Estimate bias using the compositional least-squares approach described in McLaren, Willis, and Callahan (2019).

estimate_bias(observed, actual, ...)

# S3 method for matrix
estimate_bias(observed, actual, margin, boot = FALSE, times = 1000)

# S3 method for otu_table
estimate_bias(observed, actual, ...)

# S3 method for phyloseq
estimate_bias(observed, actual, ...)

Arguments

observed

Abundance matrix of observed compositions..

actual

Abundance matrix of actual or reference compositions for the same samples and taxa in observed.

...

Arguments passed to the matrix method.

margin

Matrix margin that corresponds to observations (samples); 1 for rows, 2 for columns.

boot

Whether to perform bootstrapping.

times

Number of bootstrap replicates.

Value

A mc_bias_fit object with coef(), fitted(), residuals(), and summary() methods.

Details

Bias is estimated by applying center() to the compositional error matrix defined by observed/actual, which requires that observed and actual are non-zero for the same sample-taxa pairs. For convenience, this function will automatically set values in observed to 0 whose corresponding entries are 0 in actual, but it is up to you to replace 0 values in observed with a non-zero value (such as a pseudocount).

Name requirements for observed and actual: The row and column names (for matrices) or taxa and sample names (for phyloseq objects) must match, but can be in different orders.

See also