knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(MetaP)
This function takes between 2 and 5 Data Frames and calculates p-values for each Biomarker to perform one of the 4 pooling tests below on.
The possible pooling method arguments are:
"fisher" for Fisher
"stouffer" for Stouffer
"min" for Minimum P-value
"max" for Maximum P-value
Usage: Project(x1, x2, x3, x4, x5, test)
This function checks if the inputs are vaild to use in our main function Checks that: All inputs are lists/ Data Frames Same number of columns in each Data Frame Rows 2 to p are biomarkers Checks that the group membership column will take at least two unique values
Usage: Check.input(frames)
This function takes a Data Frame that can be normally distributed or not normally distributed. For 2 groups we will perform a two sample t-test or Wilcoxon rank sum test based on Normality. For more than 2 groups we will perform ANOVA or Kruskal Wallis test based on Normality.
Usage: GroupDifference(x)
The return is an output of pvalues for each biomarker
This function sums the log-transformed p-values, following a chi-squared distributions with 2k degrees of freedom
Usage: pool.fisher(x)
The return is a vector of p-values, one for each biomarker
This function sums the inverse normal p-values, following a standard normal distribution
Usage: pool.stouffer(x)
The return is a vector of p-values, one for each biomarker
This function follows a beta distribution with degrees of freedom α=1 and β=k
Usage: pool.min(x)
The return is a vector of p-values, one for each biomarker
This function follows a beta distribution with degrees of freedom α=k and β=1
Usage: pool.max(x)
The return is a vector of p-values, one for each biomarker
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.