analyze_time_bins() — analyze_time

Runs a test on each time-bin of time_sequence_data. Supports t.test, wilcox.test, (g)lm, and (g)lmer. Also includes support for the "bootstrapped-splines" test (see ?make_boot_splines_data and the divergence vignette for more info). By default, this function uses 'proportion-looking' (Prop) as the DV, which can be changed by manually specifying the formula. Results can be plotted to see how test-results or parameters estimates vary over time. P-values can be adjusted for multiple comparisons with p_adjust_method.

analyze_time_bins(data, ...)

# S3 method for time_sequence_data
analyze_time_bins(
  data,
  predictor_column,
  test,
  threshold = NULL,
  alpha = NULL,
  aoi = NULL,
  formula = NULL,
  treatment_level = NULL,
  p_adjust_method = "none",
  quiet = FALSE,
  ...
)

Arguments

data: The output of the 'make_time_sequence_data' function
...: Any other arguments to be passed to the selected 'test' function (e.g., paired, var.equal, etc.)
predictor_column: The variable whose test statistic you are interested in. If you are not interested in a predictor, but the intercept, you can enter "intercept" for this argument. Interaction terms are not currently supported.
test: What type of test should be performed in each time bin? Supports t.test, wilcox.test, (g)lm, and (g)lmer. Also includes support for the "bootstrapped-splines" test (see ?make_boot_splines_data and the divergence vignette for more info).
threshold: Value of statistic used in determining significance
alpha: Alpha value for determining significance, ignored if threshold is given
aoi: Which AOI should be analyzed? If not specified (and dataframe has multiple AOIs), then AOI should be a predictor/covariate in your model (so `formula` needs to be specified).
formula: What formula should be used for the test? Optional for all but (g)lmer, if unset will use Prop ~ [predictor_column]. Change this if you want to use a custom DV.
treatment_level: If your predictor is a factor, regression functions like `lm` and `lmer` by default will treatment-code it. One option is to sum-code this predictor yourself before entering it into this function. Another is to use the `treatment_level` argument, which specifies the level of the predictor. For example, you are testing a model where `Target` is a predictor, which has two levels, 'Animate' and 'Inanimate'. R will code 'Animate' as the reference level, and code 'Inanimate' as the treatment level. You'd therefore want to set `treatment_level = Inanimate`.
p_adjust_method: Method to adjust p.values for multiple corrections (default="none"). See p.adjust.methods.
quiet: Should messages and progress bars be suppressed? Default is to show

Value

A dataframe indicating the results of the test at each time-bin.

Methods (by class)

analyze_time_bins(time_sequence_data):

Examples

if (FALSE) {
data(word_recognition)
data <- make_eyetrackingr_data(word_recognition,
                               participant_column = "ParticipantName",
                               trial_column = "Trial",
                               time_column = "TimeFromTrialOnset",
                               trackloss_column = "TrackLoss",
                               aoi_columns = c('Animate','Inanimate'),
                               treat_non_aoi_looks_as_missing = TRUE
)
response_time <- make_time_sequence_data(data, time_bin_size = 250,
                                         predictor_columns = c("MCDI_Total"),
                                         aois = "Animate", summarize_by = "ParticipantName")
tb_analysis <- analyze_time_bins(response_time, predictor_column = "MCDI_Total",
                                 test = "lm", threshold = 2)
summary(tb_analysis)
}