gistfile1.txt

plot_model(fit.4, grid=T, type="diag")
plot_model(fit.1, grid=T, sort.est="sort.all", y.offset=1.0)
plot_model(fit.1, type="eff")
plot_model(fit.1, show.values = TRUE, value.offset = .3)
plot_model(fit.1, type="re") 
plot_model(fit.1, type="std")  
plot_model(fit.1, type="resid")
plot_model(fit.1, type="pred", grid=T, terms=c("curve_day [all]"))
tab_model(fit.1, 
          show.std=T, 
          show.r2=T, 
          show.aic=T, 
          show.aicc = T, 
          show.dev=T, 
          show.re.var=T)
resfit1<-augment(fit.1)
boxplot(resfit1$.resid~resfit1$newField)

ggplot(dfnew2, aes(as.factor(curve_day),
                  resid(fit.4), 
                  group=as.factor(curve_day)))+
  geom_boxplot()+
  geom_hline(yintercept=0, col="red", linetype="dashed")+
  xlab("Time")+
  ylab("Residuals")+
  ggtitle("Residuals vs Time")+
  theme_bw()+
  theme(axis.text.x=element_blank(),axis.ticks.x=element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank())

ggplot(resfit1,aes(x=feed_provided_cum, 
                  y=.fitted, 
                  colour=.resid))+
  geom_point()+
  geom_abline(intercept = 0, slope = 1, lty=2)+
  scale_color_viridis_c(option="turbo")+
  labs(title = "Calibration", y="Predicted", x="Observed", colour="Difference")+
  theme_bw()+
  facet_wrap(~newField, ncol=2)+
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank())

qqmath(~resid(fit.1)|dfnew2$newField, 
       prepanel = prepanel.qqmathline,
       panel = function(x, ...) {
         panel.qqmathline(x, ...)
         panel.qqmath(x, ...)
       })
boxplot(resfit1$.resid~resfit1$lcn_id)
xyplot(resfit1$.resid~resfit1$.fitted)
plot(acf(resid(fit.1)))
qqp(resid(fit.1))
anova(fit.1)
plot(ranef(fit.1))