similar to: Model Fit Statistics in a Logit Model

I'm running a multinomial logit in R using the Zelig packages. According to str(trade962a), my dependent variable is a factor with three levels. When I run the multinomial logit I get an error message. However, when I run 'model=logit' it works fine. any ideas on whats wrong? ## MULTINOMIAL LOGIT anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 +

I'm working on a logistic regression in R with the car package but keep getting the following error message. It's only and warning and not an error, but I'm just not sure how to resolve the issues. glm.fit: algorithm did not converge glm.fit: fitted probabilities numerically 0 or 1 occurred d1 = data.frame(mwin=c(mwin), mbid=c(mbid)) m1 = zelig(mwin ~ mbid, data=d1,

I'm running a rare events logit model in R using the Zelig package and am getting some of the oddest error messages that I can't figure out. y = rnorm(100) x = c(rep("0",1), rep("1",99)) d = data.frame(won=x, bid=y) d mod1 <- zelig(y~x, model="relogit", data=d, tau=1/100, case.correct="prior", bias.correct=TRUE, robust=TRUE) > mod1 <-

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

I'm trying to run a logit model and plot the probability curve for a number of the important predictors. I'm trying to do this with the Effects package. df=data.frame(income=c(5,5,3,3,6,5), won=c(0,0,1,1,1,0), age=c(18,18,23,50,19,39), home=c(0,0,1,0,0,1)) str(df) md1 = glm(factor(won) ~ income + age + home, data=df,

I have a logit model with about 10 predictors and I am trying to plot the probability curve for the model. Y=1 = 1 / 1+e^-z where z=B0 + B1X1 + ... + BnXi If the model had only one predictor, I know to do something like below. mod1 = glm(factor(won) ~ as.numeric(bid), data=mydat, family=binomial(link="logit")) all.x <- expand.grid(won=unique(won), bid=unique(bid)) y.hat.new

I ran three logit models in R with the Zelig package and I'm trying to compute the predicted probabilities for a number of different values on the independent variable. My dep variable was accepted or decline and my indep variable is bid amount, and varies. So for a bid amount of 3, what's the expected probability of winning. For a bid amount of 5, what's the expected probability of

I'm running an ordered logit in R with the Zelig package and am trying to calculate some predicted probabilities. However, I get the following error message. > x.low <- setx(mod, cars=1)Error in dta[complete.cases(mf), names(dta) %in% vars, drop = FALSE] : incorrect number of dimensions I googled this problem and couldn't find anything, minus a question by me on this same

I'm trying to calculate predicted probabilities in R with Zelig and keep getting the following error. Can anyone help? > x.low <- setx(mod, type=1)Error in dta[complete.cases(mf), names(dta) %in% vars, drop = FALSE] : incorrect number of dimensions When I ran the model, I ran everything but the explanatory variable as a numeric variable. Now, I'm trying everything and no

When I use zelig to compute parameter estimates with "model = logit.survey", I receive the following error: Nicholas Carnes. 2007. "logt.surveyWarning message: In eval(expr, envir, enclose) : non-integer #successes in a binomial glm! I believe this is because the model is not using "quasibinomial" (see R-News 2003, Analyzing Survey Data in R, by Thomas Lumley). Is there

Hi all, I have a dataset with a response variable with three categories (1, 2, 3) and a lot of continuous variables. I'd like to make a MLR with these variables. I've been watching the libraries nnet and zelig for this purpose but I don't understand them well. I use a training sample data to make the MLR. train.set <- sample(1:1000,1000*0.7) I have done this: library(nnet) net

Hello everyone I appologize for my lack of experience in statistical methods. I am an R user begginer and I am running a logit model using "zelig" and "pcse" packages. I will go to the point and is that Im having problems with interpreting the results of my models.. It is really simple (I guess for the most advanced scholars) however I really dont understand how to interpret

Let's say I have a variable, day, which is saved as a factor with 7 levels, and I use it in a logistic regression model. I ran the model using the car package in R and printed out the results. mod1 = glm(factor(status1) ~ factor(day), data=mydat, family=binomial(link="logit")) print(summary(mod1)) The result I get is: Coefficients: Estimate Std. Error z value

Greetings all, please consider the following data: #Build Data frame Slope<-c(1.291370, 12.208500, 2.110930, 0.578990, 5.019520, 0.807444, 0.554079 , 1.257080, 0.241504 , 0.184337 , 0.383044 , 0.342021) Exposure<-c(790.54, 1167.79 , 845.58 , 1082.47 , 1189.61 , 677.17 , 2058.56 , 469.09 , 112.02 , 803.31 , 254.14 ,1336.16) FwyDist<-c(11809.4222 ,10623.0458, 12279.6271,

I don't have a logistic regression model and am trying to generate probability curves for all possible combinations of the variables. My logit model has 5+ variables, and I want to draw curves for every scenario. See code below. When home_owner is 0 and 1, I want curves. The same goes for all other variables categories, so that I have permutations for all possible combinations. I've

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance test.  All the tests, except the deviance tests, produced p-values well above 0.05.  Would anyone please

I'm an R newbie and had the following question. I'm trying to run a logit model because I have a dependant variable that is a factor with three levels. Each of my independent variables are factor variables and have two or more levels, which is why I'm unsure about the error. regone <- glm(data96$RV961327 ~ data96$RV960605 + data96$RV960610 + data96$RV960337 + data96$RV960385 +

I want to construct a logit model, plot the probability curve with the confidence intervals, and then I want to print out a data frame with the predictor, response value, predicted value, the low ci predicted value, and the high ci predicted value. So it should look something like: value low_ci prob hi_ci 5 0.10 0.12 0.13 6 0.11 0.13 0.16 7 0.13 0.15

I am using the effects package to construct some probability graphs showing the predicted probabilities from a logistic regression model. However, I get an odd error message and don't know what the issue is. When I attempt to generate the plots, I get the following error: dat$won_ping = as.factor(dat$won_ping) mod2 = glm(won_ping ~ our_bid + age_of_oldest_driver2 + credit_type2 +

I'm running a linear model in R using the car package. I have a variable education, which i have recoded and regrouped to my wishes. However, R seems to place each element of that variable in alphabetical order. When I am running the model, don't I need the model order from lowest to highest to make an inference that a one unit change in one variable produced a one unit change in