gistfile1.txt

## External validation of VAR model ##
ext <- read_csv("ExternalVal.csv")
ext_var<-ext%>%
  filter(location_identification==1)%>%
  mutate(TempMIN = zoo::na.aggregate(t_min_out),
         TempMAX = zoo::na.aggregate(t_act_out),
         TempACT = zoo::na.aggregate(t_max_out), 
         AnimalACT = zoo::na.aggregate(animals_actual))%>%
  dplyr::select(TempMIN,TempMAX,TempACT,AnimalACT)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
autoplot(ts(ext_var[,1:3],
            start=c(2021,9,4),
            frequency=365.25))+theme_bw()
autoplot(ts(ext_var[,4],
            start=c(2021,9,4),
            frequency=365.25))+theme_bw()
nhor<-14 
var.pred<-predict(var.model_lev,
                  dumvar=ext_var[143:156,],
                  n.ahead=nhor)
plot(var.pred);fanchart(var.pred)
Egg_for_data<-cbind(date_interval=seq(ymd('2022-01-24'),
                        ymd('2022-02-06'),
                        by = "day"),
                    as.data.frame(var.pred$fcst$Eggs))
g1<-autoplot(ts(ext_var[,1:3],
            start=c(2021,9,4),
            frequency=365.25))+
  theme_bw()+theme(legend.position="bottom")
g2<-autoplot(ts(ext_var[,4],
            start=c(2021,9,4),
            frequency=365.25))+theme_bw()
g3<-plot(var.pred)
g4<-ext%>%
  filter(location_identification==1)%>%
ggplot(.)+
  geom_point(aes(x=date(date_interval), y=egg_count))+
  geom_line(aes(x=date(date_interval), y=egg_count),alpha=0.7)+
  geom_point(data=Egg_for_data, 
             aes(x=date_interval, y=fcst), col="red")+
  geom_line(data=Egg_for_data, 
             aes(x=date_interval, y=fcst), col="red", lty=2)+
  geom_ribbon(data=Egg_for_data, 
             aes(x=date_interval, ymin=lower, ymax=upper, fill="red"), alpha=0.3,
             show.legend = FALSE)+
  labs(x="Date", 
       y="Egg_count", 
       title="Egg count predicted for location 1")+
  theme_bw()
grid.arrange(g1,g2,g4,ncol=1)