On Jan 29, 2011, at 3:49 PM, Joshua Wiley wrote:
On Sat, Jan 29, 2011 at 12:31 PM, David Winsemius
<dwinsem...@comcast.net> wrote:
Huh?. With the same model and data, they should be the same:
I must be missing something, because that is what I would have
expected too, but it is not what I get (at least when I run the code
as shown below).
lm.mod2 <- lm(out ~ scale(xxA)*scale(xxB), data=dat)
newdata2 <- expand.grid(xxA=c(-1,0,1),xxB=c(-1,0,1))
newdata2$preds <- predict(lm.mod, newdata2)
^^^^^ is that
lm.mod2?
UnnHHH . lm.mod2 was the model from Zahn's posting.
lm.mod <- lm(out ~ I(scale(xxA))*I(scale(xxB)), data=dat)
So I guess you need the "I" to get the correct results with the scaled
numbers, but this "works" with the unscaled numbers, although the
numbers (meanA, meanB) are somewhat different, which deserves
investigation. The pair (meanA, meanB) gives the same result but the
others don't seem to get to the same numbers. Which is correct?
> meanA<-mean(dat$xxA); meanB<-mean(dat$xxB); sdA<-sd(dat$xxA);
sdB<-sd(dat$xxB)
> newdata2 <- expand.grid(xxA=c(meanA-sdA,meanA,meanA
+sdA),xxB=c(meanB-sdB,meanB,meanB+sdB))
> newdata2$preds <- predict(lm.mod2, newdata2)
> newdata2
xxA xxB preds
1 9.304820 18.75866 222.1326
2 9.905043 18.75866 234.0992
3 10.505266 18.75866 246.0659
4 9.304820 19.73195 232.7828
5 9.905043 19.73195 244.8696
6 10.505266 19.73195 256.9565
7 9.304820 20.70524 243.4330
8 9.905043 20.70524 255.6400
9 10.505266 20.70524 267.8471
newdata2
xxA xxB preds
1 -1 -1 218.6366
2 0 -1 232.4330
3 1 -1 246.2295
4 -1 0 230.9129
5 0 0 244.8696
6 1 0 258.8263
7 -1 1 243.1892
8 0 1 257.3062
9 1 1 271.4232
Predict(rms.res,xxA=c(-1,0,1),xxB=c(-1,0,1), conf.int=FALSE)
xxA xxB yhat
1 -1 -1 218.6366
2 0 -1 232.4330
3 1 -1 246.2295
4 -1 0 230.9129
5 0 0 244.8696
6 1 0 258.8263
7 -1 1 243.1892
8 0 1 257.3062
9 1 1 271.4232
This is copied directly from my console in a clean session. I get
different values for the predicted values from predict(glm or lm) and
"yhat" from Predict(Glm).
sessionInfo()
R version 2.12.1 (2010-12-16)
Platform: i486-pc-linux-gnu (32-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
#################################
require(rms)
Loading required package: rms
Loading required package: Hmisc
Loading required package: survival
Loading required package: splines
Attaching package: 'Hmisc'
The following object(s) are masked from 'package:survival':
untangle.specials
The following object(s) are masked from 'package:base':
format.pval, round.POSIXt, trunc.POSIXt, units
Attaching package: 'rms'
The following object(s) are masked from 'package:survival':
Surv
set.seed(10)
dat <- data.frame(xxA = rnorm(20, 10), xxB = rnorm(20, 20))
dat$out <- with(dat, xxA+xxB+xxA*xxB+rnorm(20,20))
rms.res <- Glm(out ~ scale(xxA) * scale(xxB), data=dat)
glm.mod <- glm(out ~ scale(xxA) * scale(xxB), data=dat)
lm.mod2 <- lm(out ~ scale(xxA)*scale(xxB), data=dat)
newdata2 <- expand.grid(xxA = -1:1, xxB = -1:1)
newdata2$preds1 <- predict(glm.mod, newdata2)
newdata2$preds2 <- predict(lm.mod2, newdata2)
newdata2
xxA xxB preds1 preds2
1 -1 -1 143.3433 143.3433
2 0 -1 131.6110 131.6110
3 1 -1 119.8787 119.8787
4 -1 0 137.1149 137.1149
5 0 0 126.9708 126.9708
6 1 0 116.8267 116.8267
7 -1 1 130.8865 130.8865
8 0 1 122.3306 122.3306
9 1 1 113.7747 113.7747
Predict(rms.res, xxA= -1:1, xxB= -1:1, conf.int=FALSE)
xxA xxB yhat
1 -1 -1 212.2324
2 0 -1 229.6472
3 1 -1 247.0620
4 -1 0 221.7795
5 0 0 240.6413
6 1 0 259.5030
7 -1 1 231.3266
8 0 1 251.6353
9 1 1 271.9441
Response variable (y): X * Beta
##################################
David Winsemius, MD
West Hartford, CT
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