Test for differences in a cosinor model between components. — test_cosinor

Given a time variable and optional covariates, generate inference a cosinor fit. For the covariate named (or vector of covariates), this function performs a Wald test comparing the group with covariates equal to 1 to the group with covariates equal to 0. This may not be the desired result for continuous covariates.

Usage

test_cosinor_components(
  x,
  x_str,
  param = "amp",
  comparison_A = 1,
  comparison_B = 2,
  level_index = 0,
  ci_level = 0.95
)

Arguments

x: An cglmm object.
x_str: A character. The name of the grouping variable within which differences in the selected cosinor characteristic (amplitude or acrophase) will be tested.
param: A character. Either "amp" or "acr" for testing differences in amplitude or acrophase, respectively.
comparison_A: An integer. Refers to the component number that is to act as the reference group. for the comparison.
comparison_B: An integer. Refers to the component number that is to act as the comparator group
level_index: An integer. If comparison_type = "components", level_index indicates which level of the grouping variable is being used for the comparison between components.
ci_level: The level for calculated confidence intervals. Defaults to 0.95.

Value

Returns a test_cosinor object.

Examples

data_2_component <- simulate_cosinor(
  n = 10000,
  mesor = 5,
  amp = c(2, 5),
  acro = c(0, pi),
  beta.mesor = 4,
  beta.amp = c(3, 4),
  beta.acro = c(0, pi / 2),
  family = "gaussian",
  n_components = 2,
  period = c(10, 12),
  beta.group = TRUE
)
mod_2_component <- cglmm(
  Y ~ group + amp_acro(times,
    n_components = 2, group = "group",
    period = c(10, 12)
  ),
  data = data_2_component
)
test_cosinor_components(mod_2_component, param = "amp", x_str = "group")
#> Test Details: 
#> Parameter being tested:
#> Amplitude
#> 
#> Comparison type:
#> components
#> 
#> Component indices used for comparison between groups: group
#> Reference component: 1
#> Comparator component: 2
#> 
#> 
#> Global test: 
#> Statistic: 
#> 2169.12
#> 
#> P-value: 
#> 0
#> 
#> 
#> Individual tests:
#> Statistic: 
#> 46.57
#> 
#> P-value: 
#> 0
#> 
#> Estimate and 95% confidence interval:
#> 3.02 (2.89 to 3.15)