knitr::opts_chunk$set(echo = TRUE)

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, ]

`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

library(bdots) library(data.table) ## Let's work with cohort_unrelated dataset, as it has multiple groups dat <- as.data.table(cohort_unrelated) ## And add a fixed value for which we want to find a correlation dat[, val := rnorm(1), by = Subject] head(dat)

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:

`bdObj`

, any object returned from a`bdotsFit`

call`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`ciBands`

, a boolean indicating whether or not we want to return 95% confidence intervals. Default is`FALSE`

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

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 plot(corr_ci) ## 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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