordered categorial values. Thanks a million, Pensi

Hi everyone, I am running an ordinal probit using the Bayesian MCMCpack and I am getting an error saying "attempt for find suitable starting values failed" Here is my code: > posterior <- MCMCoprobit(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 +x9 + x10 + x11 + x12 +x13 , beta.start=c(-10, 0.05, 0.02, 0.04, 0.98, 0.61, -0.29, 0.91, -0.82, 1.34, 0.68, 0.57, 0.09, 0.5), mcmc=10000)

Is there any library for fitting multinomial probit using either likelihood or the "method of simulated moments" or both. I presume it would be possible to write a family function in VGAM for the multinomial probit, but was hoping that someone has done it already. Thanking you as always. Vumani Dlamini CSO-Swaziland

Is there a function to fit heckman probit model in R ? Sincerly.. Justin BEM BP 1917 Yaoundé Tél (237) 76043774 [[alternative HTML version deleted]]

Hello, I'm trying to do a probit plot but am a little confused about how to do this. Sample data: > dat <- data.frame(dose=c(0.18,0.48,0.78,0.18,0.48,0.78),n=c(103,120,123,60,110,100),r=c(19,53,83,14,54,81), + sample=c("s","s","s","t","t","t")) > temp <-

Hello, currently I am estimating an ordered probit model with the function polr (MASS package). Is there a simple way to obtain values for the prediction of the index function ($X*\hat{\beta}$)? (E..g. in the GLM function there is the linear.prediction value for this purpose). If not, is there another function / package where this feature is implemented? Thank you very much for

Hello, I want to start testing using the MNP probit function in stead of the lrm function in my current experiment. I have one dependant label and two independent varaibles. The lrm is simple model <- lrm(label ~ val1 + val2) I tried the same thing with the mnp function and got an error that I don't understand model <- mnp(label ~ val1 + val2) I get back an immediate error that

Hello I am working on a panel data, my panel variable is the variable "yearctry", let me explain what I mean, yearctry is calculated based on the year and the ISD phone code of a country, eg, for the year 2000 say and for country USA say (code = 001), my yearctry variable will then be 2000001, there are 2000 observations (ie 2000 individual responses with yearctry = 2000001), I have 65

Howdy everyone I?m trying to get Odds ratio and OR confidence intervals using a probit model, but I'm not getting. Do you think you can help me? I?m new with R L naive = summary(glm(pcr.data[,7]~boldBeta_individual+pcr.data$age,family=binomial(link=probit))) naive_answer = c(naive$coefficients[,1:3]) #naive estimates for

Dear all, I?ll need your help with obtaining robust clustered errors. I use polr command in MASS package m<?porl(y~x1+x2,data=mydata, method=probit). In the rms package, this is as simple as: clusterSE<?robcov(m, mydata$id). Is it possible to do something similar for polr object as well? Thank you very much Best, Faradj [[alternative HTML version deleted]]