You need to spend some time reading about multiple regression. In statistics there is always what is possible and what is advisable. I'm not going to address whether a regression of 57 independent variables is advisable, only possible. For your data, it is not possible. The attached data contain only 13 observations so the maximum number of independent variables you can use is 13. Consider the following example:
example <- data.frame(y=rnorm(3), x1=rnorm(3), x2=rnorm(3), x3=rnorm(3)) lm(y~x1 + x2, example) lm(y~x1 + x2 + x3, example) We create four variables using random normal numbers for 3 cases (rows). The first regression (2 independent variables "works" (i.e. there are no NA's). The second produces an NA for the third independent variable. In my example, the three random variables are not correlated with one another. In your data there must be correlations among the 57 variables so that you are only getting slope values for 11. ---------------------------------------------- David L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77843-4352 -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of R DF Sent: Monday, February 13, 2012 9:19 AM To: r-help@r-project.org Subject: [R] multi-regression with more than 50 independent variables Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get "NA" in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached my dataset as well as R codes below: mlr.data<- read.table("./multiple.txt",header=T) mlr.output<- lm(formula = CaV ~ SHG + TrD+ CrH+ SPAD+ FlN+ FrN+ YT+ LA+ LDMP+ B+Cu+ Zn+ Mn + Fe+ K + P+ N +Clay30 +Silt30 +Sand30 +Clay60 +Silt60 +Sand60 +ESP30 +NaEx30+ CEC30+Cl30+ SAR30 +KSol30+ NaSol30 +CaMgSol3 +ZnAv30 +FeAv30 +OC30 +PAv30 +KAv30 +TNV30+ pH30+ EC30 +SP30 +ESP60 +NaEx60 +CEC60 +Cl60 +SAR60 +KSol60 +NaSol60 +CaMgSol6 +ZnAv60+FeAv60 +OC60 +PAv60 +KAv60 +TNV60 +pH60 + EC60 +SP60, data=mlr.data) summary (mlr.output) Regards, Reza ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.