## File: catL2BB.Rd

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strucchange 1.5-1-2
 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116 \name{catL2BB} \alias{catL2BB} \alias{ordL2BB} \alias{ordwmax} \title{Generators for efpFunctionals along Categorical Variables} \description{ Generators for \code{efpFunctional} objects suitable for aggregating empirical fluctuation processes to test statistics along (ordinal) categorical variables. } \usage{ catL2BB(freq) ordL2BB(freq, nproc = NULL, nrep = 1e5, probs = c(0:84/100, 850:1000/1000), \dots) ordwmax(freq, algorithm = mvtnorm::GenzBretz(), \dots) } \arguments{ \item{freq}{object specifying the category frequencies for the categorical variable to be used for aggregation: either a \code{\link{gefp}} object, a \code{\link{factor}}, or a numeric vector with either absolute or relative category frequencies.} \item{nproc}{numeric. Number of processes used for simulating from the asymptotic distribution (passed to \code{\link{efpFunctional}}). If \code{feq} is a \code{\link{gefp}} object, then its number of processes is used by default.} \item{nrep}{numeric. Number of replications used for simulating from the asymptotic distribution (passed to \code{\link{efpFunctional}}).} \item{probs}{numeric vector specifying for which probabilities critical values should be tabulated.} \item{\dots}{further arguments passed to \code{\link{efpFunctional}}.} \item{algorithm}{algorithm specification passed to \code{\link[mvtnorm]{pmvnorm}} for computing the asymptotic distribution.} } \details{ Merkle, Fan, and Zeileis (2014) discuss three functionals that are suitable for aggregating empirical fluctuation processes along categorical variables, especially ordinal variables. The functions \code{catL2BB}, \code{ordL2BB}, and \code{ordwmax} all require a specification of the relative frequencies within each category (which can be computed from various specifications, see arguments). All of them employ \code{\link{efpFunctional}} (Zeileis 2006) internally to set up an object that can be employed with \code{\link{gefp}} fluctuation processes. \code{catL2BB} results in a chi-squared test. This is essentially the LM test counterpart to the likelihood ratio test that assesses a split into unordered categories. \code{ordL2BB} is the ordinal counterpart to \code{\link{supLM}} where aggregation is done along the ordered categories (rather than continuously). The asymptotic distribution is non-standard and needs to be simulated via \code{\link[mvtnorm]{rmvnorm}} for every combination of frequencies and number of processes. This can be somewhat time-consuming, hence it is recommended to store the result of \code{ordL2BB} in case it needs to be applied several \code{\link{gefp}} fluctuation processes. \code{ordwmax} is a weighted double maximum test based on ideas previously suggested by Hothorn and Zeileis (2008) in the context of maximally selected statistics. The asymptotic distribution is (multivariate) normal and computed by means of \code{\link[mvtnorm]{pmvnorm}}. } \value{ An object of class \code{efpFunctional}. } \references{ Hothorn T., Zeileis A. (2008), Generalized Maximally Selected Statistics. \emph{Biometrics}, \bold{64}, 1263--1269. Merkle E.C., Fan J., Zeileis A. (2014), Testing for Measurement Invariance with Respect to an Ordinal Variable. \emph{Psychometrika}, \bold{79}(4), 569--584. doi:10.1007/S11336-013-9376-7. Zeileis A. (2006), Implementing a Class of Structural Change Tests: An Econometric Computing Approach. \emph{Computational Statistics & Data Analysis}, \bold{50}, 2987--3008. doi:10.1016/j.csda.2005.07.001. } \seealso{\code{\link{efpFunctional}}, \code{\link{gefp}}} \examples{ ## artificial data set.seed(1) d <- data.frame( x = runif(200, -1, 1), z = factor(rep(1:4, each = 50)), err = rnorm(200) ) d$y <- rep(c(0.5, -0.5), c(150, 50)) * d$x + d\$err ## empirical fluctuation process scus <- gefp(y ~ x, data = d, fit = lm, order.by = ~ z) ## chi-squared-type test (unordered LM-type test) LMuo <- catL2BB(scus) plot(scus, functional = LMuo) sctest(scus, functional = LMuo) ## ordinal maxLM test (with few replications only to save time) maxLMo <- ordL2BB(scus, nrep = 10000) plot(scus, functional = maxLMo) sctest(scus, functional = maxLMo) ## ordinal weighted double maximum test WDM <- ordwmax(scus) plot(scus, functional = WDM) sctest(scus, functional = WDM) } \keyword{regression}