This function performs the runs test for randomness. Users can choose whether to plot the correlation graph or not, and whether to test against two-sided, negative or positive correlation. NAs from the data are omitted. An empirical distribution may be used for the distribution of the test statistic under the null hypothesis of independence.
Usage
runsTest(
y,
plot.it = FALSE,
alternative = c("two.sided", "positive.correlated", "negative.correlated"),
emp.distribution = NULL
)Arguments
- y
a numeric vector of data values
- plot.it
logical flag. If 'TRUE' then the graph will be plotted. If 'FALSE', then it is not plotted.
- alternative
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "negative.correlated" or "positive.correlated"
- emp.distribution
vector containing the empirical distribution of test statistics under the nyll hypothesis. Generated using
getEmpDistribution.
Value
A list with the following components.
statistic the value of the standardized Runs statistic.
p.value the p-value for the test.
data.name a character string giving the names of the data.
alternative a character string describing the alternative hypothesis.
Details
On the graph observations which are less than the sample median are represented by letter "A" in red color, and observations which are greater or equal to the sample median are represented by letter "B" in blue color.
References
Mendenhall, W (1982), Statistics for Management and Economics, 4th Ed., 801-807, Duxbury Press, Boston.
J.L. Gastwirth; Y.R. Gel, W. L. Hui, V. Lyubchich, W. Miao and K. Noguchi (2015). lawstat: Tools for Biostatistics, Public Policy, and Law. R package version 3.0
Examples
data(ns.data.re)
model<-gamMRSea(birds ~ observationhour + as.factor(floodebb) + as.factor(impact),
family='poisson', data=ns.data.re)
runsTest(residuals(model))
#>
#> Runs Test - Two sided
#>
#> data: residuals(model)
#> Standardized Runs Statistic = -69.695, p-value < 2.2e-16
#>
## Empirical distribution:
simData<-generateNoise(n=500, response=fitted(model), family='poisson')
empdist<-getEmpDistribution(500, simData, model, data=ns.data.re, plot=FALSE,
returnDist=TRUE,dots=FALSE)
runsTest(residuals(model), emp.distribution=empdist)
#>
#> Runs Test - Two sided; Empirical Distribution
#>
#> data: residuals(model)
#> Standardized Runs Statistic = -69.695, p-value < 2.2e-16
#>