npRmpi (version 0.60-20)

npdeneqtest: Kernel Consistent Density Equality Test with Mixed Data Types

Description

npdeneqtest implements a consistent integrated squared difference test for equality of densities as described in Li, Maasoumi, and Racine (2009).

Usage

npdeneqtest(x = NULL,
            y = NULL,
            bw.x = NULL,
            bw.y = NULL,
            boot.num = 399,
            random.seed = 42,
            ...)

Value

npdeneqtest returns an object of type deneqtest with the following components

Tn

the (standardized) statistic Tn

In

the (unstandardized) statistic In

Tn.bootstrap

contains the bootstrap replications of Tn

In.bootstrap

contains the bootstrap replications of In

Tn.P

the P-value of the Tn statistic

In.P

the P-value of the In statistic

boot.num

number of bootstrap replications

summary supports object of type deneqtest.

Arguments

x,y

data frames for the two samples for which one wishes to test equality of densities. The variables in each data frame must be the same (i.e. have identical names).

bw.x,bw.y

optional bandwidth objects for x,y

boot.num

an integer value specifying the number of bootstrap replications to use. Defaults to 399.

random.seed

an integer used to seed R's random number generator. This is to ensure replicability. Defaults to 42.

...

additional arguments supplied to specify the bandwidth type, kernel types, and so on. This is used if you do not pass in bandwidth objects and you do not desire the default behaviours. To do this, you may specify any of bwscaling, bwtype, ckertype, ckerorder, ukertype, okertype.

Author

Tristen Hayfield , Jeffrey S. Racine

Usage Issues

If you are using data of mixed types, then it is advisable to use the data.frame function to construct your input data and not cbind, since cbind will typically not work as intended on mixed data types and will coerce the data to the same type.

It is crucial that both data frames have the same variable names.

Details

npdeneqtest computes the integrated squared density difference between the estimated densities/probabilities of two samples having identical variables/datatypes. See Li, Maasoumi, and Racine (2009) for details.

References

Li, Q. and E. Maasoumi and J.S. Racine (2009), “A Nonparametric Test for Equality of Distributions with Mixed Categorical and Continuous Data,” Journal of Econometrics, 148, pp 186-200.

See Also

npdeptest,npsdeptest,npsymtest,npunitest

Examples

if (FALSE) {
## Not run in checks: excluded to keep MPI examples stable and check times short.
## The following example is adapted for interactive parallel execution
## in R. Here we spawn 1 slave so that there will be two compute nodes
## (master and slave).  Kindly see the batch examples in the demos
## directory (npRmpi/demos) and study them carefully. Also kindly see
## the more extensive examples in the np package itself. See the npRmpi
## vignette for further details on running parallel np programs via
## vignette("npRmpi",package="npRmpi").

## Start npRmpi for interactive execution. If slaves are already running and
## `options(npRmpi.reuse.slaves=TRUE)` (default on some systems), this will
## reuse the existing pool instead of respawning. To change the number of
## slaves, call `npRmpi.stop(force=TRUE)` then restart.
npRmpi.start(nslaves=1)

mpi.bcast.cmd(set.seed(42),
              caller.execute=TRUE)

n <- 100

sample.A <- data.frame(x=rnorm(n))
sample.B <- data.frame(x=rnorm(n))

mpi.bcast.Robj2slave(sample.A)
mpi.bcast.Robj2slave(sample.B)

mpi.bcast.cmd(output <- npdeneqtest(sample.A,sample.B,boot.num=29),
              caller.execute=TRUE)

output


## For the interactive run only we close the slaves perhaps to proceed
## with other examples and so forth. This is redundant in batch mode.

## Note: on some systems (notably macOS+MPICH), repeatedly spawning and
## tearing down slaves in the same R session can lead to hangs/crashes.
## npRmpi may therefore keep slave daemons alive by default and
## `npRmpi.stop()` performs a "soft close". Use `force=TRUE` to
## actually shut down the slaves.
##
## You can disable reuse via `options(npRmpi.reuse.slaves=FALSE)` or by
## setting the environment variable `NP_RMPI_NO_REUSE_SLAVES=1` before
## loading the package.

npRmpi.stop()               ## soft close (may keep slaves alive)
## npRmpi.stop(force=TRUE)  ## hard close

## Note that in order to exit npRmpi properly avoid quit(), and instead
## use mpi.quit() as follows.

## mpi.bcast.cmd(mpi.quit(),
##               caller.execute=TRUE)
}