View source: R/test_Rfunction.R
evalPower | R Documentation |
Returns powers and a plot of the chosen test and method under different treatment effects.
evalPower(n, cov_num, level_num, pr, type, beta, di = seq(0,0.5,0.1), sigma = 1,
Iternum, sl = 0.05, method = c("HuHuCAR", "PocSimMIN", "StrBCD", "StrPBR",
"DoptBCD","AdjBCD"),
test = c("boot.test", "corr.test", "rand.test"), plot = TRUE, ...)
n |
the number of patients. |
cov_num |
the number of covariates. |
level_num |
a vector of level numbers for each covariate. Hence the length of |
pr |
a vector of probabilities. Under the assumption of independence between covariates, |
type |
a data-generating method. Optional input: |
beta |
a vector of coefficients of covariates. The length of |
di |
a value or a vector of values of difference in treatment effects. The default value is a sequence from |
sigma |
the error variance for the linear model. The default is 1. This should be a positive value and is only used when |
Iternum |
an integer. It is the number of iterations required for power calculation. |
sl |
the significance level. If the |
method |
the randomization procedure to be used for power calculation. This package provides power calculation for |
test |
a character string specifying the alternative tests used to verify hypothesis, must be one of |
plot |
a bool. It indicates whether to plot or not. Optional input: |
... |
arguments to be passed to
|
This function returns a list. The first element is a dataframe representing the powers of the chosen test under different values of treatment effects. The second element is the execution time. An optional element is the plot of power in which di
forms the vertical axis.
##settings
set.seed(2019)
n = 100#<<for demonstration,it is suggested to be larger than 1000
cov_num = 5
level_num = c(2,2,2,2,2)
pr = rep(0.5,10)
beta = c(0.1,0.4,0.3,0.2,0.5,0.5,0.4,0.3,0.2,0.1)
omega = c(0.1, 0.1, rep(0.8 / 5, times = 5))
di = seq(0,0.5,0.1)
sigma = 1
type = "linear"
p = 0.85
Iternum = 10#<<for demonstration,it is suggested to be around 1000
sl = 0.05
Reps = 10#<<for demonstration,it is suggested to be 200
#Evaluation of Power
library("ggplot2")
Strtp=evalPower(n,cov_num,level_num,pr,type,beta,di,sigma,
Iternum,sl,"HuHuCAR","rand.test",TRUE,omega,p,Reps, nthreads = 1)
Strtp
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