similar to: Marginal Effects for Beta Regression

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

Dear all, I'd like to plot the marginal effect of a variable in a multiplicative interaction regression, that is, the effect of a variable conditional on the values of another variable. As an illustration, given model lm1 lm1 <- lm(y ~ x*z) I'd like to get the effects of x on y conditional on the values of z, with the corresponding confidence intervals if possible. Does anyone know

Dear all, I am trying to get an estimate of uncertainty surrounding a single predicted value from a beta regression model (this is similar to a logistic glm - in that it involves a link function and linear predictor - but it uses the beta distribution rather than discrete binomial). For example: library(betareg) data("GasolineYield")

Dear useRs, version 2.2-2 of the "betareg" package has just been released on CRAN http://CRAN.R-project.org/package=betareg accompanied by an article in the Journal of Statistical Software http://www.jstatsoft.org/v34/i02/ The package provides beta regression for data in the unit interval (0, 1) such as rates and proportions. The manuscript replicates several practical

Dear useRs, version 2.2-2 of the "betareg" package has just been released on CRAN http://CRAN.R-project.org/package=betareg accompanied by an article in the Journal of Statistical Software http://www.jstatsoft.org/v34/i02/ The package provides beta regression for data in the unit interval (0, 1) such as rates and proportions. The manuscript replicates several practical

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 list, My question to follow is not a pure R question but contains also a more general statistical/econometrical part, but I was hoping that perhaps someone knowledgable on this list could offer some help. I have estimated a binary logistic regression model and would like to calculate average marginal effects for certain predictors of interest. The average marginal effect for a continuous

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

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

I have a question regarding calculating marginal effects (change in the probability due to a one unit change in x) for a probit or for that matter any discrete choice model. Does R have a function that will essentially do this: dE[y|x] ------- = f(x'beta)*beta dx Stata has a command called "mfx" that calculates the marginal effects at the mean (of the x

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

Hi! I've been looking for a way to calculate std errors of marginal effects when I use interaction terms, but with no success. I pretty much have two cases: continuous variable * continuous variable, and continuous variable * binary variable. In both cases, I know how to calculate the marginal effects, even with simulation. But I still can't figure out how to calculate the std errors of

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: >

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 all, I am modeling the incidence of recreational anglers along a stretch of coastline, and with a vary large proportion of zeros (>80%) have chosen to use a zero inflated negative binomial (ZINB) distribution. I am using the same variables for both parts of the model, can anyone help me with R code to compute overall marginal effects of each variable? My model is specified as follows:

Dear All, I carry out negative binomial estimations using the glm.nb command from the MASS package. Is there a command or a simple procedure for computing marginal effects from a glm.nb fitted object? If these are the same as for a Poisson fitted object (glm), my question remains how to compute them. Thanks in advance for your help. Roberto Patuelli ******************** Roberto Patuelli, Ph.D.

Dear forum, we have a 2 dimensional normal distribution of (x, y) which is consistent with a linear regression modell of type y = ax + b. It has a heteroscedastic variance. Is there a straight and simple way with R to get the variance of the marginal distribution W(y|x=xi) where xi may be randomly chosen? Maybe a single line command? Thank you best regards -- View this message in context:

This request is related to the following post from last year: https://stat.ethz.ch/pipermail/r-help/2011-June/279752.html After reading the thread, the idea is still not clear. I have fitted a model using HURDLE from the PSCL package. I am trying to get marginal effects / slopes by multiplying the coefficients by the mean of the marginal effects (I think this is right). To my understanding, this

Hi, I am running an ordered logistic regression model with an interaction, using the polr command. I am trying to find a way to calculate the marginal effects and their significance in R. Does anybody have any suggestion? Thank you! Enrico [[alternative HTML version deleted]]

Buenas tardes, Estoy interesado en ajustar modelos de regresion beta {betareg} a un conjunto de datos en el que se tienen una variable respuesta "y" y ~600K variables independientes. En el codigo en R que se encuentra en la parte inferior presento un ejemplo en el que se tienen 500 variables independientes y la misma respuesta "y" para todos. Tambien se encuentran algunos