| L_1way_cat {likelihoodR} | R Documentation |
Likelihood Support for One-way Categorical Data
Description
This function calculates the support for one-way categorical data (multinomial), also gives chi-squared and likelihood ratio test (G) statistics. If there are only 2 categories then binomial information is given too with likelihood interval, including the likelihood-based % confidence interval. Support for the variance being more different than expected (Edwards p 187, Cahusac p 158) is also calculated. It uses the optimize function to locate desired limits for both intervals.
Usage
L_1way_cat(obs, exp.p=NULL, L.int=2, alpha=0.05, toler=0.0001,
logplot=FALSE, supplot=-10, verb=TRUE)
Arguments
obs |
a vector containing the number of counts in each category. |
exp.p |
a vector containing expected probabilities. If NULL then this is 1/#cats. |
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
$S.val - support for one-way observed versus expected.
$uncorrected.sup - uncorrected support.
$df - degrees of freedom for table.
$observed - observed counts.
$exp.p - expected probabilities.
$too.good - support for the variance of counts being more different than expected.
$chi.sq - chi-squared value.
$p.value - p value for chi-squared.
$LR.test = the likelihood ratio test statistic.
$lrt.p = the p value for the likelihood ratio test statistic
Additional outputs for binomial:
$prob.val - MLE probability from data.
$succ.fail - number of successes and failures.
$like.int - likelihood interval.
$like.int.spec - specified likelihood interval in units of support.
$conf.int - likelihood-based confidence interval.
$alpha.spec - specified alpha for confidence interval.
$err.acc - error accuracy for optimize function.
References
Aitkin, M. et al (1989) Statistical Modelling in GLIM, Clarendon Press, ISBN : 978-0198522041
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
# example for binomial, p 123
obs <- c(6,4)
L_1way_cat(obs, L.int=2, toler=0.0001, logplot=FALSE, supplot=-10, verb = TRUE)
# example for multinomial, p 134
obs <- c(60,40,100)
exp <- c(0.25,0.25,0.5)
L_1way_cat(obs, exp.p=exp, L.int=2, toler=0.0001, logplot=FALSE, supplot=-10,
verb = TRUE)