View source: R/GBOP2_minSS_dualE.gbop2.R
GBOP2_minSS_dualE | R Documentation |
This function implements PSOGO to find an optimal or minimax design with dual boundaries.
GBOP2_minSS_dualE(
design = "optimal",
unified.u = unified.u,
weight = 1,
nlooks = 1,
p0 = 0.2,
p1 = 0.4,
err1 = 0.05,
minPower = 0.8,
maxPatients = 5,
Nmin_cohort1 = 1,
Nmin_increase = 1,
pso_method = "default",
nParallel = NULL,
seed = 123,
nSwarm = 1,
maxIter = 1
)
design |
choose from "optimal", "minimax", or "unified" |
unified.u |
specify when design = "unified", u in zero to one |
weight |
weight of sample size under null |
nlooks |
number of interim looks |
p0 |
Null hypothesis response rate |
p1 |
Alternative hypothesis response rate |
err1 |
Type I error rate |
minPower |
power |
maxPatients |
maximum number of patients |
Nmin_cohort1 |
minimum number of first cohort |
Nmin_increase |
minimum number of increase in each cohort |
pso_method |
"all" for using three distinct pso, otherwise indicate single pso method |
nParallel |
number of pso ensemble |
seed |
seed for pso |
nSwarm |
nSwarm for pso |
maxIter |
maxIter for pso |
Parallel computing is only used when the user explicitly sets nCore > 1. No more than 2 cores should be used
unless the user is aware and permits it. The function defaults to sequential execution. If multiple analyses
are planned, consider using init_cluster(nCore)
and stop_cluster()
manually to control the backend.
A list on design parameters and operating characteristics
# init_cluster(2)
# GBOP2_minSS_dualE(
# design = "optimal",
# unified.u = unified.u,
# nlooks = 1,
# p0 = 0.2,
# p1 = 0.4,
# err1 = 0.05,
# minPower = 0.8,
# weight = 1,
# maxPatients = 25,
# Nmin_cohort1 = 10,
# Nmin_increase = 5,
# pso_method = "default",
# nParallel = 3,
# seed = 123,
# nSwarm = 64,
# maxIter = 200
# )
# stop_cluster() # Only if init_cluster() was used
#
message("Run GBOP2_minSS_dualE() manually for real optimization.")
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