Description Usage Arguments Value Author(s) References Examples
Calculates the R statistic for count data, which is a ratio of the empirical variance to the expected binomial variance. Can take a mix of data of different sizes (i.e., different n in a Bin(n,p) distribution). Also runs the Meelis and James tests, testing for under or over dispersion.
1 2 | Meelis.test(experiment, TwoSided = FALSE)
James.test(experiment, TwoSided = FALSE)
|
experiment |
The dataset. Should be a matrix with 2 columns. The first column contains the counts of the clutch sizes, and the second column contains the counts of the number of males in each clutch. |
TwoSided |
logical; if TRUE, a two-sided test is performed. If we only want to test for under-disperion, a one-sided test could be used. |
vals |
Intermediate values used in the calculation of the other higher-level statistics |
R.av |
The descriptive R ratio which is the ratio of empirical and expected variance |
s2 |
McCullagh's $s^2$ statistic |
U.av |
The U test statistic as described in Wilkinson et al. 2013. |
p.av |
The p-value corresponding to the U statistic, calculated as part of either a one or two-sided calculation depending on the option specified. |
exp.table |
The experimental data organised into table format. |
Richard Wilkinson
Krackow et al. 2002, in Hardy 2002, Sex Ratios: Concepts and Research Methods, CUP.
1 2 3 | data(GlegneriSecondary)
Meelis.test(GlegneriSecondary, TwoSided=TRUE)
James.test(GlegneriSecondary, TwoSided=TRUE)
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