sim_power: Power analysis using Monte Carlo simulation

In phuchonguyen/mpower: Power Analysis via Monte Carlo Simulation for Correlated Data

Power analysis using Monte Carlo simulation

Description

Power analysis using Monte Carlo simulation

Usage

sim_power(
  xmod,
  ymod,
  imod,
  s = 100,
  n = 100,
  cores = 1,
  file = NULL,
  errorhandling = "stop",
  snr_iter = 10000,
  cluster_export = c()
)

Arguments

xmod	A MixtureModel object.
ymod	An OutcomeModel object.
imod	An InferenceModel object.
s	An integer for number of Monte Carlo simulations.
n	An integer for sample size in each simulation.
cores	An integer for number of processing cores. When cores > 1, parallelism is automatically applied.
file	A string, a file name with no extension to write samples to periodically. By default write to an RDS file.
errorhandling	A string "remove", "stop", or "pass". If an error occurs in any iteration, remove that iteration (remove), return the error message verbatim in the output (pass), or terminate the loop (stop). Default is "remove". See R package 'foreach' for more details.
snr_iter	An integer for number of Monte Carlo samples to estimate SNR
cluster_export	A vector of functions to pass to the parallel-processing clusters

Value

A PowerSim object. Attributes:

s	a number of simulations.
snr	a real number for SNR of the OutcomeModel.
n	a number of sample sizes.
xmod	the MixtureModel used.
ymod	the OutcomeModel used.
imod	the InferenceModel used.
sims	an output matrices.

Examples

data("nhanes1518")
chems <- c("URXCNP", "URXCOP", "URXECP", "URXHIBP", "URXMBP", "URXMC1",
"URXMCOH", "URXMEP","URXMHBP", "URXMHH", "URXMHNC", "URXMHP", "URXMIB",
"URXMNP", "URXMOH", "URXMZP")
chems_mod <- mpower::MixtureModel(nhanes1518[, chems], method = "resampling")
bmi_mod <- mpower::OutcomeModel(f = "0.2*URXCNP + 0.15*URXECP +
0.1*URXCOP*URXECP", family = "binomial")
logit_mod <- mpower::InferenceModel(model = "glm", family = "binomial")
logit_out <- mpower::sim_power(xmod=chems_mod, ymod=bmi_mod, imod=logit_mod,
s=100, n=2000, cores=2, snr_iter=2000)
logit_df <- summary(logit_out, crit="pval", thres=0.05, how="lesser")
plot_summary(logit_out, crit="pval", thres=0.05, how="lesser")

phuchonguyen/mpower documentation built on Oct. 2, 2022, 7:57 p.m.