pval_pu: Obtain an asymptotic variance matrix of theta, standard...

In RomeroLab/pudms: Positive-Unlabeled Learning for the Analysis of Deep Mutational Scanning Datasets

Description

Obtain an asymptotic variance matrix of theta, standard errors, zvalues, and pvalues

Usage

pval_pu(X, z, theta, py1, weights = rep(1, nrow(X)), effective_n_prop = 1)

Arguments

X	an n by p matrix
z	an n by 1 vector
theta	a p+1 by 1 vector
py1	a numeric value
weights	an n by 1 vector
effective_n_prop	a numeric value

Value

a list (I = expected Fisher matrix, invI = inverse of I, se = sqrt(diag(invI)), zvalue, pvalue)

RomeroLab/pudms documentation built on Jan. 2, 2021, 5:10 a.m.