| L_corr {likelihoodR} | R Documentation |
Likelihood Support for Correlation
Description
This function calculates the support for a correlation from 2 vectors of data. An expected correlation can be specified and the support calculated for this relative to the observed and the null (which is assumed to be 0, but can also be specified) values. A likelihood function is plotted for the obtained correlation with a likelihood interval added and expected correlation, if specified. Conventional p value is also given.
Usage
L_corr(xv, yv, null=0, exp.r=NULL, L.int=2, alpha=.05,
toler=0.0001, logplot=FALSE, supplot=-10, verb=TRUE)
Arguments
xv |
a numeric vector. |
yv |
a numeric vector the same length as xv. |
null |
the null value, default = 0. |
exp.r |
a specified correlation (could be expected value for the study), default = NULL. |
L.int |
likelihood interval given as support values, e.g. 2 or 3, default = 2. |
alpha |
the significance level used, 1 - alpha interval calculated, default = 0.05. |
toler |
the desired accuracy using optimise, default = 0.0001. |
logplot |
plot vertical axis as log likelihood, default = FALSE |
supplot |
set minimum likelihood display value in plot, default = -10 |
verb |
show output, default = TRUE. |
Value
$obs.r - observed correlation.
$S.0 - support for observed correlation versus the null.
$S.1 - support for the specified correlation versus observed correlation.
$S.10 - support for the specified correlation versus the null value.
$exp.r - the specified correlation.
$N - the sample size.
$p.value - the p value for significance test versus 0.
$like.int - the likelihood interval.
$like.int.spec - the specified likelihood interval in terms of support.
$conf.int - the % confidence interval for the correlation.
$alpha.spec - the specified alpha for the % confidence interval.
References
Cahusac, P.M.B. (2020) Evidence-Based Statistics, Wiley, ISBN : 978-1119549802
Royall, R. M. (1997). Statistical evidence: A likelihood paradigm. London: Chapman & Hall, ISBN : 978-0412044113
Edwards, A.W.F. (1992) Likelihood, Johns Hopkins Press, ISBN : 978-0801844430
Examples
# for heptathlon example, p 104
m200 <- c(22.6, 23.7, 23.1, 23.6, 23.6, 23.6, 25.5,
23.9, 24.5, 23.9, 24.9, 24.8, 24.7,
25.0, 24.6, 24.9, 25.0, 25.6, 24.8,
25.5, 25.7, 24.9, 26.6, 25.2, 26.2)
m800 <- c(128.5, 126.1, 124.2, 132.5,
134.7, 132.5, 138.5, 127.9, 133.7, 132.2,
136.1, 142.8, 125.8, 131.5, 137.1, 134.9,
146.7, 133.9, 146.4, 144.0, 133.4,
138.0, 139.2, 137.3, 163.4)
m=L_corr(m200, m800, null=0, exp.r=.4, L.int=3, alpha=.05,
toler=0.0001, logplot=FALSE, supplot=-10, verb=TRUE)
m
#Note: the support for observed vs 0 is different from book (5.776 vs 5.700)
#due to differences in calculation of r by Excel and R