sim_pIC50: Simulate pIC50 value

In chapmandu2/pgxsim: Simulate Dose Response Curves For Pharmacogenomics Analysis

Description

Uses the truncated normal distribution from the msm package to simulate a pIC50 value for a cell line. Incorporates genetic covariate information.

Usage

sim_pIC50(mu, sd, beta, g, n = 1, type = "d", lb = -4, ub = Inf)

Arguments

mu	mean of the truncated normal distribution
sd	standard deviation of the truncated normal distribution
beta	value of the genetic coefficient to recover - note that this is on a log scale
g	value of the gene (0/1 for discrete or Z-value for continuous)
n	number of data points to generate (default is 1)
type	Whether genetic covariate is a discrete (d) or continuous (c) variable
lb	lower bound of the pIC50 distribution
ub	upper bound of the pIC50 distribution

Value

numeric

Examples

library(dplyr)
set.seed(10000)
sim_pIC50(0, 1, 0, 0, type='d')
sim_pIC50(0, 1, 0, 0, n=10, type='d')
sim_cell_lines(n=10, type='d', prop=0.2) %>%
 dplyr::mutate(pIC50=sim_pIC50(0,1,beta=1,gene, n=n(), type='d'))

chapmandu2/pgxsim documentation built on May 6, 2019, 10:13 a.m.