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ANOVA type lll ss table for GLMER?
4 messages · Heather Moylett, Ben Bolker, Henrik Singmann
Dear Heather,
you could try to use mixed from the afex package which will give you Type III p-values for the effects via Chi^2 tests (or alternatively via parametric bootstrap):
require(afex)
(spden2 <- mixed(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson, data=rm, nAGQ = 9, method = "LRT")
Note however, that loading afex changes your overall contrasts, to reset the default contrasts use:
options(contrasts=c('contr.treatment', 'contr.poly'))
Furthermore, (g)lmer doesn't break the factors done by *all* levels. It removes the first levels (usually). Hence the parameters cannot directly be interpreted if this level is "significant".
Hope this helps,
Henrik
Am 14.05.2014 15:45, schrieb Heather Moylett:
Hello group,
This is my first time posting, so I hope I have explained my needs clearly
below.
I am running a repeated measure analysis with a raw species count data set
(SpDens). I have run different model types (zeroinfl, glm, glmer) and have
identified glmer to have the best fit. The output generated by GLMER breaks
my between groups (treat) and within groups (samp) factors down by levels.
In addition to this, I would like to look at the effect of treat and samp
overall, something similar to an ANOVA table (Type lll SS). When I use
Anova(object) I receive an ANOVA table with an F val and no P-vals. I would
prefer to stick with the z-stat and p-vals. I have seen this reported in
other papers, so I know it can be done...just can't figure out how to do it!
Components of the model:
samp: 23 sampling dates is the repeated measure (within groups)
treat: 4 levels (between groups)
TRANSECT: experimental unit (subject), 4/treat and data collected from all
16 every sampling date
When I run this code:
RM <- read.csv("C:/Users/heatbell/Desktop/Walthour-Moss/STATS/CH
1/Final/R/RM.csv")
View(RM)
rm <- subset(RM, SAMPLE >= 2)
rm<- within(rm, {
samp<-factor(SAMPLE)
yr<-factor(YEAR)
treat<-factor(TREAT)
})
summary(rm)
summary(spden<-glmer(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson,
data=rm, nAGQ = 9))
Thank you for the help!
Heather
Dr. Henrik Singmann Albert-Ludwigs-Universit?t Freiburg, Germany http://www.psychologie.uni-freiburg.de/Members/singmann
On 14-05-14 10:02 AM, Henrik Singmann wrote:
Dear Heather,
you could try to use mixed from the afex package which will give you
Type III p-values for the effects via Chi^2 tests (or alternatively via
parametric bootstrap):
require(afex)
(spden2 <- mixed(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson,
data=rm, nAGQ = 9, method = "LRT")
Note however, that loading afex changes your overall contrasts, to reset
the default contrasts use:
options(contrasts=c('contr.treatment', 'contr.poly'))
But (despite the fact that I **really** don't like afex's default behaviour of changing the overall contrasts) -- you should definitely use contr.sum when computing a marginal ANOVA table (i.e. do NOT reset the contrasts until after you're done constructing your table), if you insist on doing that.
Furthermore, (g)lmer doesn't break the factors done by *all* levels. It removes the first levels (usually). Hence the parameters cannot directly be interpreted if this level is "significant". Hope this helps, Henrik Am 14.05.2014 15:45, schrieb Heather Moylett:
Hello group,
This is my first time posting, so I hope I have explained my needs
clearly
below.
I am running a repeated measure analysis with a raw species count data
set
(SpDens). I have run different model types (zeroinfl, glm, glmer) and
have
identified glmer to have the best fit. The output generated by GLMER
breaks
my between groups (treat) and within groups (samp) factors down by
levels.
In addition to this, I would like to look at the effect of treat and samp
overall, something similar to an ANOVA table (Type lll SS). When I use
Anova(object) I receive an ANOVA table with an F val and no P-vals. I
would
prefer to stick with the z-stat and p-vals. I have seen this reported in
other papers, so I know it can be done...just can't figure out how to
do it!
Components of the model:
samp: 23 sampling dates is the repeated measure (within groups)
treat: 4 levels (between groups)
TRANSECT: experimental unit (subject), 4/treat and data collected from
all
16 every sampling date
When I run this code:
RM <- read.csv("C:/Users/heatbell/Desktop/Walthour-Moss/STATS/CH
1/Final/R/RM.csv")
View(RM)
rm <- subset(RM, SAMPLE >= 2)
rm<- within(rm, {
samp<-factor(SAMPLE)
yr<-factor(YEAR)
treat<-factor(TREAT)
})
summary(rm)
summary(spden<-glmer(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson,
data=rm, nAGQ = 9))
Thank you for the help!
Heather
Am 14.05.2014 21:47, schrieb Ben Bolker:
On 14-05-14 10:02 AM, Henrik Singmann wrote:
Dear Heather,
you could try to use mixed from the afex package which will give you
Type III p-values for the effects via Chi^2 tests (or alternatively via
parametric bootstrap):
require(afex)
(spden2 <- mixed(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson,
data=rm, nAGQ = 9, method = "LRT")
Note however, that loading afex changes your overall contrasts, to reset
the default contrasts use:
options(contrasts=c('contr.treatment', 'contr.poly'))
But (despite the fact that I **really** don't like afex's default
behaviour of changing the overall contrasts) -- you should definitely
use contr.sum when computing a marginal ANOVA table (i.e. do NOT reset
the contrasts until after you're done constructing your table), if you
insist on doing that.
First, all afex functions (including mixed) are unaffected by global contrasts as long as the argument check.contrasts = TRUE (which is the default). In other words, mixed per default uses contr.sum independently of the global contrasts (more specifically, it sets it for all factors if not already contr.sum or if the global contrasts are not contr.sum).
Second, I give in. From the current development version on (version 0.10-110) afex *does not* change the global contrasts anymore. This should not affect any of the functions within afex (my tests confirm that). To make setting contrasts globally easy, I added the following convenience functions: set_sum_contrasts(), set_default_contrasts(), set_treatment_contrasts(), ...
Are you happy now, Ben? :)
You can install the the development version of afex from R-forge (may take a few hours): install.packages("afex", repos="http://R-Forge.R-project.org")
Furthermore, (g)lmer doesn't break the factors done by *all* levels. It removes the first levels (usually). Hence the parameters cannot directly be interpreted if this level is "significant". Hope this helps, Henrik Am 14.05.2014 15:45, schrieb Heather Moylett:
Hello group,
This is my first time posting, so I hope I have explained my needs
clearly
below.
I am running a repeated measure analysis with a raw species count data
set
(SpDens). I have run different model types (zeroinfl, glm, glmer) and
have
identified glmer to have the best fit. The output generated by GLMER
breaks
my between groups (treat) and within groups (samp) factors down by
levels.
In addition to this, I would like to look at the effect of treat and samp
overall, something similar to an ANOVA table (Type lll SS). When I use
Anova(object) I receive an ANOVA table with an F val and no P-vals. I
would
prefer to stick with the z-stat and p-vals. I have seen this reported in
other papers, so I know it can be done...just can't figure out how to
do it!
Components of the model:
samp: 23 sampling dates is the repeated measure (within groups)
treat: 4 levels (between groups)
TRANSECT: experimental unit (subject), 4/treat and data collected from
all
16 every sampling date
When I run this code:
RM <- read.csv("C:/Users/heatbell/Desktop/Walthour-Moss/STATS/CH
1/Final/R/RM.csv")
View(RM)
rm <- subset(RM, SAMPLE >= 2)
rm<- within(rm, {
samp<-factor(SAMPLE)
yr<-factor(YEAR)
treat<-factor(TREAT)
})
summary(rm)
summary(spden<-glmer(SpDens~(treat*samp)-1+(1|TRANSECT),family=poisson,
data=rm, nAGQ = 9))
Thank you for the help!
Heather
Dr. Henrik Singmann Albert-Ludwigs-Universit?t Freiburg, Germany http://www.psychologie.uni-freiburg.de/Members/singmann