similar to: longitudinal tobit regression in R

Hello R-Users: I am want to use tobit regression for left censored panel/longitudinal data. Could you please provide me the name of "library" and/or "package" that will give me option of fitting tobit regression model for longitudinal data? Thank you. Sattar __________________________________________________ [[alternative HTML version deleted]]

Hi everyone I have the following situation: In a longitudinal study, subjects fill out a questionnaire every year (repeated measurements over time). Also, the subjects are nested within departments. There is an intervention going on over time. The outcome variable is continuous. Now I'd like to analyse two things: 1. Is there a significant change over time? I think this is done by a

Dear R-user: I have a left censored longitudinally measured data set with 4 variables such as sub (which is id), x (only covariate), y (repeatedly measured response) and w (weights) (note, ?-5? indicates the left censored value in the attached data set). I am using following R codes (?survival? library and ?survreg? package) for fitting a random effect tobit model for the left censored

Dear R users: I am using survreg for modeling left censored longitudinal data. When I am using the following code for fitting the tobit model I am getting some output with an warning message(highlighted with red color): > survreg(Surv(y, y>=0, type='left')~x + frailty(id), cytokine.data, weight=w, dist='gaussian', scale=1) Call: survreg(formula = Surv(y, y >= 0, type

I tried the following comm in Linux(Ubuntu), It works well: >set.seed(43);runif(3):rnorm(3) >set.seed(43);runif(3);rnorm(3) At 2011-03-26 19:00:05£¬r-sig-debian-request@r-project.org wrote: >Send R-SIG-Debian mailing list submissions to > r-sig-debian@r-project.org > >To subscribe or unsubscribe via the World Wide Web, visit >

Hello, I am working on a Tobit Model for a consumer good, left censored with zero. Relation: Sales of product depend on price, promotion (dummy), Season(dummy) and store (dummy) Used standard Tobitmodel with package(AER) vor a 48 week period: tobitmodel<-tobit(SALE~Price+Promotion+Season+Store,data= datatobit) Now I want to predict the values for week 49-52. Used predict device:

I have an unbalanced panel of daily, county data that is naturally bounded at zero so my intention is to use a tobit. I'm using tobit from the AER package. There is cyclicality in the data for each pattern that I would like to control for before I add my variables of interest. I run the regression: derp <- tobit(x ~ factor(Month)*factor(County), data = data0, left = 0, right = Inf) If I

I am trying to work out a bootstrapped Tobit regression model. I get the coefficients all right, but they all have standard error zero. And I am unable to figure out why. I know the coefficients are correct because that's what I get when do a Tobit (without bootstrapping). Here's my code: # Bootstrap 95% CI for Tobit regression coefficients? library(boot) library(AER) # for the Affairs

Dear All: I need some guidance in calculating a goodness-of-fit statistic for a Tobit Regression model. To develop the Tobit regression, I used the tobit() method from the AER package, which is basically a simpler interface to the survreg() method. I've read about pseudo R2 and C-index and was wondering if there is a package that calculates this for me. Also, is there a reason to select

I need to give a quick description of Tobit Regression (TR), including how it differs from ordinary least squares (OLS). I am an ecologist who knows just enough about remote sensing and statistics to be dangerous in both. Now I have found myself doing a remote sensing project where I have used TR: survreg(Surv()). As far as I can tell, no form of Censored Regression has been used in analyzing

Hi, How can I recover the variance-covariance matrix of the tobit model from the variance-covariance of the survreg? I first used to the survreg function and then I selected the variance matrix. However, the last parameter is log(scale) and not the variance of the standard deviation of the censored distribution as in the Tobit model. tobit<- survreg(Surv(y, y > 0, type ='left')~

Hi, I'm trying to fit a tobit regression model to some data. When fitting the exact same data in Stata, I have no problems at all, however R won't converge. Its not a maxiters thing, since I've tried increasing this already. I need to be able to fit the model in R since there are users of the code that don't have a Stata license. The code is: require(AER) left = 3.218476 x =

Dear R experts, I am currently working on a rather simple tobit regression, where the dependet variable is left-censored (>0). I would like to apply a Tobit regression and then use the parameters of this regression to make a prediction with new data. The intention behind this is to do an extrapolation. by using the VGAM or AER package, I already succeeded in getting fitted values. However

Folks, Based on http://www.biostat.wustl.edu/archives/html/s-news/1999-06/msg00125.html I thought I should experiment with using survreg() to estimate tobit models. I start by simulating a data frame with 100 observations from a tobit model > x1 <- runif(100) > x2 <- runif(100)*3 > ystar <- 2 + 3*x1 - 4*x2 + rnorm(100)*2 > y <- ystar > censored <- ystar <= 0

I'm statistician I need help with tobit regression Is there assumption in tobit regression ? if any, what kind of that ? please help me !!

Appreciate any suggestions regarding how to fit an unbalanced panel data to a Tobit model using R functions. I am trying to analyze how real estate capital expenditures (CapEx) are affected by market conditions using a panel Tobit model. The CapEx is either positive or 0, so it is censored. The data are unbalanced panel, including the CapEx of about 5000 properties over about 40 quarters, with the

Dear All, we (Ott Toomet and I) would like to add functions for maximum likelihood (ML) estimations of generalized tobit models of type 2 and type 5 (*see below) in my R package for microeconomic analysis "micEcon". So far we have called these functions "tobit2( )" and "tobit5( )". Are these classifications well known? How are these functions called in other

Dear Community, does anybody have an idea on how to estimate a system of three seemingly unrelated regressions, two of which being TOBIT and one OLS? Background: I am currently estimating a translog cost function and two corresponding cost share equations using systemfit and the seemingly unrelated regression ("SUR") specification. However, I consider it more appropriate to estimate

Hi all! Do you know if there is any R function/package that can be used to estimate "tobit" models with panel data (e.g. with random individual effects)? In economics, a "tobit" model is a model with a dependent variable that is left-censored at zero. Hence, it is a special case of a survival model and can be estimated using the "survival" package (see e.g.

I am using survreg from the survival package to run a left censored tobit model on “non-survival” data. I have to four questions that I hope someone can help me with: 1) Is there anything I should take into consideration when using frailty() to estimate random intercepts? 2) Is there anyway of extracting the estimated intercepts produced by survreg when using frailty()? 3) Can someone point