Extracting estimated and derived parameters

2024-05-02

library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.3.2
library(phenomix)
library(dplyr)
library(TMB)
#> Warning: package 'TMB' was built under R version 4.3.2

We will start with the original simple model fit to the fishdist data,

data("fishdist")
cov_dat = data.frame(nyear = unique(fishdist$year))
# rescale year -- could also standardize with scale()
cov_dat$nyear = cov_dat$nyear - min(cov_dat$nyear) 

datalist = create_data(fishdist, 
  min_number=0, 
  variable = "number", 
  time="year", 
  date = "doy",
  asymmetric_model = FALSE, 
  mu = ~ nyear,
  sigma = ~ nyear,
  covar_data = cov_dat,
  est_sigma_re = TRUE,
  est_mu_re = TRUE,
  tail_model = "gaussian")

set.seed(1)
fitted = fit(datalist)

Extracting parameters manually

The first option for extracting parameters manually is via the sdreport of TMB objects. This includes both estimated and derived quantities. You can see the names of what’s available by looking at

fitted$sdreport$value

or perhaps look at these in tabular form,

table(names(fitted$sdreport$value))
#> 
#>         b_mu       b_sig1      lower25           mu    obs_sigma         pred 
#>            2            2           21           21            1         1005 
#>        range       sigma1        theta      upper75 year_log_tot     year_tot 
#>           21           21           21           21           21           21

So if you wanted to extract the mean phenological peak in each time step (named mu), you could look at the indices of the names

idx <- which(names(fitted$sdreport$value) == "mu")

And then use the respective means and sds corresponding to those indices,

m <- fitted$sdreport$value[idx]
s <- fitted$sdreport$sd[idx]

Helper functions

We’ve also included some helper functions to extract these more quickly yourself. These all take in a fitted model object, and can be called as

m <- extract_means(fitted) # means
s <- extract_sigma(fitted) # sigmas, describing tails
t <- extract_theta(fitted) # theta parameters (scaling)
u <- extract_upper(fitted) # upper quartile
m <- extract_lower(fitted) # lower quartile

Or if you want to extact all of the above, you can use

e <- extract_all(fitted)

Annual summaries

In addition to the parameters above, we can extract the derived annual totals (predicted across all days 1-365). These are extracted with the following extract_annual function. Note that the second parameter controls whether the estimates are in normal or log space.

est_log <- extract_annual(fitted, log=TRUE)
est_normal <- extract_annual(fitted, log=FALSE)