calc_weights: Calculate Precision Weights From Gamma Regression

In PhilipBerg/PaiR: Imputation and Significance Analysis of Proteomics Data

Calculate Precision Weights From Gamma Regression

Description

Calculates the precision weights for each measurement.

Usage

calc_weights(data, gamma_reg_model)

Arguments

data	A data.frame who's quantitative columns should be converted to precision weights.
gamma_reg_model	a glm object as produced by fit_gamma_regressions or fit_gamma_weights, see Mean-Variance_Gamma_Regressions for details. Any glm object with formula sd ~ mean is valid.

Details

calc_weights takes as input a data frame and a glm object produced by e.g., fit_gamma_regressions or fit_gamma_weights, see Mean-Variance_Gamma_Regressions for details. For all numeric columns, it predicts the standard deviation using the gamma regression. It then squares and takes the reciprocal of each value to generate the precision weights.

Value

The same data.frame but with all quantitative values replaced by their precision weights.

Examples

# Generate a design matrix for the data
design <- model.matrix(~ 0 + factor(rep(1:2, each = 3)))

# Set correct colnames, this is important for fit_gamma_weights
colnames(design) <- paste0("ng", c(50, 100))

# Normalize and log-transform the data
yeast <- psrn(yeast, "identifier")

# Fit the gamma regression model for the mean-variance trend
gamma_model <- fit_gamma_weights(yeast, design, "identifier")

# Generate the weights for the yeast data
calc_weights(yeast, gamma_model)

PhilipBerg/PaiR documentation built on March 18, 2022, noon