knitr::opts_chunk$set(echo = TRUE)
# Make smaller for cran
cohort_unrelated$Subject <- as.numeric(cohort_unrelated$Subject)
cohort_unrelated <-
cohort_unrelated <- cohort_unrelated[Subject < 10, ]

Correlations in bdots

This vignette is created to illustrate the use of the bdotsCorr function, which finds the correlation between a fixed value in our dataset and the collection of fitted curves at each time points for each of the groups fit in bdotsFit.

First, let's take an existing dataset and add a fixed value for each of the subjects


## Let's work with cohort_unrelated dataset, as it has multiple groups
dat <-

## And add a fixed value for which we want to find a correlation
dat[, val := rnorm(1), by = Subject]


Now, we go about creating our fitted object as usual

## Create regular fit in bdots
fit <- bdotsFit(data = dat,
                subject = "Subject",
                time = "Time",
                group = c("LookType", "Group"),
                y = "Fixations", curveType = doubleGauss2(),
                cores = 2)

Using this fit object, we now introduce the bdotsCorr function, taking four arguments:

  1. bdObj, any object returned from a bdotsFit call
  2. val, a length one character vector of the value with which we want to correlate. val should be a column in our original dataset, and it should be numeric
  3. ciBands, a boolean indicating whether or not we want to return 95% confidence intervals. Default is FALSE
  4. method, paralleling the method argument in cor and cor.test. The default is pearson.
## Returns a data.table of class bdotsCorrObj
corr_ci <- bdotsCorr(fit, val = "val", ciBands = TRUE)

## Same, without confidence intervals
corr_noci <- bdotsCorr(fit, val = "val")

From here, we are able to use the data.tables themselves for whatever we may be interested in. We also have a plotting method associated with this object

## Default is no bands

## Try again with bands
plot(corr_ci, ciBands = TRUE)

## Narrow in on a particular window
plot(corr_ci, window = c(750, 1500))

Because this object is a data.table, we have full use of subsetting capabilities for our plots

plot(corr_ci[Group2 == "50", ])

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bdots documentation built on Jan. 7, 2023, 1:18 a.m.