Load packages & data
library(vcdExtra)
library(gnm) # for Diag(), Symm()
data(VisualAcuity, package="vcd")
Work with the data for females
women <- subset(VisualAcuity, gender=="female", select=-gender)
Fit the independence model
indep <- glm(Freq ~ right + left, data = women, family=poisson)
mosaic(indep, residuals_type="rstandard", gp=shading_Friendly,
main="Vision data: Independence (women)" )
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)
Quasi-independence: ignore the diagonal cells by fitting them
exactly
quasi.indep <- glm(Freq ~ right + left + Diag(right, left),
data = women, family = poisson)
mosaic(quasi.indep, residuals_type="rstandard", gp=shading_Friendly,
main="Quasi-Independence (women)" )
![](data:image/png;base64,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)
Symmetry: test F[i,j] = F[j,i].
Note that this model does not include the ‘main’ effects of right and
left, so assumes marginal homogeneity
symmetry <- glm(Freq ~ Symm(right, left),
data = women, family = poisson)
mosaic(symmetry, residuals_type="rstandard", gp=shading_Friendly,
main="Symmetry model (women)" )
![](data:image/png;base64,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)
Quasi-symmetry: allow different marginal frequencies for left and
right
quasi.symm <- glm(Freq ~ right + left + Symm(right, left),
data = women, family = poisson)
mosaic(quasi.symm, residuals_type="rstandard", gp=shading_Friendly,
main="Quasi-Symmetry model (women)")
![](data:image/png;base64,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)
Model comparisons: for nested models
anova(indep, quasi.indep, quasi.symm, test="Chisq")
## Analysis of Deviance Table
##
## Model 1: Freq ~ right + left
## Model 2: Freq ~ right + left + Diag(right, left)
## Model 3: Freq ~ right + left + Symm(right, left)
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 9 6672
## 2 5 199 4 6472 <2e-16 ***
## 3 3 7 2 192 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(symmetry, quasi.symm, test="Chisq")
## Analysis of Deviance Table
##
## Model 1: Freq ~ Symm(right, left)
## Model 2: Freq ~ right + left + Symm(right, left)
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 6 19.25
## 2 3 7.27 3 12 0.0075 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model summaries, with AIC and BIC
models <- glmlist(indep, quasi.indep, symmetry, quasi.symm)
LRstats(models)
## Likelihood summary table:
## AIC BIC LR Chisq Df Pr(>Chisq)
## indep 6803 6808 6672 9 <2e-16 ***
## quasi.indep 338 347 199 5 <2e-16 ***
## symmetry 157 164 19 6 0.0038 **
## quasi.symm 151 161 7 3 0.0638 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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