dodPwr: Power of the Degree-of-Difference (DOD) method

In sensR: Thurstonian Models for Sensory Discrimination

Power of the Degree-of-Difference (DOD) method

Description

Computes the power of the Degree-of-Difference (DOD) method by simulation

Usage

dodPwr(d.primeA, d.prime0=0, ncat = 4, sample.size, nsim = 1e3,
       alpha = 0.05,
       method.tau=c("LR.max", "equi.prob", "se.min", "user.defined"),
       statistic=c("likelihood", "Wilcoxon", "Pearson", "Wald"),
       alternative = c("difference", "similarity", "two.sided",
       "less", "greater"),
       tau=NULL, ...)

Arguments

d.primeA	the value of d-prime under the alternative hypothesis; non-negative numerical scalar.
d.prime0	the value of d-prime under the null hypothesis.
ncat	the number of response categories in the DOD model
sample.size	the sample size in each simulation for each of the same-pairs and different pairs. Can be a single scalar value or a 2-vector.
nsim	the number of simulations.
alpha	the significance level.
method.tau	the method with which to choose the boundary parameters - see dodSim for details on the methods.
statistic	the statistic to be used for hypothesis testing.
alternative	the nature of the alternative hypothesis in the hypothesis/significance test for d-prime. Note that "greater" is an alias for "difference" and "less" is an alias for "similarity".
tau	if method.tau = "user.defined" a vector of boundary parameters in the DOD model, otherwise not used.
...	parsed on to wilcox.test when appropriate.

Value

The simulation based estimate of the power with the following attributes:

se(power)	the estimated standard error of the estimated power. This is based on the formula sqrt(pow * (1 - pow) / n), where pow is the estimated power and n is the number of simulations used to estimate the power.
n.used	the number of simulations used to estimate the power. This is usually equal to nsim, but can sometimes be smaller than nsim due to non-convergences to which the Wald test is especially prone.

Author(s)

Rune Haubo B Christensen

References

Ennis, J.M. and R.H.B. Christensen (2015) A Thurstonian comparison of the tetrad and degree of difference tests. Food Quality and Preference, 40, pp.263-269.

See Also

dod, dod_fit, dodSim, optimal_tau, dodControl

Examples

## NOTE: The number of simulations (nsim) is set unrealistically low in
## the examples below to reduce the computation time for automatic
## package checks. nsim between 1e3 and 1e4 is usually sufficient and
## the latter often on the safe side. The standard error of the
## estimated power ('se(power)') reported by dodPwr() measures the
## accuracy of the estimated power and indicates if nsim needs to be
## increased.

## Estimate power of the conventional difference test (no-difference
## under the null hypothesis):
set.seed(125)
dodPwr(d.primeA=1, d.prime0=0, ncat=4, sample.size=100, nsim=50,
       alpha=.05, method.tau="LR.max", statistic="likelihood")
##  [1] 0.62
##  attr(,"se(power)")
##  [1] 0.1825346
##  attr(,"n.used")
##  [1] 50
## Here the boundary parameters are chosen automatically so as to
## maximize the likelihood ratio test statistic, and so this setting
## amounts to a highest achievable power scenario given d-prime = 1.

## Using another (and faster) statistic:
dodPwr(d.primeA=1, d.prime0=0, ncat=4, sample.size=100, nsim=1e3,
       alpha=.05, method.tau="LR.max", statistic="Wilcox")


## Not automatically run to reduce computation time.

## Power of a similarity test:
set.seed(127)
dodPwr(d.primeA=0, d.prime0=1, ncat=4, sample.size=100, nsim=1e2,
       alpha=.05, method.tau="LR.max", statistic="Pearson",
       alternative="similarity")
##  [1] 0.71
##  attr(,"se(power)")
##  [1] 0.1434922
##  attr(,"n.used")
##  [1] 100

## Same as above, but with a given set of boundary parameters:
dodPwr(d.primeA=0, d.prime0=1, sample.size=100, nsim=1e2,
       alpha=.05, method.tau="user.defined", statistic="Pearson",
       alternative="similarity", tau=1:3)

## Using parallel computing to speed up computations:
if(require(parallel)) {
    ## Use detectCores() to get an appropriate number of cores for
    ## practical use - for the example here we fix it at 2:
    ## cl <- makeCluster(detectCores())
    cl <- makeCluster(getOption("cl.cores", 2))
    dvec <- c(0, .2, .5, .7, 1, 1.2, 1.5, 1.75)
    system.time(
        res <- parLapply(cl, dvec, fun=function(dp) {
            library(sensR)
            x <- dodPwr(dp, 0, sample.size=100, nsim=1e4, stat="Wil")
            c("power"=x, "se"=attr(x, "se(power)"))
        })
        )
    stopCluster(cl)
    names(res) <- dvec
    mat <- do.call(cbind, res)
    round(mat[1:2, ], 3)
    ## Example output:
    ##            0   0.2   0.5   0.7     1   1.5  1.75     2
    ##  power 0.051 0.058 0.123 0.238 0.578 0.983 1.000 1.000
    ##  se    0.022 0.023 0.033 0.043 0.049 0.013 0.002 0.001
}

## Realistically one should use more simulations, e.g. nsim=1e4.

sensR documentation built on Nov. 2, 2023, 6:02 p.m.