Description Usage Arguments Value Examples
This is an implementation of an alternative to Person Correlation test as
implemented by cor.test
. It accepts the same arguments as
cor.test
. For more information regarding the model assumptions see:
http://sumsar.net/blog/2014/03/bayesian-first-aid-pearson-correlation-test/
1 2 3 4 5 6 7 8 9 10 | bayes.cor.test(x, ...)
## Default S3 method:
bayes.cor.test(x, y, alternative = c("two.sided", "less",
"greater"), method = c("pearson", "kendall", "spearman"), exact = NULL,
cred.mass = 0.95, continuity = FALSE, n.iter = 15000,
progress.bar = "text", conf.level, ...)
## S3 method for class 'formula'
bayes.cor.test(formula, data, subset, na.action, ...)
|
x, y |
numeric vectors of data values. |
... |
not used |
alternative |
ignored and is only retained in order to mantain
compatibility with |
method |
ignored |
exact |
ignored |
cred.mass |
the amount of probability mass that will be contained in
reported credible intervals. This argument fills a similar role as
|
continuity |
ignored |
n.iter |
The number of iterations to run the MCMC sampling. |
progress.bar |
The type of progress bar. Possible values are "text", "gui", and "none". |
conf.level |
same as |
formula |
a formula of the form |
data |
an optional matrix or data frame containing the variables in the
formula |
subset |
an optional vector specifying a subset of observations to be used. |
na.action |
a function which indicates what should happen when the data
contain NAs. Defaults to |
A list of class bayes_cor_test
that contains information about
the analysis. It can be further inspected using the functions
summary
, plot
, diagnostics
and
model.code
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Data from Hollander & Wolfe (1973), p. 187f.
# This example is borrowed from the documentation for cor.test().
# Assessment of tuna quality. We compare the Hunter L measure of
# lightness to the averages of consumer panel scores (recoded as
# integer values from 1 to 6 and averaged over 80 such values) in
# 9 lots of canned tuna.
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8)
# First a classical correlation test:
cor.test(x, y)
# And here is the Bayesian first aid alternative:
bayes.cor.test(x, y)
# Save the output into a variable for easy plotting and further inspection:
fit <- bayes.cor.test(x, y)
plot(fit)
summary(fit)
model.code(fit)
|
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