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sandbox_script.R
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#test type 3-5 models
library(moult)
library(moultmcmc)
library(dplyr)
library(ggplot2)
library(rstan)
data("sanderlings")
sanderlings$MCat <- case_when(sanderlings$MIndex == 0 ~ 1,
sanderlings$MIndex == 1 ~3,
TRUE ~ 2)
sanderlings$random <- rnorm(nrow(sanderlings), 0,1)
m1 = moult(MIndex ~ Day,data = sanderlings, type = 1)
uz1 = uz1_linpred(moult_cat_column = "MCat",
date_column = "Day",
data = sanderlings,
log_lik = TRUE,
cores = 4)
summary_table(m1)
logLik(m1)
summary_table(uz1)
compare_plot(m1, uz1, names = c('ml','mcmc'))
m2 = moult(MIndex ~ Day,data = sanderlings, type = 2)
uz2 = uz2_linpred("MIndex",
date_column = "Day",
data = sanderlings,
log_lik = FALSE,
cores = 4)
uz2cov = uz2_linpred("MIndex",#model with a covariate
date_column = "Day",
start_formula = ~random,
duration_formula = ~random,
data = sanderlings,
log_lik = FALSE,
cores = 4)
moult_plot(uz2cov)
predict(uz2cov)
compare_plot(m2, uz2, names = c('ml','mcmc'))
logLik(m2)
summary_table(uz2)
#compare type 1/2
compare_plot(m1,m2, names = c('Type1','Type2'))
compare_plot(uz1,uz2, names = c('Type1','Type2'))
m3 = moult(MIndex ~ Day,data = sanderlings, type = 3)
uz3 = uz3_linpred("MIndex",
date_column = "Day",
data = sanderlings,
log_lik = FALSE,
cores = 4)
compare_plot(m3, uz3)
stan_trace(uz3$stanfit)
m4 = moult(MIndex ~ Day,data = sanderlings, type = 4)
uz4 = uz4_linpred("MIndex",
date_column = "Day",
data = sanderlings,
log_lik = FALSE,
cores = 4)
compare_plot(m4, uz4)
m5 = moult(MIndex ~ Day,data = sanderlings, type = 5)
uz5 = uz5_linpred("MIndex",
date_column = "Day",
data = sanderlings,
log_lik = TRUE,
cores = 4)
compare_plot(m5,uz5)
plot(MIndex ~ Day,data = sanderlings, col = sanderlings$MCat)
segments(coef(m5)[2], 0, coef(m5)[2]+coef(m5)[1], 1)
segments(fixef(uz5)[1,1], 0, fixef(uz5)[1,1]+fixef(uz5)[2,1], 1, lty = 2)
lines(1:365, dnorm(1:365, mean = coef(m5)[2], sd = coef(m5)[3]))
lines(1:365, dnorm(1:365, mean = fixef(uz5)[1,1], sd = fixef(uz5)[4,1]), lty = 2)
lines(1:365, pnorm(1:365, mean = coef(m5)[2], sd = coef(m5)[3], lower.tail = FALSE))
lines(1:365, pnorm(1:365, fixef(uz5)[1,1], sd = fixef(uz5)[4,1], lower.tail = FALSE), lty = 2)
summary_table(m5)
logLik(m5)
summary_table(uz5)
rstan::stan_trace(uz5$stanfit)
data(weavers)
if (is.numeric(weavers$Moult)) {
scores <- format(weavers$Moult, scientific = FALSE, trim = TRUE) } else {
scores = weavers$Moult }
mscores = substr(scores, 1, 9)
feather.mass <- c(10.4, 10.8, 11.5, 12.8, 14.4, 15.6, 16.3, 15.7, 15.7)
weavers$pfmg <- ms2pfmg(mscores, feather.mass)
weavers$day <- date2days(weavers$RDate, dateformat = "yyyy-mm-dd",
startmonth = 8)
weavers$sex_factor <- factor(ifelse(weavers$Sex == 1 | weavers$Sex == 3, "male", ifelse(weavers$Sex == 2 | weavers$Sex == 4, "female", NA)))
weavers <- na.omit(weavers)
plot(pfmg ~ day, weavers)
weavers_trunc <- filter(weavers, day > 50 & day <320)
points(pfmg ~ day, weavers_trunc, col = 'green')
m88.3 <- moult(pfmg ~ day, data = weavers_trunc, type=3)
uz88.3 <- uz3_linpred('pfmg',
'day',
log_lik = FALSE,
data = weavers_trunc)#type 2 fails on start values. possibly numerical underflow with bad start values?? - but ML converges, so a smarter way of estimating start values necessary? Type 3 works, but highly heterogeneous sampling times for each chain - use non-flat priors??
summary(m88.3)
stan_trace(uz88.3$stanfit)
compare_plot(m88.3,uz88.3)
uz88.3sex <- uz3_linpred('pfmg',
'day',
start_formula = ~ sex_factor,
duration_formula = ~ sex_factor,
log_lik = FALSE,
chains = 2,
data = weavers)
weavers$MCat <- case_when(weavers$pfmg == 0 ~ 1,
weavers$pfmg == 1 ~3,
TRUE ~ 2)
table(weavers$MCat)
m88.5 <- moult(pfmg ~ day, data = weavers, type = 5)
m88.4 <- moult(pfmg ~ day, data = weavers, type = 4)
##combined model tests
set.seed(1234)
sanderlings_combined <- get(data("sanderlings"))
sanderlings_combined$MCat <- case_when(sanderlings$MIndex == 0 ~ 1,
sanderlings$MIndex == 1 ~3,
TRUE ~ 2)
remove_m_scores <- function(data, prop = 0.5){
#randomly subsample 1/3 of rows and set moult score to NA
m_birds = which(data$MCat == 2)
cats <- sample(m_birds,size = floor(length(m_birds)*prop))
data$MIndex[cats] <- NA
return(data)}
table(sanderlings_combined$MCat)
uz12_25 <- uz12_linpred("MIndex",
"MCat",
date_column = "Day",
data = remove_m_scores(sanderlings_combined, 0.25),
log_lik = FALSE)
uz12_50 <- uz12_linpred("MIndex",
"MCat",
date_column = "Day",
data = remove_m_scores(sanderlings_combined, 0.5),
log_lik = FALSE)
uz12_75 <- uz12_linpred("MIndex",
"MCat",
date_column = "Day",
data = remove_m_scores(sanderlings_combined, 0.75),
log_lik = FALSE)
uz12_80 <- uz12_linpred("MIndex",
"MCat",
date_column = "Day",
data = remove_m_scores(sanderlings_combined, 0.8),
log_lik = FALSE)
uz12_90 <- uz12_linpred("MIndex",
"MCat",
date_column = "Day",
data = remove_m_scores(sanderlings_combined, 0.9),
log_lik = FALSE)
compare_plot(m1, uz12)
compare_plot(m2, uz12)
compare_plot(uz1,uz12)
compare_plot(uz2,uz12)
bind_rows(summary_table(uz1),
#summary_table(uz12_90),
#summary_table(uz12_80),
summary_table(uz12_75),
summary_table(uz12_50),
summary_table(uz12_25),
summary_table(uz2),
.id='prop_cat') %>%
left_join(tibble(prop_cat = as.character(1:5), prop_score = c(0,25,50,75,100))) %>%
filter(!(parameter %in% c('lp__', 'log_sd_(Intercept)'))) %>%
ggplot(aes(x = prop_score, y = estimate, ymin = lci, ymax = uci)) + geom_pointrange() + facet_wrap(~parameter, scales = 'free_y') + xlab("Proportion of scored birds in active moult category") + theme_classic()