chngpt (version 2016.7-9)

chngpt.test: Change Point Tests

Description

Hypothesis testing for change point covariate in logistic regression and linear regression.

Usage

chngpt.test (formula.null, formula.chngpt, family=c("binomial","gaussian"), data,  type=c("step","hinge","segmented","stegmented"), main.method=c("lr","score"), chngpts=NULL, lb.quantile=.1, ub.quantile=.9,  chngpts.cnt=50, # this is set to 25 if int is weighted.two.sided or weighted.one.sided single.weight=1, mc.n=5e4,  prob.weights=NULL, compute.p.value=TRUE, verbose=FALSE 
)

antoch.test (formula, data, chngpt.var, plot.=FALSE)
"plot"(x, by.percentile=TRUE, both=FALSE, main=NULL, ...)

Arguments

formula.null

formula for the null model. The outcome needs to be a 0/1 variable.

formula.chngpt

formula for the change point model. For example, suppose formula.null=y~z and we want to test whether I(x>cutff) is a significant predictor, formula.chngpt=~x If instead we are interested in testing the null that neither I(x>cutff) nor z*I(x>cutff) is a significant predictor, formula.chngpt=~x*z

data

data frame.

family

Currently only linear and logistic regression are supported.

type

step: flat before and after change point; hinge: flat before and slope after change point; segmented: slope before and after change point

main.method

method for testing main effects of some threshold model.

chngpts

A grid of potential change points to maximize over. If not supplied, they will be set to a vector of length chngpt.cnt equally spaced between lb.quantile and ub.quantile.

lb.quantile

number. The lower bound in the search for change point in the unit of quantile.

ub.quantile

number. The upper bound in the search for change point in the unit of quantile.

chngpts.cnt

integer. Number of potential change points to maximize over.

mc.n

integer. Number of multivariate normal samples to generate in the Monte Carlo procedure to evaluate p-value.

single.weight

numeric. Only used when interaction.method="weighted.single.arg".

prob.weights

numeric. Not yet fully supported.

compute.p.value

Boolean. Computing p values takes a lot of time. Sometimes we just want the maximal statistic and change point associated with it.

verbose

Boolean.

chngpt.var

string. Name of the predictor to detect change point

plot.

Boolean. Whether to make a plot.

formula

formula.

x

An object of type chngpt.test.

...

arguments passed to or from methods

by.percentile

both

main

Value

A list of class htest and chngpt.test

p.value

P-value

chngpts

Vector of change points evaluated

TT

Standardized absolute score statistics

V.S.hat

Estimated variance-covariance matrix of the score statistics

Details

If there are missing values in the chngpt formula, those rows will be removed from the whole dataset, including null model and chngpt model.

The test results, e.g. p-value, should not change if the same function is called twice, because internally seed to random number generator is set to 1 before MC and set back to the R state afterwards

antoch.test is only implemented for main effect only and is based on Antoch et al. (2004). Also see Fong et al. (2014).

References

Fong, Y., Huang, Y., Gilbert, P. (2015) Estimation and hypothesis testing under four types of change point models using chngpt, submitted.

Fong Y, Di C, and Permar S. (2014) Change-Point Testing in Logistic Regression Models with Interaction Term. Statistics in Medicine.

Pastor-Barriuso, R. and Guallar, E. and Coresh, J. (2003) Transition models for change-point estimation in logistic regression. Statistics in Medicine. 22:13141

Antoch, J. and Gregoire, G. and Jaruskova, D. (2004) Detection of structural changes in generalized linear models. Statistics and probability letters. 69:315

Examples

dat=sim.chngpt("sigmoid4", type="step", n=200, seed=1, beta=1, alpha=-1, x.distr="norm", e.=4)

test=chngpt.test(formula.null=y~z, formula.chngpt=~x, dat, type="step", family="binomial")
test
plot(test)


## Not run: 
# # not run because otherwise the examples take >5s and that is a problem for R CMD check
# # has interaction
# test = chngpt.test(formula.null=y~z, formula.chngpt=~x*z, dat, type="step")
# test
# plot(test)
# ## End(Not run)

#antoch.test(y~1, dat, "x.star")