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$preds <- predict(lm.mod, newdata2)
^^^^^ is that lm.mod2?
>> newdata2
> xxA xxB preds
> 1 -1 -1 218.6366
> 2 0 -1 232.4330
> 3 1 -1 246.2295
> 4 -1 0 230.9129
> 5 0 0 244.8696
> 6 1 0 258.8263
> 7 -1 1 243.1892
> 8 0 1 257.3062
> 9 1 1 271.4232
>
>> Predict(rms.res,xxA=c(-1,0,1),xxB=c(-1,0,1), conf.int=FALSE)
> xxA xxB yhat
> 1 -1 -1 218.6366
> 2 0 -1 232.4330
> 3 1 -1 246.2295
> 4 -1 0 230.9129
> 5 0 0 244.8696
> 6 1 0 258.8263
> 7 -1 1 243.1892
> 8 0 1 257.3062
> 9 1 1 271.4232
This is copied directly from my console in a clean session. I get
different values for the predicted values from predict(glm or lm) and
"yhat" from Predict(Glm).
> sessionInfo()
R version 2.12.1 (2010-12-16)
Platform: i486-pc-linux-gnu (32-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
> #################################
> require(rms)
Loading required package: rms
Loading required package: Hmisc
Loading required package: survival
Loading required package: splines
Attaching package: 'Hmisc'
The following object(s) are masked from 'package:survival':
untangle.specials
The following object(s) are masked from 'package:base':
format.pval, round.POSIXt, trunc.POSIXt, units
Attaching package: 'rms'
The following object(s) are masked from 'package:survival':
Surv
> set.seed(10)
> dat <- data.frame(xxA = rnorm(20, 10), xxB = rnorm(20, 20))
> dat$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
> ##################################
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