In annakrystalli/vBffyR: Improving age inputs to the von Bertalanffy Growth Function

library(rstan)
library(tidyverse)
library(purrr)
library(testthat)
set.seed(1)
## Load data
load("../inst/extdata/Spence_turtle_data.Rdata")
stan_dir <- "../from Mike/"

list.files("../R", full.names = T) %>% map(source)

Herring

Assign species data to df

nrow(her_on)
sub_rows <- 1:500
df <- her_on[sub_rows,]

# create metadata sheet shell
tibble(var_names = names(df), description = "", units = "", var_type = "") %>% write_csv("../inst/extdata/metadata.csv")

df

exp_df <- catch_dat_expand(df, quarter = "Quarter", age = "Age", length_class =  "LngtClass",
                 CANoAtLngt = "CANoAtLngt")

h_complete_dat<-list(t = exp_df$age, l = exp_df$length_class, N = length(exp_df$age), q = exp_df$qs,mu_a=0.8576*2*pi-pi,sigma_a=0.40493)

fit_c <- stan(file = paste0(stan_dir, "vb.stan"), data = h_complete_dat, iter = 2000, chains = 1,control=list(adapt_delta=0.85,max_treedepth=10))

fit_c2 <- stan(file = paste0(stan_dir, "vb.stan"), data = h_complete_dat, iter = 2000, chains = 1,control=list(adapt_delta=0.85,max_treedepth=10))

compare(fit_c, fit_c2)

summary(fit_c)

plot(fit_c)

traceplot(fit_c, pars = c("l_inf", "k"))

h_comp_out<-extract(fit_c)

Inputing data

The factor type in R is not supported as a data element for RStan and must be converted to integer codes via as.integer. The Stan modeling language distinguishes between integers and doubles (type int and real in Stan modeling language, respectively). The stan function will convert some R data (which is double-precision usually) to integers if possible.

Stan cannot handle missing values in data automatically, so no element of the data can contain NA values.

annakrystalli/vBffyR documentation built on May 14, 2019, 9:34 a.m.