I am running a multinomial analysis with vglm(). It all works, but then I try to follow the instructions from the following website (https://rcompanion.org/handbook/H_08.html) to do a pairwise test, because emmeans cannot handle pairwise for vglm models. The lrtest() part gives me the following error: Error in lrtest.default(model) : 'list' object cannot be coerced to type 'double'
I cannot figure out what is wrong, I even copy and pasted the exact code that the website used (see below) and get the same error with their own code and dataset. Any ideas?
Their code and suggestion for doing pairwise testing with vglm() is the only pairwise testing option I found for vglm() anywhere on the web.
Here is the code along with all the expected output and extra details from their website (it is simpler than mine but gets same error anyways).
Input = ("
County Sex Result Count
Bloom Female Pass 9
Bloom Female Fail 5
Bloom Male Pass 7
Bloom Male Fail 17
Cobblestone Female Pass 11
Cobblestone Female Fail 4
Cobblestone Male Pass 9
Cobblestone Male Fail 21
Dougal Female Pass 9
Dougal Female Fail 7
Dougal Male Pass 19
Dougal Male Fail 9
Heimlich Female Pass 15
Heimlich Female Fail 8
Heimlich Male Pass 14
Heimlich Male Fail 17
")
Data = read.table(textConnection(Input),header=TRUE)
### Order factors otherwise R will alphabetize them
Data$County = factor(Data$County,
levels=unique(Data$County))
Data$Sex = factor(Data$Sex,
levels=unique(Data$Sex))
Data$Result = factor(Data$Result,
levels=unique(Data$Result))
### Check the data frame
library(psych)
headTail(Data)
str(Data)
summary(Data)
### Remove unnecessary objects
rm(Input)
Multinomial regression
library(VGAM)
model = vglm(Result ~ Sex + County + Sex:County,
family=multinomial(refLevel=1),
weights = Count,
data = Data)
summary(model)
library(car)
Anova(model,
type="II",
test="Chisq")```
Analysis of Deviance Table (Type II tests)
Response: Result
Df Chisq Pr(>Chisq)
Sex 1 6.7132 0.00957 **
County 3 4.1947 0.24120
Sex:County 3 7.1376 0.06764 .
library(rcompanion)
nagelkerke(model)
$Pseudo.R.squared.for.model.vs.null Pseudo.R.squared McFadden 0.0797857 Cox and Snell (ML) 0.7136520 Nagelkerke (Cragg and Uhler) 0.7136520
$Likelihood.ratio.test Df.diff LogLik.diff Chisq p.value 7 -10.004 20.009 0.0055508
library(lmtest)
lrtest(model)
Likelihood ratio test
Model 1: Result ~ Sex + County + Sex:County Model 2: Result ~ 1
#Df LogLik Df Chisq Pr(>Chisq)
1 8 -115.39
2 15 -125.39 7 20.009 0.005551 **
At the time of writing, the lsmeans package cannot be used with vglm models.
One option for post-hoc analysis would be to conduct analyses on reduced models, including only two levels of a factor. For example, if the variable County x Sex term had been significant, the following code could be used to create a reduced dataset with only Bloom–Female and Bloom–Male, and analyze this data with vglm.
Data.b = Data[Data$County=="Bloom" &
(Data$Sex=="Female"| Data$Sex=="Male") , ]
Data.b$County = factor(Data.b$County)
Data.b$Sex = factor(Data.b$Sex)
summary(Data.b)
County Sex Result Count
Bloom:4 Female:2 Pass:2 Min. : 5.0
Male :2 Fail:2 1st Qu.: 6.5
Median : 8.0
Mean : 9.5
3rd Qu.:11.0
Max. :17.0
library(VGAM)
model.b = vglm(Result ~ Sex,
family=multinomial(refLevel=1),
weights = Count,
data = Data.b)
lrtest(model.b)
Likelihood ratio test
#Df LogLik Df Chisq Pr(>Chisq)
1 2 -23.612
2 3 -25.864 1 4.5041 0.03381 *
Summary table of results
Comparison p-value Bloom–Female - Bloom–Male 0.034 Cobblestone–Female - Cobblestone–Male 0.0052 Dougal–Female - Dougal–Male 0.44 Heimlich–Female - Heimlich–Male 0.14
p.value = c(0.034, 0.0052, 0.44, 0.14)
p.adj = p.adjust(p.value,
method = "fdr")
p.adj = signif(p.adj,
2)
p.adj
[1] 0.068 0.021 0.440 0.190
Comparison p-value p.adj Bloom–Female - Bloom–Male 0.034 0.068 Cobblestone–Female - Cobblestone–Male 0.0052 0.021 Dougal–Female - Dougal–Male 0.44 0.44 Heimlich–Female - Heimlich–Male 0.14 0.19
It looks to me like qdrq() can be used. As I commented, you can't use the lazy interface, you have to give all the specific needed parameters:
> library(emmeans)
> RG = qdrg(formula(model), Data, coef(model), vcov(model), link = "log")
> RG
'emmGrid' object with variables:
Sex = Female, Male
County = Bloom, Cobblestone, Dougal, Heimlich
Transformation: “log”
> emmeans(RG, consec ~ Sex | County)
$emmeans
County = Bloom:
Sex emmean SE df asymp.LCL asymp.UCL
Female -0.588 0.558 Inf -1.68100 0.5054
Male 0.887 0.449 Inf 0.00711 1.7675
County = Cobblestone:
Sex emmean SE df asymp.LCL asymp.UCL
Female -1.012 0.584 Inf -2.15597 0.1328
Male 0.847 0.398 Inf 0.06643 1.6282
County = Dougal:
Sex emmean SE df asymp.LCL asymp.UCL
Female -0.251 0.504 Inf -1.23904 0.7364
Male -0.747 0.405 Inf -1.54032 0.0459
County = Heimlich:
Sex emmean SE df asymp.LCL asymp.UCL
Female -0.629 0.438 Inf -1.48668 0.2295
Male 0.194 0.361 Inf -0.51320 0.9015
Results are given on the log (not the response) scale.
Confidence level used: 0.95
$contrasts
County = Bloom:
contrast estimate SE df z.ratio p.value
Male - Female 1.475 0.716 Inf 2.060 0.0394
County = Cobblestone:
contrast estimate SE df z.ratio p.value
Male - Female 1.859 0.707 Inf 2.630 0.0085
County = Dougal:
contrast estimate SE df z.ratio p.value
Male - Female -0.496 0.646 Inf -0.767 0.4429
County = Heimlich:
contrast estimate SE df z.ratio p.value
Male - Female 0.823 0.567 Inf 1.450 0.1470
Results are given on the log (not the response) scale.
If I understand this model correctly, the response is the log of the ratio of the 2nd multinomial response to the 1st. So what we see above is estimated differences of logs and setimated differences of those differences. If run with type = "response" you would get estimated ratios, and ratios of those ratios.