chngpt (version 2016.7-9)

sim.chngpt: Simulation Function

Description

Generate simulation datasets for change point Monte Carlo studies.

Usage

sim.chngpt( label=c("sigmoid2","sigmoid3","sigmoid4","sigmoid5","sigmoid6", "quadratic","exp","flatHyperbolic"),  n, seed,  type=c("NA","step","hinge","segmented","stegmented"), family=c("binomial","gaussian"), beta=NULL, coef.z=log(1.4), alpha=NULL, x.distr=c("norm","norm3","norm6","imb","lin","mix","gam","zbinary"),      e.=NULL, b.=-Inf, sd=0.3, alpha.candidate=NULL, verbose=FALSE)

Arguments

label

string. Simulate scenario, see details.

type

string. Types of threshold effect to simulate, only applicable when label does not start with sigmoid.

family

string. Glm family.

n

seed

beta

coef.z

numeric. Coefficient for z.

alpha

numeric, intercept.

x.distr

string. Possible values: norm (normal distribution), gam (gamma distribution)

e.

verbose

Boolean

b.

sd

alpha.candidate

candidate values of alpha, used in code to determine alpha values

Value

A data frame with following columns:

y

0/1 outcome

x

observed covariate that we are interested in

x.star

unobserved covariate that underlies x

z

additional covariate

In addition, columns starting with 'w' are covariates that we also adjust in the model; columns starting with 'x' are covariates derived from x.

Details

When label is "sigmoid1", an intercept only model is the data generative model. When label is "sigmoid2", a binary covariate z is also part of the data generative model.