David Winsemius
2016-Sep-28 20:54 UTC
[R] How to test a difference in ratios of count data in R
> On Sep 28, 2016, at 9:49 AM, Greg Snow <538280 at gmail.com> wrote: > > There are multiple ways of doing this, but here are a couple. > > To just test the fixed effect of treatment you can use the glm function: > > test <- read.table(text=" > replicate treatment n X > 1 A 32 4 > 1 B 33 18 > 2 A 20 6 > 2 B 21 18 > 3 A 7 0 > 3 B 8 4 > ", header=TRUE) > > fit1 <- glm( cbind(X,n-X) ~ treatment, data=test, family=binomial) > summary(fit1) > > Note that the default baseline value may differ between R and SAS, > which would result in a reversed sign on the slope coefficient (and > different intercept). > > To include replicate as a random effect you need an additional > package, here I use lme4 and the glmer function: > > library(lme4) > fit2 <- glmer( cbind(X, n-X) ~ treatment + (1|replicate), data=test, > family=binomial) > summary(fit2) > > > > On Tue, Sep 27, 2016 at 9:03 PM, Shuhua Zhan <szhan at uoguelph.ca> wrote: >> Hello R-experts, >> I am interested to determine if the ratio of counts from two groups differ across two distinct treatments. For example, we have three replicates of treatment A, and three replicates of treatment B. For each treatment, we have counts X from one group and counts Y from another group. My understanding is that that GLIMMIX procedure in SAS can calculate whether the ratio of counts in one group (X/X+Y) significantly differs between treatments. >> >> I think this is the way you do it in SAS. The replicate and treatment variables are self-explanatory. The first number (n) refers to the total counts X + Y; the second number (X) refers to the counts X. Is there a way to do this in R? Since we have 20,000 datasets to be tested, it may be easier to retrive the significant test as the given dataset below and its p>F value and mean ratios of treatments in R than SAS. >> >> >> data test; >> input replicate treatment$ n X; >> datalines; >> 1 A 32 4 >> 1 B 33 18 >> 2 A 20 6 >> 2 B 21 18 >> 3 A 7 0 >> 3 B 8 4 >> ; >>Greg has already shown you how that is done in R and how to do logistic regression: # I usually think of Poisson regression when I hear a desire is to model ratios of counts that have a denominator. The log(sample_size) is supplied as an offset to correct for the variation in size of subsamples. fit1 <- glm( X ~ treatment+offset(log(n)), data=test, family=poisson) summary(fit1) # And the lme4 analogue with replication: library(lme4) fit2 <- glmer( X ~ treatment + offset(log(n))+ (1|replicate), data=test, family=poisson) summary(fit2) #----output---- Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: poisson ( log ) Formula: X ~ treatment + offset(log(n)) + (1 | replicate) Data: test AIC BIC logLik deviance df.resid 31.9 31.3 -13.0 25.9 3 Scaled residuals: Min 1Q Median 3Q Max -1.0504 -0.4146 -0.3487 0.3956 1.0791 Random effects: Groups Name Variance Std.Dev. replicate (Intercept) 0.03159 0.1777 Number of obs: 6, groups: replicate, 3 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.7875 0.3372 -5.301 1.15e-07 *** treatmentB 1.3365 0.3529 3.787 0.000152 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Correlation of Fixed Effects: (Intr) treatmentB -0.838 Compare with the binomial model: #=========== fitBin <- glmer( cbind(X,n-X) ~ treatment + (1|replicate), data=test, family=binomial) coef(fitBin) #---- $replicate (Intercept) treatmentB 1 -2.0487694 2.364695 2 -0.9908556 2.364695 3 -2.1844435 2.364695 attr(,"class") [1] "coef.mer" #----- summary(fitBin) #--------- Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod] Family: binomial ( logit ) Formula: cbind(X, n - X) ~ treatment + (1 | replicate) Data: test AIC BIC logLik deviance df.resid 30.1 29.4 -12.0 24.1 3 Scaled residuals: Min 1Q Median 3Q Max -0.88757 -0.35065 -0.03137 0.26897 0.67505 Random effects: Groups Name Variance Std.Dev. replicate (Intercept) 0.4123 0.6421 Number of obs: 6, groups: replicate, 3 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.7442 0.5438 -3.208 0.00134 ** treatmentB 2.3647 0.4741 4.988 6.11e-07 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Correlation of Fixed Effects: (Intr) treatmentB -0.568 The binomial model has a logit link. Your glimmix procedure appears to have a gaussian/normal distributional assumption and an identity link by default. If we run this using those assumptions in lme4::glmer we get these results (with a warning that in this case we can overlook since the results with lmer turned out to be identical) #-------- fitNorm <- glmer( I(X/n) ~ treatment + (1|replicate), data=test, family=gaussian) #------- Warning message: In glmer(I(X/n) ~ treatment + (1 | replicate), data = test, family = gaussian) : calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated; please call lmer() directly> coef(fitNorm); summary(fitNorm)$replicate (Intercept) treatmentB 1 0.091096925 0.4925325 2 0.324579602 0.4925325 3 0.009323473 0.4925325 attr(,"class") [1] "coef.mer" Linear mixed model fit by REML ['lmerMod'] Formula: I(X/n) ~ treatment + (1 | replicate) Data: test REML criterion at convergence: -4.2 Scaled residuals: Min 1Q Median 3Q Max -0.7864 -0.4278 -0.1152 0.5143 0.8246 Random effects: Groups Name Variance Std.Dev. replicate (Intercept) 0.027895 0.16702 Residual 0.002356 0.04854 Number of obs: 6, groups: replicate, 3 Fixed effects: Estimate Std. Error t value (Intercept) 0.14167 0.10042 1.411 treatmentB 0.49253 0.03963 12.427 Correlation of Fixed Effects: (Intr) treatmentB -0.197 That's (probably) the model to compare to your SAS results if my reading of the SAS Proc GLIMMIX manual page is correct. -- David.>> proc glimmix data=test; >> class replicate treatment; >> model X/n = treatment / solution; >> random intercept / subject=replicate; >> run; >> >> ods select lsmeans; >> proc glimmix data=test; >> class replicate treatment; >> model X/n = treatment / solution; >> random intercept / subject=replicate; >> lsmeans treatment / cl ilink; >> run; >> >> I appreciate your help in advance! >> Joshua >> >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > > > -- > Gregory (Greg) L. Snow Ph.D. > 538280 at gmail.com > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.David Winsemius Alameda, CA, USA
Shuhua Zhan
2016-Sep-30 16:40 UTC
[R] How to test a difference in ratios of count data in R
Thank you, David and Greg for your help!
I drew conclusion that the treatment B significantly increases the ratio of x
group (X/n) from based on p values from the treatmentB line of the outputs at
logistic reg. and Poisson reg.(p=6.11e-07, Logistic; p=0.000152, Poisson).
I'm wondering whether the significance of the (Intercept) line in both
outputs affect my conclusion.
Logistic reg. Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.7442 0.5438 -3.208 0.00134 **
treatmentB 2.3647 0.4741 4.988 6.11e-07 ***
Poisson reg. Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.7875 0.3372 -5.301 1.15e-07 ***
treatmentB 1.3365 0.3529 3.787 0.000152 ***
I think I may use the command below to obtain the mean ratios of x group in
treatment A and B for Logistic reg. but I have not figured out yet for Poisson
reg.
> tapply(predict(fit2, type="response"), test$treatment, mean)
A B
0.1620254 0.6404239
I'll appreciate if you know the command for Poisson reg.
Joshua
________________________________
From: David Winsemius <dwinsemius at comcast.net>
Sent: Wednesday, September 28, 2016 4:54:46 PM
To: Shuhua Zhan
Cc: r-help at R-project.org; Greg Snow
Subject: Re: [R] How to test a difference in ratios of count data in R
> On Sep 28, 2016, at 9:49 AM, Greg Snow <538280 at gmail.com> wrote:
>
> There are multiple ways of doing this, but here are a couple.
>
> To just test the fixed effect of treatment you can use the glm function:
>
> test <- read.table(text="
> replicate treatment n X
> 1 A 32 4
> 1 B 33 18
> 2 A 20 6
> 2 B 21 18
> 3 A 7 0
> 3 B 8 4
> ", header=TRUE)
>
> fit1 <- glm( cbind(X,n-X) ~ treatment, data=test, family=binomial)
> summary(fit1)
>
> Note that the default baseline value may differ between R and SAS,
> which would result in a reversed sign on the slope coefficient (and
> different intercept).
>
> To include replicate as a random effect you need an additional
> package, here I use lme4 and the glmer function:
>
> library(lme4)
> fit2 <- glmer( cbind(X, n-X) ~ treatment + (1|replicate), data=test,
> family=binomial)
> summary(fit2)
>
>
>
> On Tue, Sep 27, 2016 at 9:03 PM, Shuhua Zhan <szhan at uoguelph.ca>
wrote:
>> Hello R-experts,
>> I am interested to determine if the ratio of counts from two groups
differ across two distinct treatments. For example, we have three replicates of
treatment A, and three replicates of treatment B. For each treatment, we have
counts X from one group and counts Y from another group. My understanding is
that that GLIMMIX procedure in SAS can calculate whether the ratio of counts in
one group (X/X+Y) significantly differs between treatments.
>>
>> I think this is the way you do it in SAS. The replicate and treatment
variables are self-explanatory. The first number (n) refers to the total counts
X + Y; the second number (X) refers to the counts X. Is there a way to do this
in R? Since we have 20,000 datasets to be tested, it may be easier to retrive
the significant test as the given dataset below and its p>F value and mean
ratios of treatments in R than SAS.
>>
>>
>> data test;
>> input replicate treatment$ n X;
>> datalines;
>> 1 A 32 4
>> 1 B 33 18
>> 2 A 20 6
>> 2 B 21 18
>> 3 A 7 0
>> 3 B 8 4
>> ;
>>
Greg has already shown you how that is done in R and how to do logistic
regression:
# I usually think of Poisson regression when I hear a desire is to model ratios
of counts that have a denominator. The log(sample_size) is supplied as an offset
to correct for the variation in size of subsamples.
fit1 <- glm( X ~ treatment+offset(log(n)), data=test, family=poisson)
summary(fit1)
# And the lme4 analogue with replication:
library(lme4)
fit2 <- glmer( X ~ treatment + offset(log(n))+ (1|replicate), data=test,
family=poisson)
summary(fit2)
#----output----
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation)
[glmerMod]
Family: poisson ( log )
Formula: X ~ treatment + offset(log(n)) + (1 | replicate)
Data: test
AIC BIC logLik deviance df.resid
31.9 31.3 -13.0 25.9 3
Scaled residuals:
Min 1Q Median 3Q Max
-1.0504 -0.4146 -0.3487 0.3956 1.0791
Random effects:
Groups Name Variance Std.Dev.
replicate (Intercept) 0.03159 0.1777
Number of obs: 6, groups: replicate, 3
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.7875 0.3372 -5.301 1.15e-07 ***
treatmentB 1.3365 0.3529 3.787 0.000152 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Correlation of Fixed Effects:
(Intr)
treatmentB -0.838
Compare with the binomial model:
#===========
fitBin <- glmer( cbind(X,n-X) ~ treatment + (1|replicate), data=test,
family=binomial)
coef(fitBin)
#----
$replicate
(Intercept) treatmentB
1 -2.0487694 2.364695
2 -0.9908556 2.364695
3 -2.1844435 2.364695
attr(,"class")
[1] "coef.mer"
#-----
summary(fitBin)
#---------
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation)
[glmerMod]
Family: binomial ( logit )
Formula: cbind(X, n - X) ~ treatment + (1 | replicate)
Data: test
AIC BIC logLik deviance df.resid
30.1 29.4 -12.0 24.1 3
Scaled residuals:
Min 1Q Median 3Q Max
-0.88757 -0.35065 -0.03137 0.26897 0.67505
Random effects:
Groups Name Variance Std.Dev.
replicate (Intercept) 0.4123 0.6421
Number of obs: 6, groups: replicate, 3
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.7442 0.5438 -3.208 0.00134 **
treatmentB 2.3647 0.4741 4.988 6.11e-07 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Correlation of Fixed Effects:
(Intr)
treatmentB -0.568
The binomial model has a logit link. Your glimmix procedure appears to have a
gaussian/normal distributional assumption and an identity link by default. If we
run this using those assumptions in lme4::glmer we get these results (with a
warning that in this case we can overlook since the results with lmer turned out
to be identical)
#--------
fitNorm <- glmer( I(X/n) ~ treatment + (1|replicate), data=test,
family=gaussian)
#-------
Warning message:
In glmer(I(X/n) ~ treatment + (1 | replicate), data = test, family = gaussian) :
calling glmer() with family=gaussian (identity link) as a shortcut to lmer()
is deprecated; please call lmer() directly> coef(fitNorm); summary(fitNorm)
$replicate
(Intercept) treatmentB
1 0.091096925 0.4925325
2 0.324579602 0.4925325
3 0.009323473 0.4925325
attr(,"class")
[1] "coef.mer"
Linear mixed model fit by REML ['lmerMod']
Formula: I(X/n) ~ treatment + (1 | replicate)
Data: test
REML criterion at convergence: -4.2
Scaled residuals:
Min 1Q Median 3Q Max
-0.7864 -0.4278 -0.1152 0.5143 0.8246
Random effects:
Groups Name Variance Std.Dev.
replicate (Intercept) 0.027895 0.16702
Residual 0.002356 0.04854
Number of obs: 6, groups: replicate, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.14167 0.10042 1.411
treatmentB 0.49253 0.03963 12.427
Correlation of Fixed Effects:
(Intr)
treatmentB -0.197
That's (probably) the model to compare to your SAS results if my reading of
the SAS Proc GLIMMIX manual page is correct.
--
David.
>> proc glimmix data=test;
>> class replicate treatment;
>> model X/n = treatment / solution;
>> random intercept / subject=replicate;
>> run;
>>
>> ods select lsmeans;
>> proc glimmix data=test;
>> class replicate treatment;
>> model X/n = treatment / solution;
>> random intercept / subject=replicate;
>> lsmeans treatment / cl ilink;
>> run;
>>
>> I appreciate your help in advance!
>> Joshua
>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Gregory (Greg) L. Snow Ph.D.
> 538280 at gmail.com
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
David Winsemius
Alameda, CA, USA
[[alternative HTML version deleted]]
David Winsemius
2016-Sep-30 18:37 UTC
[R] How to test a difference in ratios of count data in R
> On Sep 30, 2016, at 9:40 AM, Shuhua Zhan <szhan at uoguelph.ca> wrote: > > > Thank you, David and Greg for your help! > I drew conclusion that the treatment B significantly increases the ratio of x group (X/n) from based on p values from the treatmentB line of the outputs at logistic reg. and Poisson reg.(p=6.11e-07, Logistic; p=0.000152, Poisson). I'm wondering whether the significance of the (Intercept) line in both outputs affect my conclusion. > Logistic reg. Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -1.7442 0.5438 -3.208 0.00134 **As in most regression situations, the p-value is calculated on the basis of a null hypothesis that the baseline assemblage of covariates will be associated with an "outcome" of one on the transformed scale of analysis. For logistic regression this would imply an odds ratio of 1.0 and that the baseline covariates would then be associated with a 50:50 split of events and non-events. This is often not a hypothesis of particular interest, so generally the advice is to ignore that p-value.> treatmentB 2.3647 0.4741 4.988 6.11e-07 *** > > Poisson reg. Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -1.7875 0.3372 -5.301 1.15e-07 ***For Poisson regression the Intercept is the log( rate) at a covariate value of again zero for continuous variables and baseline for factors. The null hypothesis tested is that the log(rate) is zero. This is often not a meaningful hypothesis, (especially if age is a covariate as it usually is events in a medical settings).> treatmentB 1.3365 0.3529 3.787 0.000152 *** > > I think I may use the command below to obtain the mean ratios of x group in treatment A and B for Logistic reg. but I have not figured out yet for Poisson reg. > > > tapply(predict(fit2, type="response"), test$treatment, mean) > A B > 0.1620254 0.6404239 > > I'll appreciate if you know the command for Poisson reg.Should be the same call but the interpretation is different as mentioned above. The ?predict.glm halp page says " the alternative "response" is on the scale of the response variable. " and that is no limited to logistic regression despite the fact that the next sentence applies that principle to the logistic case. This is knowledge one would probably get in a third or fourth semester stats course, at least based on my experience 30 year ago. This is really not what the Rhelp mailing list is set up to provide, (nor am I necessarily the most qualified to provide online statistics education) so I'm suggesting you pose any further question to a forum that is designed to address such matters. Go to http://stats.stackexchange.com/ or find an outlet that is designed for self-learning of statistical concepts. This is material that I learned by both formal education and study of Breslow and Day's two volume text "Statistical Methods in Cancer Research" which I believe is a classic and still useful resource. It used GLIM as the coding platform, but I found transferring from GLIM to R rather easy. I can also recommend Venables and Ripley's "Modern Applied Statistics with S-Plus" which in it's 4th edition had R-specific annotations. It covers a wide range of applications, but does assume significant prior training. I encourage other respondents who have different recommendations for more recent texts suitable for self-study of using R for generalized linear models with replication to chime in here, since I think such recommendations _are_ on-topic. I hesitate to recommend "Mixed Effects Models with S and S-Plus" because so much has been changed in more recent versions of package lme4 that I find it very error prone to use. I wish the effort to write a replacement had continued but my searches have suggested to me that it has not happened. I'd be happy to be corrected. Best; David.> Joshua > > > From: David Winsemius <dwinsemius at comcast.net> > Sent: Wednesday, September 28, 2016 4:54:46 PM > To: Shuhua Zhan > Cc: r-help at R-project.org; Greg Snow > Subject: Re: [R] How to test a difference in ratios of count data in R > > > > On Sep 28, 2016, at 9:49 AM, Greg Snow <538280 at gmail.com> wrote: > > > > There are multiple ways of doing this, but here are a couple. > > > > To just test the fixed effect of treatment you can use the glm function: > > > > test <- read.table(text=" > > replicate treatment n X > > 1 A 32 4 > > 1 B 33 18 > > 2 A 20 6 > > 2 B 21 18 > > 3 A 7 0 > > 3 B 8 4 > > ", header=TRUE) > > > > fit1 <- glm( cbind(X,n-X) ~ treatment, data=test, family=binomial) > > summary(fit1) > > > > Note that the default baseline value may differ between R and SAS, > > which would result in a reversed sign on the slope coefficient (and > > different intercept). > > > > To include replicate as a random effect you need an additional > > package, here I use lme4 and the glmer function: > > > > library(lme4) > > fit2 <- glmer( cbind(X, n-X) ~ treatment + (1|replicate), data=test, > > family=binomial) > > summary(fit2) > > > > > > > > On Tue, Sep 27, 2016 at 9:03 PM, Shuhua Zhan <szhan at uoguelph.ca> wrote: > >> Hello R-experts, > >> I am interested to determine if the ratio of counts from two groups differ across two distinct treatments. For example, we have three replicates of treatment A, and three replicates of treatment B. For each treatment, we have counts X from one group and counts Y from another group. My understanding is that that GLIMMIX procedure in SAS can calculate whether the ratio of counts in one group (X/X+Y) significantly differs between treatments. > >> > >> I think this is the way you do it in SAS. The replicate and treatment variables are self-explanatory. The first number (n) refers to the total counts X + Y; the second number (X) refers to the counts X. Is there a way to do this in R? Since we have 20,000 datasets to be tested, it may be easier to retrive the significant test as the given dataset below and its p>F value and mean ratios of treatments in R than SAS. > >> > >> > >> data test; > >> input replicate treatment$ n X; > >> datalines; > >> 1 A 32 4 > >> 1 B 33 18 > >> 2 A 20 6 > >> 2 B 21 18 > >> 3 A 7 0 > >> 3 B 8 4 > >> ; > >> > > Greg has already shown you how that is done in R and how to do logistic regression: > > # I usually think of Poisson regression when I hear a desire is to model ratios of counts that have a denominator. The log(sample_size) is supplied as an offset to correct for the variation in size of subsamples. > > > fit1 <- glm( X ~ treatment+offset(log(n)), data=test, family=poisson) > summary(fit1) > > # And the lme4 analogue with replication: > > library(lme4) > fit2 <- glmer( X ~ treatment + offset(log(n))+ (1|replicate), data=test, > family=poisson) > summary(fit2) > #----output---- > Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) > [glmerMod] > Family: poisson ( log ) > Formula: X ~ treatment + offset(log(n)) + (1 | replicate) > Data: test > > AIC BIC logLik deviance df.resid > 31.9 31.3 -13.0 25.9 3 > > Scaled residuals: > Min 1Q Median 3Q Max > -1.0504 -0.4146 -0.3487 0.3956 1.0791 > > Random effects: > Groups Name Variance Std.Dev. > replicate (Intercept) 0.03159 0.1777 > Number of obs: 6, groups: replicate, 3 > > Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -1.7875 0.3372 -5.301 1.15e-07 *** > treatmentB 1.3365 0.3529 3.787 0.000152 *** > --- > Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 > > Correlation of Fixed Effects: > (Intr) > treatmentB -0.838 > > Compare with the binomial model: > #===========> > > fitBin <- glmer( cbind(X,n-X) ~ treatment + (1|replicate), data=test, > family=binomial) > coef(fitBin) > #---- > $replicate > (Intercept) treatmentB > 1 -2.0487694 2.364695 > 2 -0.9908556 2.364695 > 3 -2.1844435 2.364695 > > attr(,"class") > [1] "coef.mer" > #----- > summary(fitBin) > #--------- > Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) > [glmerMod] > Family: binomial ( logit ) > Formula: cbind(X, n - X) ~ treatment + (1 | replicate) > Data: test > > AIC BIC logLik deviance df.resid > 30.1 29.4 -12.0 24.1 3 > > Scaled residuals: > Min 1Q Median 3Q Max > -0.88757 -0.35065 -0.03137 0.26897 0.67505 > > Random effects: > Groups Name Variance Std.Dev. > replicate (Intercept) 0.4123 0.6421 > Number of obs: 6, groups: replicate, 3 > > Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -1.7442 0.5438 -3.208 0.00134 ** > treatmentB 2.3647 0.4741 4.988 6.11e-07 *** > --- > Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 > > Correlation of Fixed Effects: > (Intr) > treatmentB -0.568 > > The binomial model has a logit link. Your glimmix procedure appears to have a gaussian/normal distributional assumption and an identity link by default. If we run this using those assumptions in lme4::glmer we get these results (with a warning that in this case we can overlook since the results with lmer turned out to be identical) > #-------- > fitNorm <- glmer( I(X/n) ~ treatment + (1|replicate), data=test, > family=gaussian) > > #------- > Warning message: > In glmer(I(X/n) ~ treatment + (1 | replicate), data = test, family = gaussian) : > calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated; please call lmer() directly > > coef(fitNorm); summary(fitNorm) > $replicate > (Intercept) treatmentB > 1 0.091096925 0.4925325 > 2 0.324579602 0.4925325 > 3 0.009323473 0.4925325 > > attr(,"class") > [1] "coef.mer" > Linear mixed model fit by REML ['lmerMod'] > Formula: I(X/n) ~ treatment + (1 | replicate) > Data: test > > REML criterion at convergence: -4.2 > > Scaled residuals: > Min 1Q Median 3Q Max > -0.7864 -0.4278 -0.1152 0.5143 0.8246 > > Random effects: > Groups Name Variance Std.Dev. > replicate (Intercept) 0.027895 0.16702 > Residual 0.002356 0.04854 > Number of obs: 6, groups: replicate, 3 > > Fixed effects: > Estimate Std. Error t value > (Intercept) 0.14167 0.10042 1.411 > treatmentB 0.49253 0.03963 12.427 > > Correlation of Fixed Effects: > (Intr) > treatmentB -0.197 > > That's (probably) the model to compare to your SAS results if my reading of the SAS Proc GLIMMIX manual page is correct. > > -- > David. > > >> proc glimmix data=test; > >> class replicate treatment; > >> model X/n = treatment / solution; > >> random intercept / subject=replicate; > >> run; > >> > >> ods select lsmeans; > >> proc glimmix data=test; > >> class replicate treatment; > >> model X/n = treatment / solution; > >> random intercept / subject=replicate; > >> lsmeans treatment / cl ilink; > >> run; > >> > >> I appreciate your help in advance! > >> Joshua > >> > >> > >> [[alternative HTML version deleted]] > >> > >> ______________________________________________ > >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > > > > > > > > -- > > Gregory (Greg) L. Snow Ph.D. > > 538280 at gmail.com > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > David Winsemius > Alameda, CA, USADavid Winsemius Alameda, CA, USA