similar to: Weights for polr

Hello, I recently updated to R 2.10 on my 64 bit Dell Precision T7400 running Enterprise Linux and now I can't get the arm library to load when I run R using Wine. Prior to the update I was able to use arm without any trouble. My main goal is to use arm with WinBUGS on this machine, calling it from R for Windows, which is executed with wine. After the update, when I try to load the arm

Dear R-users, I'm trying to recode a variable. After using cut(data.vector,breaks=my.breaks,labels=my.label) I need the data.vector to have the same values as the labels. To make it clear: x<-runif(10) y<-cut(x,breaks=c(0,0.3,0.7,0.9,1),labels=c(3,6,7,9)) as.numeric(y) returns something like a vector 1 3 3 2 2 1 4 2 3 4 . I need something like 3 7 7 6 6 3 7 9 6 7 9 for further use. I

Dear R listers, I am learning the MLE utility optim() in R to program ordered logit models just as an exercise. See below I have three independent variables, x1, x2, and x3. Y is coded as ordinal from 1 to 4. Y is not yet a factor variable here. The ordered logit model satisfies the parallel regression assumption. The following codes can run through, but results were totally different from what I

Hi All, I am working on some ordinal logistic regresssions using LRM in the Design package. My response variable has three categories (1,2,3) and after using the creating my model and using a call to predict some values and I wanted to use a simple .5 cut-off to classify my probabilities into the categories. I had two questions: a) first, I am having trouble directly accessing the

Hi everyone, If I compute a "Ordered Logistic or Probit Regression" with the polr function from MASS package. the summary give me : coefficients, Standard error and Tvalue.. but not directly the p.value. I can compute "manualy" the Pvalue, but Is there a way to directly obtain the pa.value, and I wonder why the p.valeu is not directly calculated, is there a reason? exemple

Hi, I have below fit with ordinal logistic regression dat = foreign::read.dta("https://stats.idre.ucla.edu/stat/data/ologit.dta") summary(MASS::polr(formula = apply ~ pared + public + gpa, data = dat)) However, instead of obtaining unconstrained estimates of model parameters, I would like to impose certain constraints on each of the model parameters, based on some non-sample

Hi, ?? Are there any packages which prints out p-values for OLR's (like `ologit' from Stata)? I want to run a bunch of OLRs and print the p-value for the first coefficient from each of them. ? I checked polr() under MASS and it doesn't. ?There's a lrm() function under Design which does print out p-values but I couldn't extract p-values from the output. ? Thanks, ? Debs

Hi Gregg, Below I try to address 1) The sum constraint would apply for each set ?? and ?? i.e. sum(??) = sum(??) = 1.60 2) Just like 1) the lower and upper bounds will be applied for individual set i.e. individual elements of ?? are subject to lower = c(1, -1, 0) and upper = c(2, 1, 1) and individual elements of ?? are subject to lower = c(1, -1, 0) and upper = c(2, 1, 1) I hope that I am

I want to test whether the proportional odds assumption for an ordered regression is met. The UCLA website points out that there is no mathematical way to test the proportional odds assumption (http://www.ats.ucla.edu/stat//R/dae/ologit.htm), and use graphical inspection ("We were unable to locate a facility in R to perform any of the tests commonly used to test the parallel slopes

Is anyone familiar with a way to account for sampling weights (e.g., in order to cope with selection bias) for individual respondents using the bayesm package (e.g., rhierMnlRwMixture)? In the regular MNL this can easily be done in STATA using the mlogit function with pweights option. However, I am unfamiliar with a way to do it in HB estimation. Any help or hints are appreciated. Best, Klaus

Hi, I'm new to R, and I'm STATA user before, could you help me where I can get document about comparison command between STATA and R. Thank you very much, Best regards, -iip-

Hello Christofer, Just writing with a detailed follow-up. Attached is a script I was able to get running with a bit of work. I did not include the script in the ext of this email. It is only attached. Optimization Progress We were initially aiming to solve the dual-slope constrained ordinal model using nloptr's SLSQP algorithm (NLOPT_LD_SLSQP), since it supports: ? Box constraints (per-?

Hello, all. I wanted to use the "survey" package to analyze data from the National Ambulatory Medical Care Survey, and am having some difficulty translating the analysis keywords from one package (Stata) to the other (R). The data were collected using a multistage probability sampling, and there are variables included to identify the sampling units and weights. Documentation from the

Hi, I am running an analysis with an ordinal outcome and I need to run a test of the parallel regression assumption to determine if ordinal logistic regression is appropriate. I cannot find a function to conduct such a test. >From searching various message boards I have seen a few useRs ask this same question without a definitive answer - and I came across a thread that indicated there is no

I've run an ordered logistic regression model in R with Zelig and am looking to calculate predicted probabilities. Zelig has a series of simple one line commands to generate the information I want on first differences and so forth. Unfortunately, I keep getting an error when running the zelig function and was wondering if there was a quick alternative for generating predicted probabilities for

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,

Hi, I am trying to fit a model with an ordered response variable (3 levels) and 13 predictor variables. The sample has complex survey design and I've used 'svydesign' command from the survey package to specify the sampling design. After reading the manual of 'svyglm' command, I've found that you can fit a logistic regression (binary response variable) by specifying the

hello everybody solved the problem with summary, now I have another one eg I estimate > try.op <- polr( > as.ordered(sod.sit.ec.fam) ~ > log(y) + > log(1 + nfiglimin) + > log(1 + nfiglimagg) + > log(ncomp - nfiglitot) + > eta + > I(eta^2) + >

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