I am running a generalised mixed effects model, of family logistic regression, using function glmer().
I am predicting likelihood of response (0/1) and my fixed effects to explore in my final model are: Day/Night (D/N) Male/Female (M/F) Time since trial began (continuous)
my random effects are ID, and location.
I am having a lot of difficulty in working out the best model to fit these, not only do the significances of the 3 fixed effects vary greatly when they are modelled alone or in combination with another... but I am reading contrasting information online as to whether "*" or ":" should be used to define interaction terms?
For example, just looking at Male/ Female and 'Time since trial began'
modelling just male/ female:
`mod1 <- glmer(RESPONSE ~ Sex + (1|`ID CODE`) + (1|Location), data = data, family = binomial)
summary(mod2)
`
output:
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -12.856 1.592 -8.075 6.74e-16 ***
SexF -12.970 3.375 -3.843 0.000122 ***
Running just Time since Trial began:
mod2 <- glmer(RESPONSE ~ Time + (1|`ID CODE`) + (1|Location), data = data, family = binomial)
summary(mod2)
output:
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.3185 0.5398 -2.442 0.014592 *
Time -1.3036 0.3542 -3.680 0.000233 ***
When running as interaction using "*" :
mod3 <- glmer(RESPONSE ~ Time*Sex + (1|`ID CODE`) + (1|Location), data = data, family = binomial)
summary(mod3)
output:
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -11.483 1.955 -5.873 4.29e-09 ***
Time -1.301 1.677 -0.776 0.4380
SexF -12.488 5.439 -2.296 0.0217 *
Time:SexF 0.396 4.827 0.082 0.9346
When running as interaction using ":" :
mod4 <- glmer(RESPONSE ~ Time:Sex + (1|`ID CODE`) + (1|Location), data = data, family = binomial)
summary(mod4)
output:
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.2957 0.5427 -2.388 0.01695 *
Time:SexM -1.1943 0.3698 -3.229 0.00124 **
Time:SexF -1.5406 0.5019 -3.070 0.00214 **