ucip_test: A Statistical Test for Super or Limited Capacity

In sft: Functions for Systems Factorial Technology Analysis of Data

Description

A nonparametric test for capacity values significantly different than those predicted by the estimated unlimited capacity, independent parallel model.

Usage

ucip.test(RT, CR=NULL, OR=NULL, stopping.rule=c("OR","AND","STST"))

Arguments

RT	A list of response time arrays. The first array in the list is assumed to be the exhaustive condition.
CR	A list of correct/incorrect indicator arrays. If NULL, assumes all are correct.
OR	Indicates whether to compare performance to an OR or AND processing baseline. Provided for backwards compatibility for package version < 2.
stopping.rule	Indicates whether to compare performance to an OR, AND or Single Target Self Terminating (STST) processing baseline.

Details

The test is based on the Nelson-Aalen formulation of the log-rank test. The function takes a weighted difference between estimated unlimited capacity, independent parallel performance, based on a participants single source response times, and the participants true performance when all sources are present.

Value

A list of class "htest" containing:

statistic	Z-score of a null-hypothesis test for UCIP performance.
p.val	p-value of a two-tailed null-hypothesis test for UCIP performance.
alternative	A description of the alternative hypothesis.
method	A string indicating whether the ART or ANOVA was used.
data.name	A string indicating which data were used for which input.

Author(s)

Joe Houpt <[e-mail]>

References

Houpt, J.W. & Townsend, J.T. (2012). Statistical Measures for Workload Capacity Analysis. Journal of Mathematical Psychology, 56, 341-355.

See Also

capacity.or capacity.and estimateUCIPor estimateUCIPand estimateNAH estimateNAK

Examples

rate1 <- .35
rate2 <- .3
RT.pa <- rexp(100, rate1)
RT.ap <- rexp(100, rate2)

CR.pa <- runif(100) < .98
CR.ap <- runif(100) < .98
CR.pp <- runif(100) < .96
CRlist <- list(CR.pp, CR.pa, CR.ap)

#  OR Processing
RT.pp.limited <- pmin( rexp(100, .5*rate1), rexp(100, .5*rate2))
RT.pp.unlimited <- pmin( rexp(100, rate1), rexp(100, rate2))
RT.pp.super <- pmin( rexp(100, 2*rate1), rexp(100, 2*rate2))
z.limited   <- ucip.test(RT=list(RT.pp.limited, RT.pa, RT.ap), CR=CRlist, stopping.rule="OR")
z.unlimited <- ucip.test(RT=list(RT.pp.unlimited, RT.pa, RT.ap), CR=CRlist, stopping.rule="OR")
z.super     <- ucip.test(RT=list(RT.pp.super, RT.pa, RT.ap), CR=CRlist, stopping.rule="OR")

#  AND Processing
RT.pp.limited <- pmax( rexp(100, .5*rate1), rexp(100, .5*rate2))
RT.pp.unlimited <- pmax( rexp(100, rate1), rexp(100, rate2))
RT.pp.super <- pmax( rexp(100, 2*rate1), rexp(100, 2*rate2))
z.limited   <- ucip.test(RT=list(RT.pp.limited, RT.pa, RT.ap), CR=CRlist, stopping.rule="AND")
z.unlimited <- ucip.test(RT=list(RT.pp.unlimited, RT.pa, RT.ap), CR=CRlist, stopping.rule="AND")
z.super     <- ucip.test(RT=list(RT.pp.super, RT.pa, RT.ap), CR=CRlist, stopping.rule="AND")

Example output

Loading required package: fda
Loading required package: splines
Loading required package: Matrix

Attaching package: 'fda'

The following object is masked from 'package:graphics':

    matplot

sft documentation built on May 2, 2019, 6:04 a.m.