scan (version 0.64.0)

estimate_design: Estimate single-case design

Description

This functions takes an scdf and extracts design parameters. The resulting object can be used to randomly create new scdf files with the same underlying parameters. This is useful for Monte-Carlo studies and bootstrapping procedures.

Usage

estimate_design(
  data,
  dvar,
  pvar,
  mvar,
  s = NULL,
  rtt = NULL,
  overall_effects = FALSE,
  overall_rtt = TRUE,
  model = "JW",
  ...
)

Value

A list of parameters for each single-case. Parameters include name, length, and starting measurement time of each phase, trend, level, and slope effects for each phase, start value, standard deviation, and reliability for each case.

Arguments

data

A single-case data frame. See scdf() to learn about this format.

dvar

Character string with the name of the dependent variable. Defaults to the attributes in the scdf file.

pvar

Character string with the name of the phase variable. Defaults to the attributes in the scdf file.

mvar

Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file.

s

The standard deviation depicting the between case variance of the overall performance. If more than two single-cases are included in the scdf, the variance is estimated if s is set to NULL.

rtt

The reliability of the measurements. The reliability is estimated when rtt = NULL.

overall_effects

If TRUE, trend, level, and slope effect estimations will be identical for each case. If FALSE, effects are estimated for each case separately.

overall_rtt

Ignored when rtt is set. If TRUE, rtt estimations will be based on all cases and identical for each case. If FALSE rtt is estimated for each case separately.

model

Model used for calculating the dummy parameters (see Huitema & McKean, 2000). Default is model = "W". Possible values are: "B&L-B", "H-M", "W", and deprecated "JW".

...

Further arguments passed to the plm function used for parameter estimation.

Examples

# create a random scdf with predefined parameters
set.seed(1234)
design <- design(
  n = 10, trend = -0.02,
  level = list(0, 1), rtt = 0.8,
  s = 1
)
scdf<- random_scdf(design)

# Estimate the parameters based on the scdf and create a new random scdf
# based on these estimations
design_est <- estimate_design(scdf, rtt = 0.8)
scdf_est <- random_scdf(design_est)

# Analyze both datasets with an hplm model. See how similar the estimations
# are:
hplm(scdf, slope = FALSE)
hplm(scdf_est, slope = FALSE)

# Also similar results for pand and randomization tests:
pand(scdf)
pand(scdf_est)
rand_test(scdf)
rand_test(scdf_est)