search for: socst

...continuous and a categorical regressor. The numerical examples are coded in Stata, and they give the same result as in R. Hence, if this is a bug in R then it is also a bug in Stata. That seems very unlikely. Here is a simulation in R of the above mentioned Case 2 in Stata: df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) print("Full model") print(model.matrix(~(socst+grp)^2 ,data=df)) print("Example 2.1: drop socst") print(model.matrix(~(socst+grp)^2 -socst ,data=df)) print("Example 2.2: drop grp") print(model.matr...

...cal examples are coded in Stata, and they give the same >> result as in R. Hence, if this is a bug in R then it is also a bug in >> Stata. That seems very unlikely. >> >> Here is a simulation in R of the above mentioned Case 2 in Stata: >> >> df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) >> print("Full model") >> print(model.matrix(~(socst+grp)^2 ,data=df)) >> print("Example 2.1: drop socst") >> print(model.matrix(~(socst+grp)^2 -socst ,data=df)) >> print("...

...e >> >> result as in R. Hence, if this is a bug in R then it is also a bug in >> >> Stata. That seems very unlikely. >> >> >> >> Here is a simulation in R of the above mentioned Case 2 in Stata: >> >> >> >> df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) >> >> print("Full model") >> >> print(model.matrix(~(socst+grp)^2 ,data=df)) >> >> print("Example 2.1: drop socst") >> >> print(model.matrix(~(socst+grp)^2 -so...

...or. > > The numerical examples are coded in Stata, and they give the same > result as in R. Hence, if this is a bug in R then it is also a bug in > Stata. That seems very unlikely. > > Here is a simulation in R of the above mentioned Case 2 in Stata: > > df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) > print("Full model") > print(model.matrix(~(socst+grp)^2 ,data=df)) > print("Example 2.1: drop socst") > print(model.matrix(~(socst+grp)^2 -socst ,data=df)) > print("Example 2.2: drop gr...

...is also a bug >> >> >> in >> >> >> Stata. That seems very unlikely. >> >> >> >> >> >> Here is a simulation in R of the above mentioned Case 2 in Stata: >> >> >> >> >> >> df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) >> >> >> print("Full model") >> >> >> print(model.matrix(~(socst+grp)^2 ,data=df)) >> >> >> print("Example 2.1: drop socst") >> >> >> p...

...a, and they give the same > >> result as in R. Hence, if this is a bug in R then it is also a bug in > >> Stata. That seems very unlikely. > >> > >> Here is a simulation in R of the above mentioned Case 2 in Stata: > >> > >> df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) > >> print("Full model") > >> print(model.matrix(~(socst+grp)^2 ,data=df)) > >> print("Example 2.1: drop socst") > >> print(model.matrix(~(socst+grp)^2 -socst ,data=df)) &...

...t as in R. Hence, if this is a bug in R then it is also a bug in > >> >> Stata. That seems very unlikely. > >> >> > >> >> Here is a simulation in R of the above mentioned Case 2 in Stata: > >> >> > >> >> df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) > >> >> print("Full model") > >> >> print(model.matrix(~(socst+grp)^2 ,data=df)) > >> >> print("Example 2.1: drop socst") > >> >> print(model.matri...

...gt;> >> in > >> >> >> Stata. That seems very unlikely. > >> >> >> > >> >> >> Here is a simulation in R of the above mentioned Case 2 in Stata: > >> >> >> > >> >> >> df <- expand.grid(socst=c(-1:1),grp=c("1","2","3","4")) > >> >> >> print("Full model") > >> >> >> print(model.matrix(~(socst+grp)^2 ,data=df)) > >> >> >> print("Example 2.1: drop socst") > >>...

...://www.ats.ucla.edu/stat/data/hsb2.csv") names(hsb2) set.seed(8296) ctrl <- cforest_unbiased(ntree=500, mtry=2) varlist <- names(hsb2)[3:4] for (h in varlist){ for (k in c(1,0)){ for (i in c(1,2)){ ## Data subset filtered <- subset(hsb2, schtyp == i & female == k, select = c(id:socst)) rank.cf <- cforest(h ~ write + math + science + socst, data = filtered, control = ctrl) print(rank.cf) ## Standard importance values __________________________ imp=varimp(rank.cf, conditional = TRUE) print(imp) ## predict variables _________________________________________ predicted=predict(r...