# Multinomial Logit Model with lots of Dummy Variables

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## Multinomial Logit Model with lots of Dummy Variables

 Hi All, I am attempting to build a Multinomial Logit model with dummy variables of the following form: Dependent Variable : 0-8 Discrete Choices Dummy Variable 1: 965 dummy vars[hidden email][hidden email]Dummy Variable 2: 805 dummy vars The data set I am using has the dummy columns pre-created, so it's a table of 72,381 rows and 1770 columns. The first 965 columns represent the dummy columns for Variable 1 The next 805 columns represent the dummy columns for Variable 2 My code to build the mlogit model looks like the following. I want to know...is there a better way of doing this without these huge equations? (I probably also need a more powerful PC to do all of this). I'll also want to perform a joint test of significance on the first 805 coefficients... Is this possible? Thanks GP [code] #install MLOGIT library(mlogit) #load mydata mydata = 0 mydata<-read.csv(file="G:\\data.csv",head=TRUE) my_data=0 num.rows=length(mydata[,1]) num.cols=965+805+1 my_data=matrix(0,nr=num.rows,nc=num.cols) for(i in 1:num.rows) {         nb=mydata[i,2]         np=mydata[i,3]         my_data[i,nb]=1         my_data[i,965+np]=1         my_data[i,1+1770]=mydata[i,1]         } #convert matrix to data.frame # convert to data frame my_data_frame<-as.data.frame(my_data) #check data frame headers head(my_data_frame) #load dataframe into mldata with choice variable mldata<-mlogit.data(my_data_frame, varying=NULL, choice="V1771", shape="wide") #V1771 = dependent var #V1-V965 = variable 1 dummies #V966-V1700 = variable 2 dummies #regress V1771 against all 1700 variables... mlogit.model<-mlogit(V1771~0|V1+V2+V3...+V1700,data=mldata, reflevel="0") [/code]