search for: final3

...,20) X<-rnorm(20) Y<-rnorm(20) Z<-rnorm(20) data3<-data.frame(firm3,year3,industry3,X,Y,Z) data3 colnames(data3)<-c("firm","year","industry","X","Y","Z") final1<-rbind(data1,data2) final2<-rbind(final1,data3) final2 final3<-final2[order(final2$industry,final2$year),] final3 I need to estimate a linear model Y = b0 + b1X + b2Z by industry and year, to obtain the estimates of b0, b1 and b2 by industry and year (for example I need to have de b0 for industry 20 and year 2000, for industry 20 and year 2001...). Then...

...765,389,23456,2367,3892,6438,24824, 23,2897) data3<-data.frame(firm3,year3,industry3,dummy3,dimension3) data3 colnames(data3)<-c("firm","year","industry","dummy","dimension") final1<-rbind(data1,data2) final2<-rbind(final1,data3) final2 final3<-final2[order(final2$year,final2$industry,final2$dimension),] final3 Thank you very much, Cecília Carmo Universidade de Aveiro - Portugal [[alternative HTML version deleted]]

Hi, Try this: final3New<-read.table(file="real_data_cecilia.txt",sep="\t") dim(final3New) #[1] 5369??? 5 #Inside the split within split, dummy==1 for the first row.? For lists that have many rows, I selected the row with dummy==0 (from the rest) using the #condition that the absolute difference...

Dear R community, I am working with huge arrays, so I spend a lot of time computing. This is my code: for (x in 1:dim(variable)[1]){ for (y in 1:dim(variable)[2]){ for (z in 1:dim(variable)[3]){ result <- max(variable[x,y,z,]) } } } Is there a more efficient procedure to do this task? Thanks in advance! [[alternative HTML version deleted]]