Anne wrote:
Hi all,
I still have problems with the predict function by setting up the values on which I want to predict
ie: original df: p1 (193 obs) variates y x1 x2
rm(list=ls()) x1<-rnorm(193) x2<-runif(193,-5,5) y<-rnorm(193)+x1+x2 p1<-as.data.frame(cbind(y,x1,x2)) p1 y x1 x2 1 -0.6056448 -0.1113607 -0.5859728 2 -4.2841793 -1.0432688 -3.3116807 ...... 192 -1.3228239 1.0263013 -2.7801324 193 1.8736683 1.0480632 0.4746959
newdf<-data.frame(x1= seq(min( p1$x1),max( p1$x1),length=10), x2=rep(median( p1$x2),10) ) pr<-predict(g<-lm(p1$y~p1$x1+p1$x2) ,newdf, se.fit = TRUE)
newdf x1 x2 1 -2.3844149 -0.2594991 2 -1.8388635 -0.2594991 ... 9 1.9799963 -0.2594991 10 2.5255477 -0.2594991
pr$fit 1 -0.6766906 2 -4.4198864 ..... 192 -1.6531906 193 1.6395442
so apparently the predict() function did not take up the new data.frame
I looked up with conflicts() to see if I had masked objects in the search path potentially causing this problem but found none
Hi Anne,
predict is working properly (though not as you expected). It's not evaluating your newdf because it has no columns called p1$x1 or p1$x2. Try this instead:
pr <- predict(g <- lm(y ~ x1 + x2, p1), newdf, se.fit = TRUE)
> str(pr) List of 4 $ fit : Named num [1:10] -2.365 -1.865 -1.366 -0.867 -0.367 ... ..- attr(*, "names")= chr [1:10] "1" "2" "3" "4" ... $ se.fit : Named num [1:10] 0.1751 0.1424 0.1120 0.0868 0.0723 ... ..- attr(*, "names")= chr [1:10] "1" "2" "3" "4" ... $ df : int 190 $ residual.scale: num 0.987
--sundar
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