Refit with Saved Parameters
In bdots: Bootstrapped Differences of Time Series

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(bdots)
# Make smaller for cran
cohort_unrelated$Subject <- as.numeric(cohort_unrelated$Subject)
cohort_unrelated <- as.data.table(cohort_unrelated)
cohort_unrelated <- cohort_unrelated[Subject < 10, ]

Overview

This vignette walks through using a text file of previously fit model parameters to use in the bdotsRefit function. This is convenient if you have already gone through the refitting process and would like to save/load the refitted parameters in a new session.

To demonstate this process, we start with fitting a set of curves to our data

library(bdots)

fit <- bdotsFit(data = cohort_unrelated,
                subject = "Subject",
                time = "Time",
                y = "Fixations",
                group = c("Group", "LookType"),
                curveType = doubleGauss(concave = TRUE),
                cor = TRUE,
                numRefits = 2,
                cores = 2,
                verbose = FALSE)

refit <- bdotsRefit(fit, quickRefit = TRUE, fitCode = 5)

From this, we can create an appropriate data.table that can be used in a later session

parDT <- coefWriteout(refit)
head(parDT)

It's important that columns are included that match the unique identifying columns in our bdotsObj, and that the parameters match the coefficients used from bdotsFit

## Subject, Group, and LookType
head(refit)

## doubleGauss pars
colnames(coef(refit))

We can save our parameter data.table for later use, or read in any other appropriately formatted data.frame

## Save this for later using data.table::fwrite
fwrite(parDT, file = "mypars.csv")
parDT <- fread("mypars.csv")

Once we have this, we can pass it as an argument to the bdotsRefit function. Doing so will ignore the remaining arguments

new_refit <- bdotsRefit(refit, paramDT = parDT)

We end up with a bdotsObj that matches what we had previously. As seeds have not yet been implemented, the resulting parameters may not be exact. It will, however, assist with not having to go through the entire refitting process again manually (although, there is always the option to save the entire object with save(refit, file = "refit.RData))