compute_timewise_statistics {jlmerclusterperm}

Fit Julia regression models to each time point of a time series data

Description

Fit Julia regression models to each time point of a time series data

Usage

compute_timewise_statistics(
  jlmer_spec,
  family = c("gaussian", "binomial"),
  statistic = c("t", "chisq"),
  ...
)

Arguments

jlmer_spec

Data prepped for jlmer from make_jlmer_spec()

family

A GLM family. Currently supports "gaussian" and "binomial".

statistic

Test statistic for calculating cluster mass. Can be one of "t" (default) from the regression model output or "chisq" from a likelihood ratio test (takes about twice as long to calculate).

...

Optional arguments passed to Julia for model fitting. Defaults to fast = TRUE (when family = "binomial") and progress = FALSE.

Value

A predictor-by-time matrix of cluster statistics, of class timewise_statistics.

See Also

jlmer(), make_jlmer_spec()

Examples

library(dplyr, warn.conflicts = FALSE)

# Specification object
spec <- make_jlmer_spec(
  weight ~ 1 + Diet, filter(ChickWeight, Time <= 20),
  subject = "Chick", time = "Time"
)
spec

# Predictor x Time matrix of t-statistics from regression output
empirical_statistics <- compute_timewise_statistics(spec)
round(empirical_statistics, 2)

# Collect as dataframe with `tidy()`
empirical_statistics_df <- tidy(empirical_statistics)
empirical_statistics_df

# Timewise statistics are from regression models fitted to each time point
# - Note the identical statistics at `Time == 0`
empirical_statistics_df %>%
  filter(time == 0)
to_jlmer(weight ~ 1 + Diet, filter(ChickWeight, Time == 0)) %>%
  tidy() %>%
  select(term, statistic)