correlations

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

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)

##    Subject  Time DB_cond  Fixations LookType Group         val
##      <num> <int>  <char>      <num>   <char> <int>       <num>
## 1:       1     0      50 0.01136364   Cohort    50 -0.08495266
## 2:       1     4      50 0.01136364   Cohort    50 -0.08495266
## 3:       1     8      50 0.01136364   Cohort    50 -0.08495266
## 4:       1    12      50 0.01136364   Cohort    50 -0.08495266
## 5:       1    16      50 0.02272727   Cohort    50 -0.08495266
## 6:       1    20      50 0.02272727   Cohort    50 -0.08495266

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)
head(corr_ci)

##     time Correlation      lower        upper     Group Group1 Group2
##    <int>       <num>      <num>        <num>    <char> <char> <char>
## 1:     0  -0.7593430 -0.9462663 -0.192093126 Cohort 50 Cohort     50
## 2:     4  -0.7511366 -0.9442319 -0.173629170 Cohort 50 Cohort     50
## 3:     8  -0.7376427 -0.9408544 -0.144126203 Cohort 50 Cohort     50
## 4:    12  -0.7191254 -0.9361529 -0.105287669 Cohort 50 Cohort     50
## 5:    16  -0.6961348 -0.9302055 -0.059539066 Cohort 50 Cohort     50
## 6:    20  -0.6694374 -0.9231415 -0.009570667 Cohort 50 Cohort     50

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

##     time Correlation     Group Group1 Group2
##    <int>       <num>    <char> <char> <char>
## 1:     0  -0.7593430 Cohort 50 Cohort     50
## 2:     4  -0.7511366 Cohort 50 Cohort     50
## 3:     8  -0.7376427 Cohort 50 Cohort     50
## 4:    12  -0.7191254 Cohort 50 Cohort     50
## 5:    16  -0.6961348 Cohort 50 Cohort     50
## 6:    20  -0.6694374 Cohort 50 Cohort     50

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", ])