search for: predprobs

Dear R-Help listers, I am trying to estimate an proportional odds logistic regression model (or ordered logistic regression) and then make predictions by supplying a hypothetical x vector. However, somehow this does not work. I guess I must have missed something here. I first used the polr function in the MASS package, and I create a data frame and supply it to the predict function (see below):

Dear All, I am using the function vuong from pscl package to compare 2 non nested models NB1 (negative binomial I ) and Zero-inflated model. NB1 <-  glm(, , family = quasipoisson), it is an object of class: "glm" "lm" zinb <- zeroinfl( dist = "negbin") is an object of class: "zeroinfl"   when applying vuong function I get the following: vuong(NB1,

Dear Friends, My problem is related to how to measure probabilities from a probit model by changing one independent variable keeping the others constant. A simple toy example is like this Range for my variables is defined as follows y=0 or 1, x1 = -10 to 10, x2=-40 to 100, x3 = -5 to 5 Model output <- glim(y ~ x1+x2+x3 -1, family=binomial(link="probit")) outcoef <-

Dear All, I am using the function vuong of the package pscl to compare 2 non nested glm models with a numeric response. I did the following m1<-glm(y ~x ,data=xxx) m2<-glm(y ~z , data=xxx) When calling the vuong function I get the following message: > vuong(m1,m2) Error in predprob.glm(m1) : your object of class glm is unsupported by predprob.glmyour object of class lm is unsupported

Dear All, I am using the function vuong of the package pscl to compare 2 non nested glm models with a numeric response. I did the following m1<-glm(y ~x ,data=xxx) m2<-glm(y ~z , data=xxx) When calling the vuong function I get the following message: > vuong(m1,m2) Error in predprob.glm(m1) : your object of class glm is unsupported by predprob.glmyour object of class lm is unsupported

Hello, Frank was nice enough to point me to the val.prob function of the Design library. It creates a beautiful graph that really helps me visualize how well my model is predicting probabilities. By default, there are two lines on the graph 1) fitted logistic calibration curve 2) nonparametric fit using lowess Right now, the nonparametric line doesn't look very good. The

Good Day All, I have a negative binomial model which I have developed using the MASS library. I now would like to develop some predictions from it. Running the predict.glm (stats library) using type="response" gives me a non-integer value which was rather puzzling. I would like to confirm that this is actually the mean predicted value of the probability mass function as opposed

...) plot(x, y, pch = 16, col = "darkblue", main = expression(paste("Scatter diagram of ", italic(y[t]), "against ", italic(x[t]))), xlab = expression(italic(x[t])), ylab = expression(italic(y[t]))) model<-glm(y ~ x, family = binomial) predProbs<-predict(model,data.frame(x=seq(min(x), max(x), length.out=100)), type="response") lines(seq(min(x), max(x), length.out=100), predProbs, col=2, lwd=2) Note also that it is not a good idea to name "t" a R object, since the name is reserved for a special function. Stefano...

Hello ; I am doing a regression of count data (number of award and there are some covariates) I have estiamted the parameters of negative binomial distribuion (lambda is a function of covaraites, GLM model) by glm.nb function and training dataset. Now I want to predict the number of award (for example y=0, y=1, y=2,) or testing dataset. I dont know how to calculate this numbers? I would be very