I want to do model selection,here is null model and full model.
p5.lgm.null<-lmer(Y~1+(1|p/aa),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1))
summary(p5.lgm.null)

p5.lgm.full <- lmer(Y 
~as*ms*cur+(1|p/aa),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1))
summary(p5.lgm.full)

I do like this.
 > p5.lgm.bwd <- stepAIC(p5.lgm.full, direction="backward")
Error in terms.default(object) : no terms component
 > summary(p5.lgm.bwd)
Error: object "p5.lgm.bwd" not found
Error in summary(p5.lgm.bwd) : error in evaluating the argument 'object' in 
selecting a method for function 'summary'
 >

it seems stepAIC doesn't work for lmer model.
Is this true?

Aimin Yan


 > p5.lgm.9 <- lmer(Y 
~p*aa*index*x*y*z*sdx*sdy*sdz*delta*as*ms*cur+(1|p/aa),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1))
Error: cannot allocate vector of size 1565600 Kb
In addition: Warning messages:
1: Reached total allocation of 494Mb: see help(memory.size)
2: Reached total allocation of 494Mb: see help(memory.size)


At 12:45 PM 11/21/2006, you wrote:
>On 11/21/06, Aimin Yan <aiminy@iastate.edu> wrote:
>>thanks, Here is data under this link with file name as p_5_angle.csv
>>http://www.public.iastate.edu/~aiminy/data/
>>p is protein names(5 proteins)
>>aa are nested in p(up to 19 levels for each p, some p doesn't have 19 levels)
>>index is position of aa.
>>there are only one observation for each position of each aa within p.
>>
>>consider p as random effect,
>>since aa is nested in p, so aa is also random effect.
>>
>>p and aa are qualitative predictors.
>>x,y,z,sdx,sdy,sdz,delta,as,ms,cur are quantitative predictors.
>>sc is binary responsible variable(>=90 and <90)
>>
>>we want to know the effect of p,aa,x,y,z,sdx,sdy,sdz,delta,as,ms,cur) on
>>P(sc>=90).
>>
>>So I consider to use logistic regression model with p and aa as random 
>>effect.
>>
>>Firstly I try to use p,aa,x,y,z,sdx,sdy,sdz,delta,as,ms,cur as predictors,
>>but it seems it has too many predictors.
>>so I use p,aa,as,ms,cur as predictors, but it still doesn't work.
>
>Here are the fits for two of your models using lmer.
>
>>library(lme4)
>Loading required package: Matrix
>Loading required package: lattice
>>p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_angle.csv")
>>p5$Y <- p5$sc >= 90
>>(mp5.NULL <- lmer(Y ~ 1|p/aa, p5, binomial, control = list(usePQL = FALSE)))
>Generalized linear mixed model fit using Laplace
>Formula: Y ~ 1 | p/aa
>   Data: p5
>Family: binomial(logit link)
>  AIC  BIC logLik deviance
>1390 1405   -692     1384
>Random effects:
>Groups Name        Variance Std.Dev.
>aa:p   (Intercept) 0.447654 0.66907
>p      (Intercept) 0.015078 0.12279
>number of obs: 1030, groups: aa:p, 92; p, 5
>
>Estimated scale (compare to  1 )  0.9736361
>
>Fixed effects:
>            Estimate Std. Error z value Pr(>|z|)
>(Intercept)  -0.1325     0.1151  -1.151     0.25
>>(mp5.full <- lmer(Y ~ as*ms*cur + (1|p/aa), p5, binomial, control = 
>>list(usePQL = FALSE)))
>Generalized linear mixed model fit using Laplace
>Formula: Y ~ as * ms * cur + (1 | p/aa)
>   Data: p5
>Family: binomial(logit link)
>  AIC  BIC logLik deviance
>1278 1327 -628.8     1258
>Random effects:
>Groups Name        Variance Std.Dev.
>aa:p   (Intercept) 0.085104 0.29173
>p      (Intercept) 0.026769 0.16361
>number of obs: 1030, groups: aa:p, 92; p, 5
>
>Estimated scale (compare to  1 )  0.9833564
>
>Fixed effects:
>              Estimate Std. Error z value Pr(>|z|)
>(Intercept)  5.506e-01  1.714e-01   3.213  0.00131 **
>as          -3.964e-02  2.322e-02  -1.707  0.08778 .
>ms           1.879e-02  2.149e-02   0.874  0.38206
>cur          3.413e-01  6.706e-01   0.509  0.61078
>as:ms        1.091e-04  7.615e-05   1.432  0.15201
>as:cur       8.315e-02  7.069e-02   1.176  0.23951
>ms:cur      -4.880e-02  5.372e-02  -0.908  0.36366
>as:ms:cur   -3.998e-04  6.602e-04  -0.606  0.54476
>---
>Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
>Correlation of Fixed Effects:
>          (Intr) as     ms     cur    as:ms  as:cur ms:cur
>as        -0.028
>ms        -0.144 -0.960
>cur       -0.391  0.070  0.019
>as:ms      0.290 -0.655  0.443 -0.155
>as:cur    -0.094  0.401 -0.288  0.211 -0.530
>ms:cur     0.118  0.601 -0.672 -0.473 -0.185 -0.263
>as:ms:cur  0.110 -0.354  0.232  0.001  0.614 -0.874  0.036


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