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#' Check logging resolution by looking at timestamp differences.
#'
#' \code{mt_check_resolution} computes the timestamp differences as a measure of
#' the logging resolution. It provides various descriptive statistics to check
#' the logging resolution.
#'
#' If mouse-tracking experiments are conducted using the mousetrap plug-ins for
#' OpenSesame, the logging resolution can be specified explicitly in the
#' experiment under "Logging resolution", which corresponds to the delay (in
#' milliseconds) between recordings of the mouse position. By default, mouse
#' positions are recorded every 10 ms (corresponding to a 100 Hz sampling rate).
#' As the actual resolution achieved depends on the performance of the hardware,
#' it makes sense to check the logging resolution using
#' \code{mt_check_resolution}. Note that delays smaller than the specified delay
#' typically result from mouse clicks in the experiment.
#'
#'
#' @inheritParams mt_time_normalize
#' @param timestamps a character string specifying the trajectory dimension
#' containing the timestamps.
#' @param desired an optional integer. If specified, additional statistics are
#' computed concerning the (relative) frequencies with which exactly the
#' desired timestamp difference (with tolerance 1e-12) occurred.
#'
#' @return A list with various descriptive statistics. For convenience, the
#' relative frequencies are rounded to 4 decimal places.
#'
#' @examples
#' mt_check_resolution(mt_example)
#'
#' @author
#' Pascal J. Kieslich (\email{kieslich@@psychologie.uni-mannheim.de})
#'
#' Felix Henninger
#'
#' @export
mt_check_resolution <- function(data, use="trajectories",
timestamps="timestamps", desired=NULL) {
trajectories <- extract_data(data=data,use=use)
# Compute steps in the timestamps
if(dim( trajectories )[1] == 1) {
log_diffs <- diff(trajectories[, , timestamps])
} else {
log_diffs <- diff(t(trajectories[, , timestamps]))
}
# Clean data type and remove empty values
log_diffs <- as.numeric(log_diffs)
log_diffs <- log_diffs[!is.na(log_diffs)]
results <- list(
summary=summary(log_diffs),
sd=stats::sd(log_diffs),
frequencies=table(log_diffs),
relative_frequencies=round(
table(log_diffs) / length(log_diffs), 4)
)
if (!is.null(desired)){
log_diffs_class <-
cut(log_diffs,
c(0,desired-10^(-12),desired+10^(-12),Inf),
c("smaller","desired","greater")
)
results <-
c(results,
list(
frequencies_desired=table(log_diffs_class),
relative_frequencies_desired=round(
table(log_diffs_class) / length(log_diffs_class), 4)
))
}
return(results)
}
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