This vignette walks through using a text file of previously fit model parameters to use in the brefit
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 demonstrate this process, we start with fitting a set of curves to our data
library(bdots)
bfit(data = cohort_unrelated,
fit <-subject = "Subject",
time = "Time",
y = "Fixations",
group = c("Group", "LookType"),
curveFun = doubleGauss(concave = TRUE),
cor = TRUE,
numRefits = 2,
cores = 2,
verbose = FALSE)
brefit(fit, quickRefit = TRUE, fitCode = 5)
refit <-#> All observations fitCode greater than 5. Nothing to refit :)
From this, we can create an appropriate data.table
that can be used in a later session
coefWriteout(refit)
parDT <-head(parDT)
#> Subject Group LookType mu ht sig1 sig2 base1
#> <char> <char> <char> <num> <num> <num> <num> <num>
#> 1: 1 50 Cohort 417.6899 0.1986711 145.5628 323.1882 0.01586359
#> 2: 1 65 Cohort 636.8447 0.2632815 306.2330 214.9787 -0.02154793
#> 3: 2 50 Cohort 647.5295 0.2547779 496.6745 256.4257 -0.18223561
#> 4: 2 65 Cohort 734.1526 0.2585742 405.6348 240.2926 -0.05751246
#> 5: 3 50 Cohort 501.1949 0.2258572 398.7760 158.6752 -0.16159477
#> 6: 3 65 Cohort 460.7152 0.3067659 382.7322 166.0833 -0.24330874
#> base2
#> <num>
#> 1: 0.03412371
#> 2: 0.02858644
#> 3: 0.01217570
#> 4: 0.03455280
#> 5: 0.02529158
#> 6: 0.03992168
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 bfit
## Subject, Group, and LookType
head(refit)
#> Subject Group LookType fit R2 AR1 fitCode
#> <char> <char> <char> <list> <num> <lgcl> <int>
#> 1: 1 50 Cohort <gnls[19]> 0.9701367 FALSE 0
#> 2: 1 65 Cohort <gnls[19]> 0.9805103 FALSE 0
#> 3: 2 50 Cohort <gnls[19]> 0.9813076 FALSE 0
#> 4: 2 65 Cohort <gnls[19]> 0.9701257 FALSE 0
#> 5: 3 50 Cohort <gnls[19]> 0.9765418 FALSE 0
#> 6: 3 65 Cohort <gnls[19]> 0.9534922 FALSE 0
## doubleGauss pars
colnames(coef(refit))
#> [1] "mu" "ht" "sig1" "sig2" "base1" "base2"
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")
fread("mypars.csv") parDT <-
Once we have this, we can pass it as an argument to the brefit
function. Doing so will ignore the remaining arguments
brefit(refit, paramDT = parDT) new_refit <-
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))
head(new_refit)
#> Key: <Subject, Group, LookType>
#> Subject Group LookType fit R2 AR1 fitCode
#> <char> <char> <char> <list> <num> <lgcl> <int>
#> 1: 1 50 Cohort <gnls[19]> 0.9701367 FALSE 0
#> 2: 1 50 Unrelated_Cohort <gnls[19]> 0.9793524 FALSE 0
#> 3: 1 65 Cohort <gnls[19]> 0.9805103 FALSE 0
#> 4: 1 65 Unrelated_Cohort <gnls[19]> 0.8742477 FALSE 1
#> 5: 2 50 Cohort <gnls[19]> 0.9813076 FALSE 0
#> 6: 2 50 Unrelated_Cohort <gnls[19]> 0.9561882 FALSE 0