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, ]
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
## 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
## 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))
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