similar to: question on marginal effects

Dear all, I have two questions that are almost completely related to how to do things in R. I am running an ordinal probit regression analysis in R. The dependent variable has three levels (0=no action; 1=warning; 2=sanction). I use the lrm command in the rms package: print( res1<- lrm(Y ~ x1+x2+x3+x4+x5+x6, y=TRUE, x=TRUE, data=mydata)) I simply couldn't make any sense of the

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

Dear all, I am trying to estimate the marginal effects of a logit regression using the mfx package. It is crucial that the standard errors are clustered at the year level. Hence, the code looks as follows: marginal.t24.2<-logitmfx(stock.market.crash~crash.t24+bubble.t24+RV.t24,data=Data_logitregression_lags, clustervar1 = "year")

Dear R-community, I am currently replicating a study and obtain mostly the same results as the author. At one point, however, I calculate marginal effects that seem to be unrealistically small. I would greatly appreciate if you could have a look at my reasoning and the code below and see if I am mistaken at one point or another. My sample contains 24535 observations, the dependent variable

Hi, I have a problem with the ordered probit model -polr function (library MASS). My independent variables are countinuos. I am not able to understand two main points: a) how to calculate marginal effects b) how to calculate the relative importance of each independent variables If required i will attach my model output. Thanks Franco

Dear Oscar, and any other R-project person, Can you please help me to figure out the meaning of the following error message in red ? Error in eval(predvars, data, env) : numeric 'envir' arg not of length one I computed ordered logit models using 'polr' in R (I just followed the guidance a handout I found on princeton.edu about logit, probit and multinomial logit models) . The

I would like to know more about the output from the terms option in predict(), especially for a glm. And especially when there is an interaction effect being considered. Here's why I ask. These articles were recently brought to my attention. They claim that just about everybody who has reported an interaction coefficient in a logit or probit glm has interpreted it incorrectly. Ai, C. and

Hi, I wonder if there is a function in (some package of) R which computes marginal effects of the variables in a glm, say, for concretness, a probit model. By marginal effects of the covariate x_j I mean d P(y=1 | x), which is approx g(xB)B_j dx_j where g is the pdf of the normal distribution, x is the vector of covariates (at some points, say, the mean values) and B is the estimated

In that case, I can't work out why the first model fails but not the second. I would start looking at "Data" to see what it contains. if: object2 <- polr(Inc ~ Training ,Data,Hess = T,method = "logistic" ) works, the problem may be with the "Adopt" variable. Jim On Tue, Mar 20, 2018 at 10:55 AM, Willy Byamungu <wmulimbi at email.uark.edu> wrote: >

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

Dear R community, I would like to import data saved with Stata and then run a Probit model using R. My data comes from the World Values Surveys and in the Probit model I want to control for countries. So far I figured out that I should put "convert.factors = FALSE" when using stata.get() in order to import numeric values instead of label mappings, which is what I want for most of the

Dear R-Help, I'm hoping to find a Multinomial Nested Logit package in R. It would be great to find something analogous to "PROC MDC" in SAS: > The MDC (Multinomial Discrete Choice) procedure analyzes models > where the > choice set consists of multiple alternatives. This procedure > supports conditional logit, > mixed logit, heteroscedastic extreme value,

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

Dear all: When I am trying to get the marginal effects: summary(result7,adv_inc_ratio=mean(m9201 $adv_inc_ratio),adv_price_ratio=mean(m9201$adv_price_ratio), ...(SOME MORE CONTINUOUS AND DISCRETE VARIABLES BUT I AM NOT LISTING)... regW=c (0,mean(m9201$regW),1), regWM=c(0,mean(m9201$regWM),1)) It gave out an error message: Error in summary.Design(result7, adv_inc_ratio = mean(m9201

Deal all: i want to do the probit with sample selection estimation, the following is my code: probit with sample selection can be done by stata :heckprob The heckprobll is the likelihood function shown in W.H. Greene 5th p714 ¡´ The question is the convergence is very slow compare with Stata using likellihood only. ¡´ Second i did the similar way in matlab using fminsearch , the estimated

Hello, I am learning R and I am fluent in Stata and I try to translate part of my Stata code to R to check the reliability of the data under R. I have a proportion variable as a dependent variable pQSfteHT . Independent variables are dummies for two categorical variables called dQSvacrateHTQuali3 and cluster_3. I am fitting a model with the Stata command below: glm pQSfteHT dQSvacrateHTQuali3_2

I have had some weird results using the optim() function. I wrote a probit likelihood and wanted to run it with optim() with simulated data. I did not include a gradient at first and found that optim() would not even iterate using BFGS and would only occasionally work using SANN. I programmed in the gradient and it iterates fine but the estimates it returns are wrong. The simulated data work

Hi All, This question was brought up some time ago but I never saw a reply so I'd like to bring it up again. When using ROracle package (version 0.5-5), I am unable to run any queries that are greater than 4000 characters in length. If I do, I get the following message: Error in oraPrepareStatement(con, statement, bind=NULL) : RS-DBI driver: (too long a statement -- it must has less than

Hi everybody, I try to calculate and display the marginal effect(s) in a hierarchical model using lmer. Here is my model: m1<- lmer(vote2011~ Catholic + Attendance+ logthreshold + West + Catholicproportion+ (Catholic * Catholicproportion) + (Attendance*Catholicproportion) + Catholicproportion?+ (Catholic *Catholicproportion?)+ (Attendance* Catholicproportion?) + (1 + Attendance+ Catholic