check_cor {PUMP}

Check correlation of test statistics (simulation function)

Description

Estimates the pairwise correlations between test statistics for all outcomes.

Takes in two options: - a pumpresult object OR - a list of necessary data-generating parameters - the context (d_m) - Tbar

Note that this function can take several minutes to run.

Usage

check_cor(
  pump.object = NULL,
  rho.V = NULL,
  rho.w0 = NULL,
  rho.w1 = NULL,
  rho.X = NULL,
  rho.u0 = NULL,
  rho.u1 = NULL,
  rho.C = NULL,
  rho.r = NULL,
  d_m = NULL,
  model.params.list = NULL,
  Tbar = 0.5,
  n.sims = 100
)

Arguments

pump.object

A pumpresult object.

rho.V

matrix; correlation matrix of level 3 covariates.

rho.w0

matrix; correlation matrix of level 3 random effects.

rho.w1

matrix; correlation matrix of level 3 random impacts.

rho.X

matrix; correlation matrix of level 2 covariates.

rho.u0

matrix; correlation matrix of level 2 random effects.

rho.u1

matrix; correlation matrix of level 2 random impacts.

rho.C

matrix; correlation matrix of level 1 covariates.

rho.r

matrix; correlation matrix of level 1 residuals.

d_m

string; a single context, which is a design and model code. See pump_info() for list of choices.

model.params.list

list; model parameters such as ICC, R2, etc. See simulation vignette for details.

Tbar

scalar; the proportion of samples that are assigned to the treatment.

n.sims

numeric; Number of simulated datasets to generate. More datasets will achieve a more accurate result but also increase computation time.

Value

matrix; M x M correlation matrix between test statistics.

Examples

pp <- pump_power( d_m = "d3.2_m3ff2rc",
                  MTP = "BF",
                  MDES = rep( 0.10, 2 ),
                  M = 2,
                  J = 4, # number of schools/block
                  K = 10, # number RA blocks
                  nbar = 50,
                  Tbar = 0.50, # prop Tx
                  alpha = 0.05, # significance level
                  numCovar.1 = 5, numCovar.2 = 3,
                  R2.1 = 0.1, R2.2 = 0.7,
                  ICC.2 = 0.05, ICC.3 = 0.4,
                  rho = 0.4, # how correlated test statistics are
                  tnum = 200
)
cor.tstat <- check_cor(
    pump.object = pp, n.sims = 4
)
est.cor <- mean(cor.tstat[lower.tri(cor.tstat)])