ranksProb_compare: Compare rank probabilities
In mshasan/OPWpaper1: Reproduce figures and simulations of OPWeight

Description Usage Arguments Details Value Author(s) References See Also Examples

OPWeight package proposed a method to compute the ranks probabilities of the covariate given the test-effect sizes from three approaches: simualation, exact formula, and normal approximation. This funciton uses the methods to compare the ranks probabilities from the three approahes

1 2	ranksProb_compare(ey, e.one, m0, m1, sampleSize, effectType = c("continuous", "binary"))

`ey`	Numerics, mean covariate-effect size
`e.one`	Numeric, one test effect which will vary across all tests
`m0`	Integer, number of true null tests
`m1`	Integer, number of true alternative tests
`sampleSize`	Integer, total number of sample generated (use sample size at least 100,000)
`effectType`	Character ("continuous" or "binary"), type of effect sizes

The OPWeight package proposed methods to compute the ranks probabilitiesof the covariate given the test effect size. This funciton uses the methods to compare the rank probabilities from three approahes: 1) simulation, 2) exact formula, and 3) normal approximation

The lower rank may generate missing values because of the large effcet sizes. This is particularly true for the simulaiton approach. however, matplot function requires equal sized vectors. This procedure will replace the missing values by NA so that the vectors size become equal.

Data A data frame containing the seven columns; the ranks and the corresponding ranks probability of the true null and the true alternative hypothesis of the three approaches.

Mohamad S. Hasan, shakilmohamad7@gmail.com

Hasan and Schliekelman (2017)

prob_rank_givenEffect_simu prob_rank_givenEffect_exact prob_rank_givenEffect_approx

# use sample size at least 100,000 for better result
# This is just an example
sampleSize = 1000
probData <- ranksProb_compare(ey = 1, e.one = 2, m0 = 5, m1 = 5,
                         sampleSize = sampleSize, effectType = "binary")

# plots------------
# colnames(probData) <- c("ranks", "SH0","SH1","EH0","EH1","AH0","AH1")
# matplot(probData[, 1], probData[, 2:5], type = "l", lty = 1:6, col =1:6,
# lwd = 2, xlab = "ranks", ylab = "P(rank | effect)")
# legend("topright", legend = c("SH0","SH1","EH0","EH1","AH0","AH1"),
#               lty = 1:6, col =1:6, lwd = 2)