MJacobs1985

B0 <- tidy_summary_1$estimate[1]
B1 <- tidy_summary_1$estimate[2]
B2 <- tidy_summary_1$estimate[3]
d %>%
  add_fitted_draws(model2, n = 200, re_formula = NA) %>%
  ggplot(aes(x = ADF, y = ADG)) +
  geom_line(aes(y = .value, group = .draw), alpha = 0.1) +
  geom_abline(intercept = B0+B2, slope = B1, color = "blue") +theme_bw()

MJacobs1985

neff_ratio(model2)
rhat(model2)
mcmc_trace(model2, size = 0.1)
mcmc_dens_overlay(model2)
mcmc_acf(model2)
pp_check(model2) + labs(x = "ADG", title = "model2")

MJacobs1985

model2<- stan_glmer(
  ADG~ADF + Included2017 + (1|Studycode),
  data = d, family = gaussian,
  prior_intercept = normal(100,3, autoscale = TRUE),
  prior = normal(0, 2.5, autoscale = TRUE), 
  prior_aux = normal(9, 3, autoscale = TRUE),
  prior_covariance = decov(reg = 1, conc = 1, shape = 1, scale = 1),
  chains = 4, iter = 5000*2, seed = 84735)
summary(model2)

MJacobs1985

model1<-lmer(ADG~ADF + Included2017 + (1|Studycode), data=d)
summary(model1)
plot(model1)

MJacobs1985

Book<- read_excel("X:/StatisticsPlatform/Website/ELearning/Workshops/Bayesian Analysis/Data/Book1.xlsx")
d<-Book%>%dplyr::select(ADG,ADF,FCR,Studycode,Included2017)
d<-d[complete.cases(d), ]
d$Studycode<-as.factor(d$Studycode)
ggplot(d, aes(y = ADG, x = ADF, 
                 colour=Studycode, 
                 size=FCR)) + 
  geom_point(alpha=0.2)+
  facet_grid(~Included2017)+
  theme_bw()

MJacobs1985

library(LaplacesDemon)
library(tidyverse)
library(brms)
library(cmdstanr)
library(posterior)
library(bayesplot)
library(coda)
library(mvtnorm)
library(loo)
library(dagitty)

MJacobs1985

library(LaplacesDemon)
library(tidyverse)
library(brms)
library(cmdstanr)
library(posterior)
library(bayesplot)
library(coda)
library(mvtnorm)
library(loo)
library(dagitty)

MJacobs1985

library(readr)
library(ggplot2)
library(gganimate)
library(tidyverse)
library(ggrepel)
library(lubridate)
library(tidymodels)
library(modeltime)
library(tsbox)
library(TSstudio)

MJacobs1985

m13.4 <- ulam(
  alist(
    ADG ~ dnorm(m0,s0),
    m0 <- a[Country]+ b[Year]+c[Treatment],
    a[Country]   ~ dnorm(m1, s1),
    b[Year]      ~ dnorm(m2, s2),
    c[Treatment] ~ dnorm(m3, s3),
    m1           ~ dnorm(38,10),
    m2           ~ dnorm(14,5),
    m3           ~ dnorm(0,2),

MJacobs1985

Book<- read_excel("X:/StatisticsPlatform/Website/ELearning/Workshops/Bayesian Analysis/Data/Book1.xlsx")
d<-Book%>%dplyr::select(ADG,Country)
d<-d[complete.cases(d), ]
d$Country<-as.factor(d$Country)
histogram(~ADG | Country, data=d,
          type="density",
       xlab="ADG" , 
       ylab="Density")

fit<-lmer(ADG~1+(1|Country), data=d);summary(fit)