pval_type_1_err: Estimate type I error rate

In OchoaLab/simtrait: Simulate Complex Traits from Genotypes

Estimate type I error rate

Description

Given a significance level and p-values with known causal status, this function estimates the type I error rate, defined as the proportion of null p-values that are below or equal to the threshold. Note that these simple empirical estimates are likely to be zero unless the number of p-values is much larger than 1/alpha.

Usage

pval_type_1_err(pvals, causal_indexes, alpha = 0.05)

Arguments

pvals	The vector of association p-values to analyze. This function assumes all p-values are provided (a mix of null and alternative tests). NA values are allowed in input and removed. Non-NA values outside of [0, 1] will trigger an error.
causal_indexes	The vector of causal indexes, whose p-values will be omitted. Values of causal_indexes as returned by sim_trait work. This parameter is required to prevent use of this function except when the true status of every test (null vs alternative) is known. Set to NULL if all loci are truly null (non-causal). Otherwise, causal_indexes must have at least one causal index.
alpha	The desired significance level (default 0.05). May be a vector.

Value

The type I error rate estimates at each alpha

See Also

pval_srmsd() to directly quantify null p-value uniformity, a more robust alternative to type I error rate.

pval_infl() for the more traditional inflation factor, which focuses on the median of the full distribution (combination of causal and null cases).

Examples

# simulate truly null p-values, which should be uniform
pvals <- runif(10)
# for toy example, take the first p-value to be truly causal (will be ignored below)
causal_indexes <- 1
# estimate desired measure
pval_type_1_err( pvals, causal_indexes )

OchoaLab/simtrait documentation built on April 19, 2024, 7:36 p.m.