data_analysis: Emperical ranks probbaility of the test given the effect size
In mshasan/empOPW: Empirical Method of Optimal Pvalue Weighting

Description Usage Arguments Details Value Author(s) Examples

View source: R/data_analysis.R

Emperical comnputation of the ranks probability of a test being higher than any other test given the effect size from the external information.

1 2	data_analysis(alpha, pvalue, filter, N_current = 1L, N_prior = 1L, tail, max.group, standarized = FALSE, effectType = c("continuous", "binary"))

`alpha`	Nmeric, significance level of the hypothesis test
`pvalue`	a vector of pvalues of the test statistics
`filter`	a vector of filter statistics
`N_current`	Integer vector, number of observations per test in the current data
`N_prior`	Integer vector, number of observations per test in the prior data
`tail`	right-tailed or two-tailed hypothesis test. default is right-tailed test.
`max.group`	maximum number of groups to be used to split the p-values, default is five. Note that, it is better to keep approximately 1000 p-values per group.
`standarized`	Character of c("TRUE" or "FALSE") determine whether standarization is required. Default is FALSE means filter is a vector of zscore. Thus, standarization will not used.
`effectType`	Character of type ("binary" or"continuous") of effect sizes

Perform data analysis for the different methods such as proposed, bonferroni, Benjamini and Hoghburgh, IHW, and Dorbibian methods.

rejections A numeric vector of the number of rejected test of the different methods.

Mohamad S. Hasan, shakilmohamad7@gmail.com

# generating data (known in practice)
set.seed(123)
m = 10000
X = runif(m, min = 0, max = 2.5)               # covariate
H = rbinom(length(X), size = 1, prob = 0.1)   # hypothesis true or false
Z = rnorm(length(X), mean = H * X)            # Z-score
p = 1 - pnorm(Z)
rejections <- data_analysis(alpha = .1, pvalue = p, filter = X, N_current = m,
         N_prior = m, tail = 2, max.group = 10, effectType = "continuous")