search for: mldata

I have a large excel file with data in it. I converted it to a 'csv' format. I imported this dataset to R using the follownig command mldata <- read.csv("c:\\temp\\mldata.csv", header=T) all the column names and the rows seems to be correct. Now that I have this object, I need to perfrom hclust. I used the following hc <- hclust(dist(mldata), method="single") I get the following error > hc <- hc...

On May 24, 2011; 11:06pm Belle wrote: > Does anyone know how to run Multinomial logistical Model in R in order to > get predicted probability? Yes. I could stop there but you shouldn't. The author of the package provides plenty of examples (and two good vignettes) showing you how to do this. Suggest you do some work in that area. Look especially at how model formulas are

...mod > > mydata[1:10,] brand female age 1 1 0 24 2 1 0 26 3 1 0 26 4 1 1 27 5 1 1 27 6 3 1 27 7 1 0 27 8 1 0 27 9 1 1 27 10 1 0 27 > mydata$brand<-as.factor(mydata$brand) > mldata<-mlogit.data(mydata, varying=NULL, choice="brand", shape="wide") > > mldata[1:10,] brand female age 1.1 TRUE 0 24 1.2 FALSE 0 24 1.3 FALSE 0 24 2.1 TRUE 0 26 2.2 FALSE 0 26 2.3 FALSE 0 26 3.1 TRUE 0 26 3.2 FALSE 0...

...s) 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......

...tives assumption and now I would like to run a mixed logit model. It is a "wide" data set with 9 independent (individual) variables and three choices (variable Y). The database is in a cvs file called CAU. This is the code I have run successfully up to now. CAU$Y<- as.factor(CAU$Y) mldata<-mlogit.data(CAU, varying=NULL, choice="Y", shape="wide") mlogit.model<-mlogit(Y~1|Gender+Age+Net+CompT+Hrs+Motivado+Visits+StudyT+HrsVis,data=mldata,reflevel="1") I am having problems running the mixed logit model. I have changed (only) the mlogit command, as...

...et a data problem, for which I can't find any clue from R archive. code below shows my related code all the way to the error #--------------------------------------------------------------------------- mydata <- data.frame(dependent,x,y,z) mydata$dependent<-as.factor(mydata$dependent) mldata<-mlogit.data(mydata, varying=NULL, choice="dependent", shape="wide") summary(mlogit.1<- mlogit(dependent~1|x+y+z, data = mldata, reflevel="0")) "Error in X[omitlines, ] <- NA : subscript out of bounds" , #-------------------------------------------...

...ata.txt", header=T, as.is=T) #"bullsharks" is what I named it bullsharks$Obs bullsharks$Density bullsharks$Visibility bullsharks$Behavior bullsharks$Data #or to view all data bullsharks library(mlogit) bullsharks[1:2,] bullsharks$Density<-as.factor(bullsharks$Density) mldata<-mlogit.data(bullsharks, varying=NULL, choice="Density", shape="wide") mlogit.model <- mlogit(Density~1|Visibility+Behavior, data = mldata, reflevel="1") summary(mlogit.model) However, I get an error at the mlogit.model stage.  Is there something wrong with m...

...individual and for each individual I have *9 questions*. I have a response from *516 individuals*. So it is a panel of 9*516 observations. I have arranged the data in long format (it contains 100 columns indicating different variables and identifiers). In mlogit I tried the following command--- *mldata<- mlogit.data(mydata, shape = "long", alt.var = "Alt_name", choice = "Choice_binary", id.var = "IND")* It is giving me the following error message- Error in 1:nchid : result would be too long a vector Could you please help me with this? I don't think...