Generate simulation datasets for change point Monte Carlo studies.
1 2 3 4 5 6 7 8 9 10 11  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)

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 
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.
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.
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