w4m_filter_median_imputation: Impute missing intensities to median for W4M data matrix

In HegemanLab/w4mclassfilter: W4M Class Filter

Description

Substitute median feature intensity (across all samples) for missing values and zero for negative values in W4M data matrix

Usage

w4m_filter_median_imputation(m)

Arguments

m	matrix: W4M data matrix potentially containing NA or negative values

Value

matrix: input data matrix with zeros substituted for negative values and median substituted for missing values

Author(s)

Art Eschenlauer, [e-mail]

See Also

https://github.com/HegemanLab/w4mclassfilter

http://workflow4metabolomics.org/

Examples

# input contains negative and missing values
my_input <- matrix(c(NA,-1,3,2), ncol = 2, nrow = 2)

# expected output converts negative and missing values to zero
my_expected <- matrix(c(3,0,3,2), ncol = 2, nrow = 2)

# run the imputation method to generate actual output
my_output <- w4m_filter_median_imputation(my_input)

# validate actual output against expected output
all.equal(my_output, my_expected, check.attributes = FALSE)

HegemanLab/w4mclassfilter documentation built on March 14, 2021, 1:19 a.m.