I was hoping for some advice regarding possible explanations for the
fitted probability values I obtained for a multinomial logistic
regression. The analysis aims to predict whether Capgras delusions
(present/absent) are associated with group (ABH, SV, homicide; values
= 1,2,3,), controlling for previous violence. What has me puzzled is
that for each combination the fitted probabilities are identical. I
haven't seen this in the worked examples I have come across and was
interested to know if this is a problem or what might be the cause
for this. I ran an analysis with another independent variable and
obtained a similar pattern.
Any assistance with this is appreciated
Bob Green
> predictors <- expand.grid(group=1:3, in.acute.danger = c("y","n"),
violent.convictions = c("y","n"))
> p.fit <- predict(mod.multacute, predictors, type='probs')
> p.fit
1 2 3
1 0.4615070 0.3077061 0.2307869
2 0.4615070 0.3077061 0.2307869
3 0.4615070 0.3077061 0.2307869
4 0.7741997 0.1290310 0.0967693
5 0.7741997 0.1290310 0.0967693
6 0.7741997 0.1290310 0.0967693
7 0.4230927 0.3846055 0.1923017
8 0.4230927 0.3846055 0.1923017
9 0.4230927 0.3846055 0.1923017
10 0.7058783 0.1647063 0.1294153
11 0.7058783 0.1647063 0.1294153
12 0.7058783 0.1647063 0.1294153
> mod.multacute <- multinom(group ~ in.acute.danger *
violent.convictions, data = kc, na.action = na.omit)
# weights: 15 (8 variable)
initial value 170.284905
iter 10 value 131.016050
final value 130.993722
converged
> summary(mod.multacute, cor=F, Wald=T)
Call:
multinom(formula = group ~ in.acute.danger * violent.convictions,
data = kc, na.action = na.omit)
Coefficients:
(Intercept) in.acute.dangery violent.convictionsy
in.acute.dangery:violent.convictionsy
2 -1.455279 1.3599055 -0.3364982
0.02651913
3 -1.696416 0.9078901 -0.3830842
0.47860722
Std. Errors:
(Intercept) in.acute.dangery violent.convictionsy
in.acute.dangery:violent.convictionsy
2 0.2968082 0.5282077 0.6162498
0.9936493
3 0.3279838 0.6312569 0.6946869
1.1284891
Value/SE (Wald statistics):
(Intercept) in.acute.dangery violent.convictionsy
in.acute.dangery:violent.convictionsy
2 -4.903094 2.574566 -0.5460419
0.02668862
3 -5.172256 1.438226 -0.5514486
0.42411327
Residual Deviance: 261.9874
AIC: 277.9874
> Anova (mod.multacute)
Anova Table (Type II tests)
Response: group
LR Chisq Df Pr(>Chisq)
in.acute.danger 10.9335 2 0.004225 **
violent.convictions 0.5957 2 0.742430
in.acute.danger:violent.convictions 0.1895 2 0.909600
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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