View source: R/csSensitivity.R
csSensitivity  R Documentation 
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
)
cs1 
a numeric vector of values. If the 
cs2 
a numeric vector of values. If the 
group 
column index or name that contain the group data. See

data 
numeric matrix or data frame that contains the relevant data. 
alternative 
a character string for the specification of
the alternative hypothesis. Possible values: 
conf.level 
Interval's confidence level. 
mu 
a numeric value for the mean value or mean difference. 
rscaleSens 
the scale factor for the prior used in the Bayesian t.test 
out.thres 
The threshold for detecting outliers (default is 3). If set
to 0, no outliers analysis will be performed. See 
csCompare
performs both a student ttest (using the
stats::t.test
function) and a Bayesian ttest (using the
BayesFactor::ttest.tstat
). In case group
is not defined,
pairedsamples ttests 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 ttest is run.
The function returns a data frame with the results of the student ttest and the Bayesian ttest.
Krypotos, A. M., Klugkist, I., & Engelhard, I. M. (2017). Bayesian hypothesis testing for human threat conditioning research: An introduction and the condir R package. European Journal of Psychotraumatology, 8.
csCompare
, t.test
,
ttest.tstat
set.seed(1000)
csSensitivity(cs1 = rnorm(n = 100, mean = 10),
cs2 = rnorm(n = 100, mean = 9))
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