# Proper use of weights argument in linear mixed model [lme()]?

After attempting to produce a linear mixed model I was left with a great deal of heterogeneity.

lme1 <- lme(Average.payoff ~ Game + Type + Others.Type + Game:Type +
Game:Others.Type + Type:Others.Type, random=~1|Subjects, method="REML",
data=Subjectsm1)


The response term Average.payoff is continuous, whilst all explanatory variables are binary.

When I look to validate, I can clearly see that the spread of the residuals decreases with the larger fitted values. So, I decided to look at using the weights argument. I came up with the following model which seems to be the best fit (AIC and loglik).

vf1 <- varComb(varIdent(form=~1|Others.Type), varIdent(form=~1|Game),
varIdent(form=~1|Type), varIdent(form=~1|Male))

lme1 <- lme(Average.payoff ~ Game + Type + Others.Type + Game:Type +
Game:Others.Type + Type:Others.Type, random=~1|Subjects, method="ML",
weights=vf1, data=Subjectsm1)

1. Can I justify using the varIdent function alongside random effects?
2. Can I justify combining varIdent functions so the variance of all explanatory variables are accounted for?
3. If I remove a fixed effect explanatory variable for being insignificant should the same variable be removed from the variance structure?
4. If I get a better fit (using AIC and loglik) without including the random effects by re-modelling with gls(), can I justify using the model without the random effects?

The experiment involved $20$ subjects, for each subject I have two samples, one where Others.Type=0 and one where Others.Type=1.