pump_sample_raw: Calculating Needed Sample Size for Raw (Unadjusted) Power

Description

This is a helper function for getting a needed Sample Size when no adjustments has been made to the test statistics.

Usage

pump_sample_raw(
  d_m,
  MTP,
  typesample,
  MDES,
  nbar = NULL,
  J = NULL,
  K = NULL,
  target.power,
  Tbar,
  alpha = 0.05,
  two.tailed,
  numCovar.1 = 0,
  numCovar.2 = 0,
  numCovar.3 = 0,
  R2.1,
  R2.2 = NULL,
  R2.3 = NULL,
  ICC.2 = NULL,
  ICC.3 = NULL,
  omega.2 = NULL,
  omega.3 = NULL,
  max.steps = 100,
  warn.small = FALSE
)

Value

Requisite sample size (as integer) and associated degrees of freedom.

Arguments

d_m: string; a single context, which is a design and model code. See pump_info() for list of choices.
MTP: string, or vector of strings; multiple testing procedure(s). See pump_info() for list of choices.
typesample: type of sample size to calculate: J, K, or nbar
MDES: scalar or vector; the desired MDES values for each outcome. Please provide a scalar, a vector of length M, or vector of values for non-zero outcomes.
nbar: scalar; the harmonic mean of the number of level 1 units per level 2 unit (students per school). Note that this is not the total number of level 1 units, but instead the number of level 1 units nested within each level 2 unit, so the total number of level 1 units is nbar x J x K.
J: scalar; the harmonic mean of number of level 2 units per level 3 unit (schools per district). Note that this is not the total number of level 2 units, but instead the number of level 2 units nested within each level 3 unit, so the total number of level 2 units is J x K.
K: scalar; the number of level 3 units (districts).
target.power: target power to arrive at
Tbar: scalar; the proportion of samples that are assigned to the treatment.
alpha: scalar; the family wise error rate (FWER).
two.tailed: scalar; TRUE/FALSE for two-tailed or one-tailed power calculation.
numCovar.1: scalar; number of level 1 (individual) covariates.
numCovar.2: scalar; number of level 2 (school) covariates.
numCovar.3: scalar; number of level 3 (district) covariates.
R2.1: scalar, or vector of length M; percent of variation explained by level 1 covariates for each outcome.
R2.2: scalar, or vector of length M; percent of variation explained by level 2 covariates for each outcome.
R2.3: scalar, or vector of length M; percent of variation explained by level 3 covariates for each outcome.
ICC.2: scalar, or vector of length M; level 2 (school) intraclass correlation.
ICC.3: scalar, or vector length M; level 3 (district) intraclass correlation.
omega.2: scalar, or vector of length M; ratio of variance of level 2 average impacts to variance of level 2 random intercepts.
omega.3: scalar, or vector of length M; ratio of variance of level 3 average impacts to variance of level 3 random intercepts.
max.steps: how many steps allowed before terminating
warn.small: Warn if degrees of freedom issues are causing inability to achieve target power for sample size.

Details

It is for a single, individual outcome. It only takes scalar values for all its arguments, and does not have an M argument (for number of outcomes).

It requires iteration because we do not know the degrees of freedom, and so we guess and then calculate sample size, and then recalculate df based on sample size, until we converge.

It is possible that the returned sample size will be the minimum sample size required to have at least 1 degree of freedom (even if this provides higher than target level power).