search for: abigailclifton

Hi There, I am trying to fit a logit model to some data in a CSV file in R. Here is my code: Prepared_Data = read.csv("Prepared_Data.csv", header=TRUE) Prepared_Data attach(Prepared_Data) lrfit<-glm(C3~A1*B2*D4*E5,family = binomial) anova(lrfit, test="Chisq") write.csv(anova(lrfit, test="Chisq"), file="CWModelA.csv") shell.exec("CWModelA.csv")

Hi there, I have this code: Prepared_Data <- na.omit(read.csv("Prepared_Data.csv", header=TRUE)) pd <- Prepared_Data[,-3] ## data minus response variable lev <- sapply(pd,function(x) length(unique(x))) ## total parameters for n variables par(las=1,bty="l") plot(cumprod(lev),log="y") library(Matrix) m <- sparse.model.matrix(~.^2,data=pd) ncol(m)

Hi there, I am trying to fit a generalised linear model to some loan application and default data. The purpose of this is to eventually work out the probability an applicant will default. However, R seems to crash or die when I run "glm" on anything greater than a 5-way saturated model for my data. My first question: is the best way to fit a generalised linear model in R to fit the

Hi, This is my code (my data is attached): library(languageR) library(rms) library(party) OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls