Le dimanche 08 novembre 2009 à 00:05 +0100, Christian Lerch a écrit :
Dear list members,
I try to simulate an incomplete block design in which every participants
receives 3 out of 4 possible treatment. The outcome in binary.
Assigning a binary outcome to the BIB or PBIB dataset of the package
SASmixed gives the appropriate output.
With the code below, fixed treatment estimates are not given for each of
the 4 possible treatments, instead a kind of summary measure(?) for
'treatment' is given.
block<-rep(1:24,each=3)
treatment<-c(1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4, 2, 4, 3, 3, 1,
2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2, 4, 4, 1, 3, 1, 2, 3, 2, 1, 4, 3, 4,
1, 4, 3, 2, 1, 3, 4, 2, 4, 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2,
4, 4, 1, 3)
outcome<-c(0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0)
data<-data.frame(block,treatment,outcome)
lmer(outcome~treatment +(1|block), family=binomial, data=data)
Is this a problem with the recovery of interblock estimates?
No...
Are there
special rules how incomplete block designs should look like to enable
this recovery?
Neither...
Compare :
library(lme4)
Le chargement a nécessité le package : Matrix
Le chargement a nécessité le package : lattice
summary(lmer(outcome~treatment +(1|block), family=binomial,
+ data=data.frame(block<-rep(1:24,each=3),
+ treatment<-c(1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4, 2, 4,
+ 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2, 4, 4,
+ 1, 3, 1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4,
+ 2, 4, 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2,
+ 4, 4, 1, 3),
+ outcome<-c(0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
0,
+ 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,
0,
+ 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
1,
+ 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,
0))
+ ))
Generalized linear mixed model fit by the Laplace approximation
Formula: outcome ~ treatment + (1 | block)
Data: data.frame(block <- rep(1:24, each = 3), treatment <- c(1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4, 2, 4, 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2, 4, 4, 1, 3, 1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4, 2, 4, 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2, 4, 4, 1, 3), outcome <- c(0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0))
AIC BIC logLik deviance
86.28 93.1 -40.14 80.28
Random effects:
Groups Name Variance Std.Dev.
block (Intercept) 0.60425 0.77734
Number of obs: 72, groups: block, 24
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.27783 0.71767 -1.780 0.075 .
treatment 0.01162 0.25571 0.045 0.964
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
treatment -0.892
with :
summary(lmer(outcome~treatment +(1|block), family=binomial,
+ data=data.frame(block<-rep(1:24,each=3),
+ treatment<-factor(c(1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3,
4,
+ 2, 4, 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3,
1,
+ 3, 2, 4, 4, 1, 3, 1, 2, 3, 2, 1, 4, 3, 4,
1,
+ 4, 3, 2, 1, 3, 4, 2, 4, 3, 3, 1, 2, 4, 2,
1,
+ 1, 4, 2, 2, 3, 1, 3, 2, 4, 4, 1, 3)),
+ outcome<-c(0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
0,
+ 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,
0,
+ 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
1,
+ 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,
0))
+ ))
Generalized linear mixed model fit by the Laplace approximation
Formula: outcome ~ treatment + (1 | block)
Data: data.frame(block <- rep(1:24, each = 3), treatment <- factor(c(1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4, 2, 4, 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2, 4, 4, 1, 3, 1, 2, 3, 2, 1, 4, 3, 4, 1, 4, 3, 2, 1, 3, 4, 2, 4, 3, 3, 1, 2, 4, 2, 1, 1, 4, 2, 2, 3, 1, 3, 2, 4, 4, 1, 3)), outcome <- c(0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0))
AIC BIC logLik deviance
87.33 98.72 -38.67 77.33
Random effects:
Groups Name Variance Std.Dev.
block (Intercept) 0.86138 0.9281
Number of obs: 72, groups: block, 24
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.9246 0.7117 -2.704 0.00684 **
treatment2 1.3910 0.8568 1.624 0.10446
treatment3 0.4527 0.9163 0.494 0.62124
treatment4 0.4526 0.9163 0.494 0.62131
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) trtmn2 trtmn3
treatment2 -0.775
treatment3 -0.721 0.598
treatment4 -0.721 0.598 0.558
In the first case (your original "data"), "treatment" is interpreted as
a numeric (quantitative) variable , and whr lmre estimtes is a logistic
regression coefficient of the outcome n this numeric variable. Probbly
nonsensical, unless you hve reason to thin that your factor is ordered
and should be treated as numeric).
In the second case, "treatment" is a factor, so you get an estimate for
each treatment level except the first, to be interpreted as difference
of means with the first level.
I fell in that trap myself a few times, and took the habit to give evels
to my fctors tht cannot be interpreted as numbers (such as f<-paste("F",
as.character(v))).
Any help is appreciated!
HTH,
Emmanuel Charpentier
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