Dear R-help-list,
I have a problem in which the explanatory variables are categorical,
the response variable is a proportion, and experiment contains
technical replicates (pseudoreplicates) as well as biological
replicated. I am new to both generalized linear models and mixed-
effects models and would greatly appreciate the advice of experienced
analysts in this matter.
I analyzed the data in 4 ways and want to know which is the best way.
The 4 ways are:
1. A generalized linear model with binomial error in which the
positive and negative counts for each biological replicate is summed
over technical replicates.
2. Same as 1 with a quasibinomial error model.
3. A generalized linear mixed-effects model with binomial error in
which technical replication is treated as a random effect.
4. A generalized linear mixed-effects model with binomial error in
which technical replication is treated as a random effect and
overdispersion is taken into account by individual level variability.
Here are the relevant data for each model:
For everything:
> sessionInfo()
R version 2.13.0 (2011-04-13)
Platform: i386-apple-darwin9.8.0/i386 (32-bit)
locale:
[1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lme4_0.999375-39 Matrix_0.999375-50 lattice_0.19-23
loaded via a namespace (and not attached):
[1] grid_2.13.0 nlme_3.1-100 stats4_2.13.0 tools_2.13.0
treatment Apositivesum Bnegativesum
1 A 208 439
2 A 215 395
3 A 235 411
4 A 304 450
5 A 215 395
6 B 224 353
7 B 279 405
8 B 265 418
9 B 278 392
10 B 249 383
11 C 196 385
12 C NA NA
13 C 266 397
14 C 216 460
15 C 264 419
16 D 283 401
17 D NA NA
18 D 270 410
19 D 248 316
20 D 302 386
1. A generalized linear model with binomial error in which the
positive and negative counts for each biological replicate is summed
over technical replicates.
> y<-cbind(Apositivesum , Bnegativesum)
> model<-glm(y ~ treatment, binomial)
> summary(model)
Call:
glm(formula = y ~ treatment, family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3134 -0.5712 -0.3288 0.8616 2.4352
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.574195 0.036443 -15.756 < 2e-16 ***
treatmentB 0.164364 0.051116 3.216 0.00130 **
treatmentC 0.007025 0.054696 0.128 0.89780
treatmentD 0.258135 0.053811 4.797 1.61e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 60.467 on 17 degrees of freedom
Residual deviance: 28.711 on 14 degrees of freedom
(2 observations deleted due to missingness)
AIC: 160.35
Number of Fisher Scoring iterations: 3
Since Residual deviance >> degrees of freedom I tried
2. Same as 1 with a quasibinomial error model.
> model<-glm(y ~ treatment, quasibinomial)
> summary(model)
Call:
glm(formula = y ~ treatment, family = quasibinomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3134 -0.5712 -0.3288 0.8616 2.4352
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.574195 0.052173 -11.006 2.82e-08 ***
treatmentB 0.164364 0.073180 2.246 0.04136 *
treatmentC 0.007025 0.078306 0.090 0.92978
treatmentD 0.258135 0.077038 3.351 0.00476 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 2.049598)
Null deviance: 60.467 on 17 degrees of freedom
Residual deviance: 28.711 on 14 degrees of freedom
(2 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 3
> anova(model,test=F)
Error in match.arg(test) : 'arg' must be NULL or a character vector
> anova(model,test="F")
Analysis of Deviance Table
Model: quasibinomial, link: logit
Response: y
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev F Pr(>F)
NULL 17 60.467
treatment 3 31.756 14 28.711 5.1646 0.01303 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
I then tried to take the effect of pseudoreplication into account with
3:
3. A generalized linear mixed-effects model with binomial error in
which technical replication is treated as a random effect.
Here is the input file:
treatment mouse observation positive negative
A 1 1 73 149
A 1 2 50 129
A 1 3 85 161
A 2 1 73 139
A 2 2 89 144
A 2 3 53 112
A 3 1 69 97
A 3 2 82 128
A 3 3 84 186
A 4 1 111 145
A 4 2 78 146
A 4 3 115 159
A 5 1 74 133
A 5 2 82 153
A 5 3 59 109
B 1 1 90 146
B 1 2 58 108
B 1 3 76 99
B 2 1 105 136
B 2 2 99 139
B 2 3 75 130
B 3 1 95 160
B 3 2 95 135
B 3 3 75 123
B 4 1 95 129
B 4 2 101 130
B 4 3 82 133
B 5 1 63 109
B 5 2 86 132
B 5 3 100 142
C 1 1 72 128
C 1 2 57 137
C 1 3 67 120
C 2 1 86 110
C 2 2 79 121
C 2 3 101 166
C 3 1 82 231
C 3 2 60 125
C 3 3 74 104
C 4 1 90 155
C 4 2 84 141
C 4 3 90 123
D 1 1 91 107
D 1 2 101 183
D 1 3 91 111
D 2 1 79 146
D 2 2 97 155
D 2 3 94 109
D 3 1 69 88
D 3 2 84 107
D 3 3 95 121
D 4 1 92 127
D 4 2 112 140
D 4 3 98 119
y<-cbind(positive,negative)
treatment<-factor(treatment)
mouse<-factor(mouse)
model<-lmer(y~treatment+(1|treatment/mouse),family=binomial)
> data<-read.table(mixedinp.txt",header=T,sep="\t")
> dim(data)
[1] 54 5
> dim(data)
[1] 54 5
> y<-cbind(positive,negative)
> treatment<-factor(treatment)
> mouse<-factor(mouse)
> model<-lmer(y~treatment+(1|treatment/mouse),family=binomial)
> summary(model)
Generalized linear mixed model fit by the Laplace approximation
Formula: y ~ treatment + (1 | treatment/mouse)
AIC BIC logLik deviance
91.85 103.8 -39.93 79.85
Random effects:
Groups Name Variance Std.Dev.
mouse:treatment (Intercept) 0.0039316 0.062702
treatment (Intercept) 0.0000000 0.000000
Number of obs: 54, groups: mouse:treatment, 18; treatment, 4
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.577592 0.046034 -12.547 < 2e-16 ***
treatmentB 0.166954 0.064749 2.578 0.009923 **
treatmentC 0.008545 0.069065 0.124 0.901537
treatmentD 0.262350 0.068364 3.838 0.000124 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) tBPBS_ tCPBS_
trt -0.711
trt -0.667 0.474
trt -0.673 0.479 0.449
Since the non-mixed effects model showed evidence of overdispersion,
and since there is no quasibinomial option in lme4, I tried to account
for overdispesion by including individual level variability with..
4. A generalized linear mixed-effects model with binomial error in
which technical replication is treated as a random effect and
overdispersion is taken into account by individual level variability.
> data$obs<-1:nrow(data)
> names(data)
[1] "treatment" "mouse" "observation" "positive" "negative"
[6] "obs"
> detach(data)
> attach(data)
The following object(s) are masked _by_ '.GlobalEnv':
mouse, treatment
> model2<-lmer(y~treatment+(1|treatment/mouse)+(1|
obs) ,family=binomial)
Number of levels of a grouping factor for the random effects
is *equal* to n, the number of observations
> summary(model2)
Generalized linear mixed model fit by the Laplace approximation
Formula: y ~ treatment + (1 | treatment/mouse) + (1 | obs)
AIC BIC logLik deviance
89.75 103.7 -37.88 75.75
Random effects:
Groups Name Variance Std.Dev.
obs (Intercept) 1.0529e-02 1.0261e-01
mouse:treatment (Intercept) 1.3158e-12 1.1471e-06
treatment (Intercept) 0.0000e+00 0.0000e+00
Number of obs: 54, groups: obs, 54; mouse:treatment, 18; treatment, 4
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.57842 0.04522 -12.792 < 2e-16 ***
treatmentB 0.16590 0.06356 2.610 0.00904 **
treatmentC 0.01642 0.06784 0.242 0.80878
treatmentD 0.26677 0.06710 3.976 7.01e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) tBPBS_ tCPBS_
trtB -0.711
trtC -0.666 0.474
trtD -0.674 0.479 0.449
>
I then compared the 2 models.
> anova(model,model2,test="F")
Data:
Models:
model: y ~ treatment + (1 | treatment/mouse)
model2: y ~ treatment + (1 | treatment/mouse) + (1 | obs)
Df AIC BIC logLik Chisq Chi Df Pr(>Chisq)
model 6 91.853 103.79 -39.926
model2 7 89.750 103.67 -37.875 4.1023 1 0.04282 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
My questions:
1. Am I correct that, of the 4 models, I should use the mixed effect
model with individual variability?
Although both models make the same effects to be significant, I would
like to know which one I should report and use as input to a
subsequent multiple comparisons analysis with multiicomp.
2. If I am incorrect what model should I use and why?
3. I would appreciate any further suggestions for analyzing this data.
Thanks and best wishes,
Rich
------------------------------------------------------------
Richard A. Friedman, PhD
Associate Research Scientist,
Biomedical Informatics Shared Resource
Herbert Irving Comprehensive Cancer Center (HICCC)
Lecturer,
Department of Biomedical Informatics (DBMI)
Educational Coordinator,
Center for Computational Biology and Bioinformatics (C2B2)/
National Center for Multiscale Analysis of Genomic Networks (MAGNet)
Room 824
Irving Cancer Research Center
Columbia University
1130 St. Nicholas Ave
New York, NY 10032
(212)851-4765 (voice)
fried...@cancercenter.columbia.edu
http://cancercenter.columbia.edu/~friedman/
I am a Bayesian. When I see a multiple-choice question on a test and I
don't
know the answer I say "eeney-meaney-miney-moe".
Rose Friedman, Age 14
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