similar to: a new way to crash R? (PR#8981)

Dear all, I have a very simple set of data and I would like to analyze them with probit analysis. dose event trial 0.0 3 15 1.1 4 15 1.3 4 15 2.0 3 15 2.2 5 15 2.8 4 15 3.7 5 15 3.9 9 15 4.4 8 15 4.8 11 15 5.9 12 15 6.8 13 15 The dose should be transformed with log10(). I use glm(y ~ log10(dose), family=binomial(link=probit)) to do probit analysis, however, I have to exclude the

Hi, I have used the heckit function in micEcon. Now I would like to evaluate the fit of the probit part of the model but when I enter AIC(sk$probit) I get this error Error in logLik(object) : no applicable method for "logLik" How can I then get the AIC for this model? Side question: If you know - from the top of your head - some link to readings dealing with evaluating the

hi list! i'm plotting a probit plot .On x axis i have value of a statistical variable. on y axis the corresponding normalized representation. I have this code plot(vals,perc,axes=F,col="red",pch=19,cex=0.25) probit.scale.values <- c(0,0.001,0.01,0.05,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95,0.99,0.999,1) probit.scale.at <- qnorm(probit.scale.values)

Hi, I'm trying to make a new S4 object with a slot for a "link-glm" object. R doesn't like me have a slot of class "link-glm" > class(make.link("probit")) [1] "link-glm" > setClass("a",representation(item="link-glm")) [1] "a" Warning message: undefined slot classes in definition of "a": item(class

Hello All, I'm trying to run a multinomial probit on a dataset with 28 data points and five levels (0,1,2,3,4) in the latent choice involving response variable. I downloaded the latest mnp package to run the regression. It starts the calculation and then crashes the rpogram. I wish I could give the error message but it literally shuts down R without a warning. I'm using the R

Dear R helpers, I need to estimate a probit model with box constraints placed on several of the model parameters. I have the following two questions: 1) How are the standard errors calclulated in glm (family=binomial(link="probit")? I ran a typical probit model using the glm probit link and the nlminb function with my own coding of the loglikehood, separately. As nlminb does not

Not real data. It was gererated randomly. The original codes are the following: par(mfrow=c(2,1)) n <- 500 ######################### #DATA GENERATING PROCESS# ######################### x1 <- rnorm(n,0,1) x2 <- rchisq(n,df=3,ncp=0)-3 sigma <- 1 u1 <- rnorm(n,0,sigma) ylatent1 <-x1+x2+u1 y1 <- (ylatent1 >=0) # create the binary indicator ####################### #THE

R users, I got a problem to analyze with probit model. What package contains the algorithm to do probit model. Lawrence N.M Kazembe Mathematical Sciences Department Chancellor College University of Malawi P.O. Box 280 Zomba Malawi Tel: (265) 524 222 ext 284 Fax: (265) 524 046 e-mail: lkazembe at chirunga.sdnp.org.mw url: kazembe.cjb.net kazembe.tsx.org

Hi, I am a stata user trying to transition to R. Typically I compute marginal effects plots for (example) probit models by drawing simulated betas by using the coefficient/standard error estimates after I run a probit model. I then use these simulated betas to compute first difference marginal effects. My question is, can I do this in R? Specifically, I was wondering if anyone knows how R

Hi there Looking at the archives I didn't find a solution to the following problem we have here: Printing from our linux-server (wagner) to an intel printserver (PS652D8F) doesn't work. Here's the stuff we know/tried: wagner:~ # smbclient -L //PS652D8F -N added interface ip=10.0.0.10 bcast=10.0.0.255 nmask=255.255.255.0 Got a positive name query response from 10.0.0.40 ( 10.0.0.40

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

Hello all, I have a very simple set of data and I would like to analyze them with probit analysis. The data are: X Event Trial 100 8 8 75 8 8 50 6 8 25 4 8 10 2 8 0 0 8 I want to estimate the value of X that will give a 95% hit rate (Event/Trial) and the corresponding 95% CI. Anyone can offer some help? Thanks!! -

Hi! I was trying to implement a probit model on a dichotomous outcome variable and found that the predictions were outside the (0,1) interval that one should get. I later tried it with some simulated data with a similar result. Here is a toy program I wrote and I cant figure why I should be getting such odd predictions. x1<-rnorm(1000) x2<-rnorm(1000) x3<-rnorm(1000)

Hello, I am getting some results from my Probit estimation in R that are in the opposite direction of what I hypothesized. In sas, the default is probability that y=0 (instead of 1) so one needs to type the word "descending" to get P(y=1). Is the same true for R? Is the default set to P(0)? Thank you in advance. Nita Umashankar [[alternative HTML version deleted]]

If I wanted to fit a logit model and account for clustering of observations, I would do something like: library(Design) f <- lrm(Y1 ~ X1 + X2, x=TRUE, y=TRUE, data=d) g <- robcov(f, d$st.year) What would I do if I wanted to do the same thing with a probit model? ?robcov says the input model must come from the Design package, but the Design package appears not to do probit? Thanks very

Hi to all of you, I'm fitting an full factorial probit model from an experiment, and I've the independent variables as factors. The model is as follows: fit16<-glm(Sube ~ as.factor(CE)*as.factor(CEBO)*as.factor(Luz), family=binomial(link="probit"), data=experimento) but, when I took a look to the results I've obtained the following: glm(formula = Sube ~ CE * CEBO *

> I make a study in health econometrics and have a categorical > dependent variable (take value 1-5). I would like to fit an ordered > probit or ordered logit but i didn't find a command or package who > make that. Does anyone know if it's exists ? R is very fancy. You won't get mundane things like ordered probit off the shelf. (I will be very happy if someone will show

Hi - I am fitting a probit model using glm(), and the deviance and residual degrees of freedom are different depending on whether I use a binary response vector of length 80 or a two-column matrix response (10 rows) with the number of success and failures in each column. I would think that these would be just two different ways of specifying the same model, but this does not appear to be the case.

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

Dear All, I have been struggling to understand why for the housing data in MASS library R and stata give coef. estimates that are really different. I also tried to come up with many many examples myself (see below, of course I did not have the set.seed command included) and all of my `random' examples seem to give verry similar output. For the housing data, I have changed the data into numeric