Missing confidence intervals complicate model predictions and plots
Question
I am running a model where the response variable is proportion, inflated with a majority of 'zero' and quite a few '1' responses, the terms of the model interact. I have therefore opted for a GALMSS model with family=BEINF.
The model coefficients seem reasonable, yet when I try to calculate or plot the marginal effects for the model I run into trouble. Based on the errors and warnings I receive I think this may be because there is no data for some levels of factors when used in certain interactions. Therefore some confidence intervals are NA.
I am looking for a way to work around this as I'd like to plot the marginal effects with use of confidence intervals, but am unsure how.
Below are my coding attempts, at the bottom of the post is dput for a reproducible example.
pr1 <- gamlss(Winproportion~Opponent:Age + Number_opps:Age, family=BEINF,data=pr1data,method=mixed())
summary(pr1, level=0.95) #Model summary output
Could not compute variance-covariance matrix of predictions. No confidence intervals are returned.
confint(pr1, level=0.95) #model confidence intervals
#Attempt to plot anyways
plot_model(pr1, type = "pred", terms = c("Number_opps","Age","Opponent"),dodge=0.5)
Could not compute variance-covariance matrix of predictions. No confidence intervals are returned.
I attempted to circumvent this using ggpredict, which did not work either
ggpredict(Winproportion,terms=c("Number_opps","Age","Opponent"),ci.lvl=0.95,what="mu")
But it returns the same 'Could not compute vcov...' Libraries
library(dplyr)
library(ggplot2)
library(sjPlot)
library(gamlss.dist)
library(gamlss)
library(ggeffects)
CODE:
structure(list(Age = structure(c(2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L), .Label = c("Adult", "Child"), class = "factor"),
Number_opps = c(5, 4, 5, 5, 7, 7, 7, 5, 4, 5, 7, 6, 7, 7,
5, 6, 6, 5, 5, 7, 4, 5, 4, 7, 7, 6, 7, 7, 6, 6, 6, 5, 4,
6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 4, 6, 7, 7, 7, 6, 4, 5,
5, 5, 5, 7, 7, 7, 3, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 4, 5,
3, 3, 3, 4, 5, 6, 5, 5, 4, 3, 3, 3, 4, 4, 3, 6, 4, 6, 5,
6, 5, 5, 4, 5, 4, 7, 6, 7, 5, 5, 7, 3, 5, 4, 3, 2, 4, 4,
4, 5, 4, 3, 6, 3, 4, 4, 3, 6, 6, 4, 3, 6, 6, 6, 6, 6, 6,
4, 4, 3, 3, 5, 5, 6, 7, 4, 4, 4, 5, 4, 4, 4, 7, 7, 6, 7,
3, 7, 6, 4, 3, 5, 3, 4, 4, 2, 2, 1, 6, 5, 5, 5, 3, 3, 6,
6, 4, 5, 6, 3, 4, 5, 5, 4, 6, 5, 7, 5, 5, 7, 7, 4, 7, 7,
7, 7, 3, 4, 5, 4, 4, 3, 7, 7, 2, 5, 4, 5, 5, 4, 6, 5, 5,
3, 7, 4, 5, 5, 3, 5, 7, 5, 3, 3, 7, 2, 6, 4, 3, 3, 5, 7,
7, 7, 5, 5, 4, 4, 5, 5, 5, 5, 4, 5, 5, 4, 4, 4, 4, 6, 5,
5, 4, 4, 6, 6, 5, 6, 5, 5, 5, 4, 3, 7, 6, 7, 5, 4, 7, 5,
6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 4, 6, 7, 4, 4, 4, 4, 7, 7,
6, 7, 3, 7, 4, 4, 5, 4, 6, 5, 5, 5, 6, 6, 4, 6, 5, 5, 4,
4, 6, 4, 4, 4, 4, 5, 4, 4, 7, 5, 5, 7, 7, 7, 7, 7, 4, 7,
4, 4, 5, 4, 4, 4, 4, 7, 7, 4, 5, 4, 6, 5, 5, 7, 5, 3, 4,
7, 5, 4, 4, 7, 6, 5, 7, 7, 7, 5, 5, 4, 5, 5, 5, 5, 4, 5,
5, 2, 3, 3, 4, 3, 3, 2, 2, 4, 4, 4, 4, 5, 6, 5, 5, 2, 3,
3, 3, 4, 4, 3, 6, 4, 6, 5, 6, 5, 5, 3, 4, 5, 4, 3, 3, 3,
4, 7, 1, 7, 5, 4, 5, 7, 5, 4, 3, 3, 4, 4, 4, 2, 3, 3, 3,
4, 3, 3, 3, 3, 6, 3, 2, 3, 2, 4, 3, 6, 6, 6, 6, 6, 6, 4,
4, 2, 4, 6, 7, 3, 3, 4, 4, 4, 4, 4, 2, 4, 4, 4, 7, 7, 6,
7, 3, 7, 6, 4, 4, 4, 3, 5, 3, 4, 4, 3, 5, 5, 5, 2, 3, 3,
6, 6, 3, 3, 2, 3, 4, 5, 5, 3, 4, 4, 3, 3, 3, 4, 4, 4, 4,
5, 4, 4, 7, 5, 2, 3, 3, 5, 7, 7, 4, 7, 7, 7, 3, 4, 7, 3,
4, 5, 4, 4, 4, 4, 3, 2, 3, 7, 7, 4, 5, 4, 3, 3, 3, 5, 5,
4, 6, 3, 3, 3, 5, 5, 3, 3, 7, 3, 4, 5, 5, 3, 5, 3, 4, 7,
5, 3, 2, 3, 3, 3, 4, 4, 3, 7, 4, 4, 3, 3, 3, 3, 5, 4, 5,
5, 7, 7, 7, 3, 5, 4, 5, 3, 3, 5, 6, 3, 4, 6, 4, 6, 6, 4,
7, 6, 7, 5, 7, 3, 4, 2, 5, 6, 4, 4, 6, 6, 6, 6, 6, 6, 6,
6, 4, 5, 5, 6, 7, 4, 4, 4, 5, 4, 7, 7, 6, 7, 7, 6, 4, 6,
3, 6, 6, 4, 5, 6, 3, 4, 4, 6, 7, 7, 7, 7, 7, 7, 7, 3, 4,
4, 7, 7, 4, 5, 5, 7, 5, 5, 7, 7, 2, 6, 5, 4, 2, 5, 5, 5,
7, 7, 7, 5, 5, 4, 4, 2, 5, 5, 5, 5, 5, 4, 5, 5, 5, 2, 3,
3, 4, 3, 3, 2, 2, 3, 4, 4, 4, 4, 5, 6, 5, 5, 2, 4, 3, 3,
3, 3, 4, 4, 4, 3, 6, 4, 6, 5, 6, 5, 5, 3, 4, 5, 4, 3, 3,
3, 4, 7, 6, 2, 2, 7, 2, 2, 5, 4, 5, 7, 5, 4, 3, 3, 4, 4,
4, 2, 3, 3, 2, 5, 3, 4, 3, 3, 3, 3, 6, 3, 2, 4, 3, 6, 6,
2, 2, 4, 6, 6, 6, 6, 6, 6, 4, 4, 3, 3, 2, 4, 5, 5, 6, 7,
3, 3, 4, 4, 5, 4, 4, 4, 2, 4, 4, 4, 7, 7, 6, 7, 7, 6, 4,
4, 4, 3, 3, 5, 3, 4, 4, 2, 2, 6, 3, 5, 5, 5, 2, 3, 3, 3,
6, 6, 5, 6, 3, 3, 2, 3, 3, 4, 5, 5, 3, 4, 4, 4, 6, 3, 3,
3, 4, 4, 4, 4, 5, 4, 2, 2, 4, 7, 5, 2, 3, 3, 5, 7, 7, 4,
7, 7, 7, 3, 4, 7, 3, 3, 4, 4, 5, 4, 4, 4, 4, 3, 2, 3, 7,
7, 4, 5, 4, 3, 3, 3, 5, 5, 4, 6, 3, 3, 3, 5, 5, 3, 3, 7,
3, 4, 5, 5, 3, 5, 3, 4, 7, 5, 3, 2, 3, 3, 3, 2, 4, 4, 3,
7, 6, 4, 4, 3, 3, 3, 3, 5, 4, 2, 5, 5, 5, 7, 7, 7, 3, 5,
5, 4, 4, 2, 5, 5, 5, 5, 5, 4, 5, 5, 4, 5, 3, 4, 3, 3, 3,
2, 2, 2, 2, 4, 4, 4, 4, 5, 6, 5, 5, 4, 3, 4, 4, 4, 6, 6,
5, 6, 5, 5, 3, 4, 5, 4, 3, 1, 3, 7, 6, 2, 2, 7, 2, 2, 5,
4, 5, 7, 3, 5, 3, 4, 4, 4, 3, 3, 2, 5, 3, 4, 3, 3, 4, 3,
3), Opponent = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
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1 answers
solution1
0 2022-08-31 23:09:57
You can try the development version of the marginaleffects package , which can compute standard errors for predicted values using the delta method. (Disclaimer: I am the author.) Install it from Github:
library(remotes)
install_github("vincentarelbundock/marginaleffects")
Estimate the model:
library(marginaleffects)
library(gamlss)
pr1data <- na.omit(pr1data)
pr1data <- subset(pr1data, !is.na(Age))
pr1 <- gamlss(
Winproportion ~ Opponent:Age + Number_opps:Age,
family = BEINF,
data = pr1data,
method = mixed())
Call the predictions() function and display the first 6 rows of the results:
predictions(pr1, what = "mu") |> head()
#> rowid type predicted std.error Winproportion Opponent Age Number_opps
#> 1 1 response 0.2852127 0.06649437 0.07142857 Opp5 Adult 4
#> 2 2 response 0.2458822 0.05741226 0.14285714 Opp5 Adult 5
#> 3 3 response 0.2852127 0.06649437 0.00000000 Opp5 Adult 4
#> 4 4 response 0.1787867 0.04544875 0.11111111 Opp5 Adult 7
#> 5 5 response 0.1787867 0.04544875 0.26315789 Opp5 Adult 7
#> 6 6 response 0.2103789 0.05048144 0.00000000 Opp5 Adult 6
Obviously, you could then construct symmetric intervals as usual:
predictions(pr1, what = "mu") |>
transform(
conf.low = predicted - 1.96 * std.error,
conf.high = predicted + 1.96 * std.error) |>
head()
#> rowid type predicted std.error Winproportion Opponent Age Number_opps
#> 1 1 response 0.2852127 0.06649437 0.07142857 Opp5 Adult 4
#> 2 2 response 0.2458822 0.05741226 0.14285714 Opp5 Adult 5
#> 3 3 response 0.2852127 0.06649437 0.00000000 Opp5 Adult 4
#> 4 4 response 0.1787867 0.04544875 0.11111111 Opp5 Adult 7
#> 5 5 response 0.1787867 0.04544875 0.26315789 Opp5 Adult 7
#> 6 6 response 0.2103789 0.05048144 0.00000000 Opp5 Adult 6
#> conf.low conf.high
#> 1 0.15488379 0.4155417
#> 2 0.13335415 0.3584102
#> 3 0.15488379 0.4155417
#> 4 0.08970716 0.2678663
#> 5 0.08970716 0.2678663
#> 6 0.11143532 0.3093226
And you can make predictions for user-specified values of the regressors using the datagrid() function and the newdata argument. There are many examples in the vignette.
Finally, note that the plot_cap() function will only return confidence intervals when the model is linear, or when it is a model for which we can make predictions on the link function and then backtransform them. This is not the case here, but you can still do this:
plot_cap(pr1, condition = "Number_opps", what = "mu")
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