csCompare
resultsPerform a sensitivity analysis for the Bayes factors computed
with the csCompare
results
csSensitivity(cs1, cs2, group = NULL, data = NULL,
alternative = "two.sided", conf.level = 0.95, mu = 0,
rscaleSens = c(0.707, 1, 1.41), out.thres = 3)
a numeric vector of values. If the data
argument is
defined, it can refer to either the column index or the column name of
the data object. See Details
for more information.
a numeric vector of values. If the data
argument is
defined, it can refer to either the column index or the column name of
the data object. See Details
for more information.
column index or name that contain the group data. See
Details
for more information.
numeric matrix or data frame that contains the relevant data.
a character string for the speficication of
the alternative hypothesis. Possible values: "two.sided"
(default),
"greater"
or "less"
.
Interval's confidence level.
a numeric value for the mean value or mean difference.
the scale factor for the prior used in the Bayesian t.test
The threeshold for detecting outliers (default is 3). If set
to 0, no outliers analysis will be performed. See Details
below for
more information.
The function returns a data frame with the results of the student t-test and the Bayesian t-test.
csCompare
performs both a student t-test (using the
stats::t.test
function) and a Bayesian t-test (using the
BayesFactor::ttest.tstat
). In case group
is not defined,
paired-samples t-tests are run. In case the group
is
defined, then the csCompare first computes difference scores between the cs1
and the cs2
(i.e., cs1 - cs2).
In case the group argument is defined
but, after removal of NA's (stats::na.omit
), only one group
is defined, a paired samples t-test is run.
Krypotos, A.-M., Klugkist, I., & Engelhard, I. M. (submitted).Bayesian Hypothesis Testing for Human Threat Conditioning Research: An introduction and the condir R package.
# NOT RUN {
csSensitivity(cs1 = rnorm(n = 100, mean = 10),
cs2 = rnorm(n = 100, mean = 9))
# }
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