I created the following GAMM function using the R package gamlss:
model<-gamlss(Overlap~ Diff.Long + Diff.Fzp + DiffSeason +
random(Xnumber),family=BEZI(mu.link = "logit", sigma.link = "log",
nu.link = "logit"),data=data,trace=F)
The output of this model is:
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss(formula = Overlap ~ Diff.Long + Diff.Fzp + DiffSeason +
random(Xnumber), family = BEZI(mu.link = "logit",
sigma.link = "log", nu.link = "logit"), data = data, trace = F)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.188647 0.208359 -0.905 0.36715
Diff.Long -0.002072 0.000736 -2.814 0.00575 **
Diff.Fzp -0.030909 0.013749 -2.248 0.02648 *
DiffSeasonEW-LW -0.617976 0.211260 -2.925 0.00415 **
DiffSeasonLW-LW -0.356989 0.270548 -1.320 0.18963
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.865 0.126 14.8 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.3470 0.3156 -7.437 2.02e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms maybe are not accurate.
------------------------------------------------------------------
No. of observations in the fit: 126
Degrees of Freedom for the fit: 11.15247
Residual Deg. of Freedom: 114.8475
at cycle: 5
Global Deviance: -43.54531
AIC: -21.24037
SBC: 10.39118
******************************************************************
I'm not the most familiar yet with additive models, and am trying to find the significance of my random effect ("Xnumber"). I know that the package mgcv has a way but the package gamlss is the only one to have the distribution I need (zero-inflated beta).
If anyone knows any functions I can use, that would be great? Or is it in the summary, but I just don't know where to look?
