Description Usage Arguments Value References Examples
View source: R/predict_dominance.R
Predict hemispheric dominance based on observed laterality measures, using the methods described in \insertCiteSorensen2020;textualBayesianLaterality.
1 2 3 4 5 6 7 8  predict_dominance(
data,
parameters = dplyr::tibble(dominance = rep(c("left", "right", "none"), each = 2),
handedness = rep(c("left", "right"), 3), mean_li = c(10, 12, 24, 24, 0, 0), sd_li =
c(24.9, 17, 24.9, 17, 22, 22), prob_dominance = c(0.65, 0.87, 0.35, 0.13, 0, 0)),
truncation = c(100, 100),
icc = 0
)

data 
Data frame with the following columns:
In addition, an optional column named 
parameters 
Data frame in which the first two columns specify combinations of hemispheric dominance and handedness and the last three columns specify the corresponding parameter values. In particular, the columns are defined as follows:

truncation 
Numeric vector with two elements specifying the lower and upper bounds for truncation of the normal distribution for dichotic listening scores. 
icc 
Intraclass correlation for repeated measurements on the same individual. Defaults to 0. 
The probability of left or right hemispheric dominance in additional
columns of data
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # The package comes with two example datasets.
# The first contains single measurements on three subjects.
# We can first take a look at the data
example_data1
# Next, compute predictions.
# Since there is no ID column, predict_dominance() will print a message telling
# the user that the rows are assumed to contain observations from different subjects.
predict_dominance(example_data1)
# The next example dataset contains repeated measurements
example_data2
# We compute the predictions as before:
predict_dominance(example_data2)

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