I'm trying to plot the Worm plot residuals on a model fitted using the gamlss
function from the gamlss
package. The interest graph looks like the one below:
Initially, below is the computational routine referring to the use of the wormplot_gg
function from the childsds
package, however, the result expressed using the function described above is not looks like the example shown above, which is being applied to a dataset contained within R.
library(ggplot2)
library(gamlss)
library(childsds)
head(Orange)
Dados <- Orange
Model <- gamlss(circumference~age, family=NO,data=Dados); Model
wp(Model)
wormplot_gg(m = Model)
Below are the traditional results via the wp
function in the gamlss
package.
And finally, we have the results obtained through the wormplot_gg
function from the childsds
package. However, as already described, this one does not present itself in the way I am interested, that is, with the visual structure of the first figure.
using qqplotr https://aloy.github.io/qqplotr/index.html with the detrend=True
option
library(qqplotr)
set.seed(1)
df <- data.frame(z=rnorm(50))
ggplot(df, aes(sample=z)) +
stat_qq_point(detrend = T) +
stat_qq_band(detrend = T, color='black', fill=NA, size=0.5)
you can also add geom_hline(yintercept = 0)
edit:
In the case of using this with a gamlss model, the first have to extract the randomized residuals out of the model, which for gamlss is done simply with the function residuals
, so you can just do e.g., df <- data.frame(z=residuals(Model))
and then just continue with the rest of the code