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)
Anne, predict cannot replace the data set properly, because you have specified fixed values in
g <- lm(p1$y ~ p1$x1 + p1$x2)
Instead, use:
g <- lm(y ~ x1 + x2, data = p1)
and all the stuff will work.
Please insert some blanks in your code to make it readable, and try to give reproducable examples by, e.g., setting the seed as in:
set.seed(123) rm(list=ls()) x1 <- rnorm(193) x2 <- runif(193, -5, 5) y <- rnorm(193) + x1 + x2 p1 <- data.frame(y = y, x1 = x1, x2 = x2)
newdf <- data.frame(x1 = seq(min(p1$x1), max(p1$x1), length = 10),
x2 = rep(median(p1$x2), 10))g <- lm(y ~ x1 + x2, data = p1)
pr1 <- predict(g, se.fit = TRUE) pr2 <- predict(g, newdata = newdf, se.fit = TRUE)
Uwe Ligges
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
Thanks and a good week end! (I for one need it) Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: [EMAIL PROTECTED] --------------------------------------------------- M-TD Modelling and Technology Development PSE-C CH-1015 Lausanne Switzerland Tel: +41 21 693 83 98 Fax: +41 21 646 41 33
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