I am trying to automate running through different model subsets in
lme4 (version 0.999375-37) using glmer and storing the model output as
a list. ?It runs through the sequence fine but the problem I am having
is the Formula stored in each list element has the formula object,
"form," I created rather than the specific formula. I have looked
through several lists and can not really see where I am going wrong.
I am not sure if this is something specific to glmer or something more
basic that I am missing. Thank you for any help or direction you can
provide.
Here is an example:
set.seed(2147483647)
dat<-data.frame(event=rpois(15,
1.5),mort=c(0,0,0,0,1,1,0,0,1,0,1,0,1,0,1),X1=rnorm(15),X2=rnorm(15),X3=rnorm(15),X4=rnorm(15))
cand.vars<-data.frame(cbind(c('X1','X2'),c('X2','X4'),c('X3','X1')))
Cand.models<-list()
for(i in 1:ncol(cand.vars)){
?? ?form<-as.formula(paste("mort~",paste(cand.vars[,i],collapse
='+'),"+(1|event)"))
?? ?Cand.models[[i]]<-glmer(form,family = binomial, data = dat)
}
print(Cand.models)
# output
[[1]]
Generalized linear mixed model fit by the Laplace approximation
Formula: form
?? Data: dat
?? AIC ? BIC logLik deviance
?26.29 29.12 -9.143 ? ?18.29
Random effects:
?Groups Name ? ? ? ?Variance Std.Dev.
?event ?(Intercept) ?0 ? ? ? ?0
Number of obs: 15, groups: event, 6
Fixed effects:
?? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
(Intercept) ?-0.5155 ? ? 0.5770 ?-0.893 ? ?0.372
X1 ? ? ? ? ? -0.8158 ? ? 0.6354 ?-1.284 ? ?0.199
X2 ? ? ? ? ? -0.1028 ? ? 0.5714 ?-0.180 ? ?0.857
Correlation of Fixed Effects:
?? (Intr) X1
X1 ?0.194
X2 -0.047 ?0.279
[[2]]
Generalized linear mixed model fit by the Laplace approximation
Formula: form
?? Data: dat
?? AIC ? BIC logLik deviance
?25.76 28.59 -8.878 ? ?17.76
Random effects:
?Groups Name ? ? ? ?Variance ?Std.Dev.
?event ?(Intercept) 3.898e-13 6.2434e-07
Number of obs: 15, groups: event, 6
Fixed effects:
?? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
(Intercept) ?-0.4947 ? ? 0.5828 ?-0.849 ? ?0.396
X2 ? ? ? ? ? ?0.1890 ? ? 0.5898 ? 0.320 ? ?0.749
X4 ? ? ? ? ? -1.0695 ? ? 0.8021 ?-1.333 ? ?0.182
Correlation of Fixed Effects:
?? (Intr) X2
X2 -0.175
X4 ?0.089 -0.167
[[3]]
Generalized linear mixed model fit by the Laplace approximation
Formula: form
?? Data: dat
??AIC ? BIC logLik deviance
?26.3 29.13 ?-9.15 ? ? 18.3
Random effects:
?Groups Name ? ? ? ?Variance Std.Dev.
?event ?(Intercept) ?0 ? ? ? ?0
Number of obs: 15, groups: event, 6
Fixed effects:
?? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
(Intercept) ?-0.5675 ? ? 0.6611 ?-0.858 ? ?0.391
X3 ? ? ? ? ? ?0.1465 ? ? 1.0263 ? 0.143 ? ?0.886
X1 ? ? ? ? ? -0.7904 ? ? 0.6103 ?-1.295 ? ?0.195
Correlation of Fixed Effects:
?? (Intr) X3
X3 -0.497
X1 ?0.202 -0.070
formula objects and glmer
2 messages · Christopher Chizinski, Douglas Bates
On Tue, Feb 8, 2011 at 2:21 PM, Christopher Chizinski <chizi001 at umn.edu> wrote:
I am trying to automate running through different model subsets in lme4 (version 0.999375-37) using glmer and storing the model output as a list. ?It runs through the sequence fine but the problem I am having is the Formula stored in each list element has the formula object, "form," I created rather than the specific formula. ?I have looked through several lists and can not really see where I am going wrong. I am not sure if this is something specific to glmer or something more basic that I am missing. Thank you for any help or direction you can provide.
It is general to formulas. If you want to manipulate a formula it is best to use substitute as in
nm <- "bar" (newform <- substitute(baz ~ foo, list(baz = as.name(nm))))
bar ~ foo Sorry if this response is too cryptic. Right now I have a bus to catch.
Here is an example:
set.seed(2147483647)
dat<-data.frame(event=rpois(15,
1.5),mort=c(0,0,0,0,1,1,0,0,1,0,1,0,1,0,1),X1=rnorm(15),X2=rnorm(15),X3=rnorm(15),X4=rnorm(15))
cand.vars<-data.frame(cbind(c('X1','X2'),c('X2','X4'),c('X3','X1')))
Cand.models<-list()
for(i in 1:ncol(cand.vars)){
?? ?form<-as.formula(paste("mort~",paste(cand.vars[,i],collapse
='+'),"+(1|event)"))
?? ?Cand.models[[i]]<-glmer(form,family = binomial, data = dat)
}
print(Cand.models)
# output
[[1]]
Generalized linear mixed model fit by the Laplace approximation
Formula: form
?? Data: dat
?? AIC ? BIC logLik deviance
?26.29 29.12 -9.143 ? ?18.29
Random effects:
?Groups Name ? ? ? ?Variance Std.Dev.
?event ?(Intercept) ?0 ? ? ? ?0
Number of obs: 15, groups: event, 6
Fixed effects:
?? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
(Intercept) ?-0.5155 ? ? 0.5770 ?-0.893 ? ?0.372
X1 ? ? ? ? ? -0.8158 ? ? 0.6354 ?-1.284 ? ?0.199
X2 ? ? ? ? ? -0.1028 ? ? 0.5714 ?-0.180 ? ?0.857
Correlation of Fixed Effects:
?? (Intr) X1
X1 ?0.194
X2 -0.047 ?0.279
[[2]]
Generalized linear mixed model fit by the Laplace approximation
Formula: form
?? Data: dat
?? AIC ? BIC logLik deviance
?25.76 28.59 -8.878 ? ?17.76
Random effects:
?Groups Name ? ? ? ?Variance ?Std.Dev.
?event ?(Intercept) 3.898e-13 6.2434e-07
Number of obs: 15, groups: event, 6
Fixed effects:
?? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
(Intercept) ?-0.4947 ? ? 0.5828 ?-0.849 ? ?0.396
X2 ? ? ? ? ? ?0.1890 ? ? 0.5898 ? 0.320 ? ?0.749
X4 ? ? ? ? ? -1.0695 ? ? 0.8021 ?-1.333 ? ?0.182
Correlation of Fixed Effects:
?? (Intr) X2
X2 -0.175
X4 ?0.089 -0.167
[[3]]
Generalized linear mixed model fit by the Laplace approximation
Formula: form
?? Data: dat
??AIC ? BIC logLik deviance
?26.3 29.13 ?-9.15 ? ? 18.3
Random effects:
?Groups Name ? ? ? ?Variance Std.Dev.
?event ?(Intercept) ?0 ? ? ? ?0
Number of obs: 15, groups: event, 6
Fixed effects:
?? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
(Intercept) ?-0.5675 ? ? 0.6611 ?-0.858 ? ?0.391
X3 ? ? ? ? ? ?0.1465 ? ? 1.0263 ? 0.143 ? ?0.886
X1 ? ? ? ? ? -0.7904 ? ? 0.6103 ?-1.295 ? ?0.195
Correlation of Fixed Effects:
?? (Intr) X3
X3 -0.497
X1 ?0.202 -0.070
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