generate.pvalues {DiscreteFDR}

Generation of P-Values and Their Supports After Data Transformations

Description

Simple wrapper for generating p-values of discrete tests and their supports after pre-processing the input data. The user only has to provide 1.) a function that generates p-values and supports and 2.) an optional function that pre-processes (i.e. transforms) the input data (if necessary) before it can be used for p-value calculations. The respective arguments are provided by named lists.

Usage

generate.pvalues(
  dat,
  test.fun,
  test.args = NULL,
  preprocess.fun = NULL,
  preprocess.args = NULL
)

Arguments

dat

input data; must be suitable for the first parameter of the provided preprocess.fun function or, if preprocess.fun is NULL, for the first parameter of the test.fun function.

test.fun

function from package DiscreteTests, i.e. one whose name ends with ⁠*.test.pv⁠ and which performs hypothesis tests and provides an object with p-values and their support sets; can be specified by a single character string (which is automatically checked for being a suitable function from that package and may be abbreviated) or a single function object.

test.args

optional named list with arguments for test.fun; the names of the list fields must match the test function's parameter names. The first parameter of the test function MUST NOT be included!

preprocess.fun

optional function for pre-processing the input data; its result must be suitable for the first parameter of the test.fun function.

preprocess.args

optional named list with arguments for preprocess.fun; the names of the list fields must match the pre-processing function's parameter names. The first parameter of the test function MUST NOT be included!

Value

A DiscreteTestResults R6 class object.

Examples

X1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1)
X2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2)
N1 <- rep(148, 9)
N2 <- rep(132, 9)
Y1 <- N1 - X1
Y2 <- N2 - X2
df <- data.frame(X1, Y1, X2, Y2)
df

# Compute p-values and their supports of Fisher's exact test
test.result <- generate.pvalues(df, "fisher")
raw.pvalues <- test.result$get_pvalues()
pCDFlist <- test.result$get_pvalue_supports()

# Compute p-values and their supports of Fisher's exact test with pre-processing
df2 <- data.frame(X1, N1, X2, N2)
generate.pvalues(
  dat = df2,
  test.fun = "fisher.test.pv",
  preprocess.fun = function(tab) {
    for(col in c(2, 4)) tab[, col] <- tab[, col] - tab[, col - 1]
    return(tab)
  }
)

# Compute p-values and their supports of a binomial test with pre-processing
generate.pvalues(
  dat = rbind(c(5, 2, 7), c(3, 4, 0)), 
  test.fun = "binom.test.pv",
  test.args = list(n = c(9, 8, 11), p = 0.6, alternative = "two.sided"),
  preprocess.fun = colSums
)