sim.chngpt {chngpt}

Simulation Function

Description

Generate simulation datasets for change point Monte Carlo studies.

Usage

sim.chngpt (mean.model = c("thresholded", "thresholdedItxn",
 "quadratic", "quadratic2b", "cubic2b", "exp",
 "flatHyperbolic", "z2", "z2hinge", "z2segmented",
 "z2linear", "logistic"), threshold.type = c("NA",
 "M01", "M02", "M03", "M10", "M20", "M30", "M11",
 "M21", "M12", "M22", "M22c", "M31", "M13", "M33c",
 "hinge", "segmented", "upperhinge", "segmented2",
 "step", "stegmented"), b.transition = Inf, family =
 c("binomial", "gaussian"), x.distr = c("norm",
 "norm3", "norm6", "imb", "lin", "mix", "gam",
 "zbinary", "gam1", "gam2", "fixnorm", "unif"), e. =
 NULL, mu.x = 4.7, sd.x = NULL, sd = 0.3, mu.z = 0,
 alpha = NULL, alpha.candidate = NULL, coef.z =
 log(1.4), beta = NULL, beta.itxn = NULL,
 logistic.slope = 15, n, seed, weighted = FALSE,
 heteroscedastic = FALSE, ar = FALSE, verbose = FALSE)

sim.twophase.ran.inte(threshold.type, n, seed)

sim.threephase(n, seed, gamma = 1, e = 3, beta_e = 5, f = 7, beta_f = 2, coef.z = 1)

Arguments

threshold.type

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

family

string. Glm family.

n

n

mu.z

n

seed

seed

weighted

beta

beta

beta

coef.z

numeric. Coefficient for z.

beta.itxn

numeric. Coefficient for z.

alpha

numeric, intercept.

mu.x

numeric

sd.x

numeric

mean.model

numeric

x.distr

string. Possible values: norm (normal distribution), gam (gamma distribution). gam1 is a hack to allow e. be different

e.

e.

verbose

Boolean

b.transition

b.

sd

b.

ar

autocorrelation

alpha.candidate

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

e

e

beta_e

beta_e

f

f

beta_f

beta_f

logistic.slope

beta_f

gamma

beta_f

heteroscedastic

Boolean.

Details

mean.model, threshold.type and b.transition all affect mean models.

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.

Examples

seed=2
par(mfrow=c(2,2))
dat=sim.chngpt(mean.model="thresholded", threshold.type="hinge", family="gaussian", beta=0, n=200, 
    seed=seed, alpha=-1, x.distr="norm", e.=4, heteroscedastic=FALSE)
plot(y~z, dat)
dat=sim.chngpt(mean.model="thresholded", threshold.type="hinge", family="gaussian", beta=0, n=200, 
    seed=seed, alpha=-1, x.distr="norm", e.=4, heteroscedastic=TRUE)
plot(y~z, dat)
dat=sim.chngpt(mean.model="z2", threshold.type="hinge", family="gaussian", beta=1, n=200, 
    seed=seed, alpha=1, x.distr="norm", e.=4, heteroscedastic=FALSE)
plot(y~z, dat)
dat=sim.chngpt(mean.model="z2", threshold.type="hinge", family="gaussian", beta=1, n=200, 
    seed=seed, alpha=1, x.distr="norm", e.=4, heteroscedastic=TRUE)
plot(y~z, dat)