We saw in the general overview when
first generating our model fits with bdotsFit that we we
could specify the curve with the argument curveType.
Presently, the bdots package contains three options for
this: doubleGauss, logistic, and
polynomial. Documentation is included for each of these
curves.
library(bdots)
fit <- bdotsFit(data = cohort_unrelated,
subject = "Subject",
time = "Time",
y = "Fixations",
group = c("DB_cond", "LookType"),
curveType = doubleGauss(concave = TRUE),
cores = 2)Note that each of these is a function in their own right and must be
passed in as a call object. Curve functions that include
arguments further specifying the type of curve, i.e.,
doubleGauss(concave = TRUE) and
polynomial(degree = n), should include these when the call
is passed into bdotsFit as seen in the example above.
Because each of the functions exists independently of
bdotsFit, users can specify their own curve functions for
the fitting and bootstrapping process. The purpose of this vignette is
to demonstrate how to do so. If you find that you have a curve function
that is especially useful, please create a request to have it added to
the bdots package here.
We will examine the doubleGauss function in more detail
to see how we might go about creating our own. First, let’s identify the
components of this function
doubleGauss
#> function (dat, y, time, params = NULL, concave = TRUE, ...)
#> {
#> if (is.null(params)) {
#> params <- dgaussPars(dat, y, time, concave)
#> }
#> else {
#> if (length(params) != 6)
#> stop("doubleGauss requires 6 parameters be specified for refitting")
#> if (!all(names(params) %in% c("mu", "ht", "sig1", "sig2",
#> "base1", "base2"))) {
#> stop("doubleGauss parameters for refitting must be correctly labeled")
#> }
#> }
#> if (is.null(params)) {
#> return(NULL)
#> }
#> y <- str2lang(y)
#> time <- str2lang(time)
#> ff <- bquote(.(y) ~ (.(time) < mu) * (exp(-1 * (.(time) -
#> mu)^2/(2 * sig1^2)) * (ht - base1) + base1) + (mu <=
#> .(time)) * (exp(-1 * (.(time) - mu)^2/(2 * sig2^2)) *
#> (ht - base2) + base2))
#> attr(ff, "parnames") <- names(params)
#> return(list(formula = ff, params = params))
#> }
#> <bytecode: 0x5609f8414ca0>
#> <environment: namespace:bdots>There are four things to note:
concave = TRUE, which specifies
the curve, we also have dat, y,
time, params = NULL, and ....
These are the names that must be used for the function to be called
correctly. The first represents a data.frame or
data.table subset from the data argument to
bdotsFit, while y and time
correspond to their respective arguments in bdotsFit and
should assume that the arguments are passed in as
character. It’s important to remember to set
params = NULL, as this is only used during the refitting
step.
params = NULL, the body of the
function computes the necessary starting parameters to be used with the
gnls fitting function. In this case, the function
dgaussPars handles the initial parameter estimation and
returns a named numeric. When params is not
NULL, it’s usually a good idea to verify that it is the
correct length and has the correct parameter names.
formula object, as
it must be quoted. One may use bquote and
str2lang to substitute in the character values
for y and time. Alternatively, if this is to
only be used for a particular data set, one can simply use
quote with the appropriate values used for y
and time, as we will demonstrate below. Finally, the quoted
formula should contain a single attribute
parnames which has the names of the parameters used.
formula and params, a named
numeric with the parameters.
Briefly, we can see how this function is used by subsetting the data to a single subject and calling it directly.
## Return a unique subject/group permutation
dat <- cohort_unrelated[Subject == 1 & DB_cond == 50 & LookType == "Cohort", ]
dat
#> Subject Time DB_cond Fixations LookType Group
#> <char> <int> <char> <num> <char> <int>
#> 1: 1 0 50 0.01136364 Cohort 50
#> 2: 1 4 50 0.01136364 Cohort 50
#> 3: 1 8 50 0.01136364 Cohort 50
#> 4: 1 12 50 0.01136364 Cohort 50
#> 5: 1 16 50 0.02272727 Cohort 50
#> ---
#> 497: 1 1984 50 0.02272727 Cohort 50
#> 498: 1 1988 50 0.02272727 Cohort 50
#> 499: 1 1992 50 0.02272727 Cohort 50
#> 500: 1 1996 50 0.02272727 Cohort 50
#> 501: 1 2000 50 0.02272727 Cohort 50## See return value
doubleGauss(dat = dat, y = "Fixations", time = "Time", concave = TRUE)
#> $formula
#> Fixations ~ (Time < mu) * (exp(-1 * (Time - mu)^2/(2 * sig1^2)) *
#> (ht - base1) + base1) + (mu <= Time) * (exp(-1 * (Time -
#> mu)^2/(2 * sig2^2)) * (ht - base2) + base2)
#> attr(,"parnames")
#> [1] "mu" "ht" "sig1" "sig2" "base1" "base2"
#>
#> $params
#> mu ht sig1 sig2 base1 base2
#> 428.00000000 0.21590909 152.00000000 396.00000000 0.01136364 0.02272727We will now create an entirely new function that is not included in
bdots to demonstrate that it works the same; the only
change we will make is to substitute in the values for y
and time without using str2lang. For our data
set here, the corresponding values to y and
time are "Fixations" and "Time",
respectively
doubleGauss2 <- function (dat, y, time, params = NULL, concave = TRUE, ...) {
if (is.null(params)) {
## Instead of defining our own, just reuse the one in bdots
params <- bdots:::dgaussPars(dat, y, time, concave)
}
else {
if (length(params) != 6)
stop("doubleGauss requires 6 parameters be specified for refitting")
if (!all(names(params) %in% c("mu", "ht", "sig1", "sig2",
"base1", "base2"))) {
stop("doubleGauss parameters for refitting must be correctly labeled")
}
}
## Here, we use Fixations and Time directly
ff <- bquote(Fixations ~ (Time < mu) * (exp(-1 * (Time - mu)^2 /
(2 * sig1^2)) * (ht - base1) + base1) + (mu <= Time) *
(exp(-1 * (Time - mu)^2/(2 * sig2^2)) * (ht - base2) + base2))
return(list(formula = ff, params = params))
}
same_fit_different_day <- bdotsFit(data = cohort_unrelated,
subject = "Subject",
time = "Time",
y = "Fixations",
group = c("DB_cond", "LookType"),
curveType = doubleGauss2(concave = TRUE),
cores = 2)Seeds have not yet been implemented, so there is some possibility
that the resulting parameters are slightly different; however, using the
coef function, we can roughly confirm their equivalence
## Original fit
head(coef(fit))
#> mu ht sig1 sig2 base1 base2
#> [1,] 417.6899 0.1986711 145.5628 323.1882 0.01586359 0.03412371
#> [2,] 636.8447 0.2632815 306.2330 214.9787 -0.02154794 0.02858644
#> [3,] 647.5295 0.2547779 496.6745 256.4257 -0.18223562 0.01217570
#> [4,] 734.1526 0.2585742 405.6348 240.2926 -0.05751246 0.03455280
#> [5,] 501.1948 0.2258572 398.7759 158.6752 -0.16159473 0.02529158
#> [6,] 460.7152 0.3067659 382.7323 166.0833 -0.24330894 0.03992168
## "New" fit
head(coef(same_fit_different_day))
#> mu ht sig1 sig2 base1 base2
#> [1,] 417.7084 0.1986854 145.5640 323.0982 0.01586123 0.03413207
#> [2,] 636.9012 0.2632786 306.3314 214.9349 -0.02159757 0.02858820
#> [3,] 647.5817 0.2547770 496.8812 256.3761 -0.18243279 0.01217937
#> [4,] 734.1179 0.2585766 405.5335 240.3190 -0.05744374 0.03455157
#> [5,] 501.3765 0.2258482 399.5403 158.5325 -0.16241720 0.02529587
#> [6,] 460.7372 0.3067971 382.5240 165.9931 -0.24286084 0.03993349