replication: Inference for Replication Studies

In relevance: Calculate Relevance and Significance Measures

Inference for Replication Studies

Description

Calculate inference for a replication study and for its comparison with the original

Usage

replication(original, replication, testlevel=getOption("testlevel"),
           rlv.threshold=getOption("rlv.threshold") )

Arguments

original	list of class inference, providing the effect estimate (["effect"]), its standard error (["se"]), the number of observations (["n"]), and the scatter (["scatter"]) for the 'original' study, or a matrix or data.frame containing this information as the first row.
replication	the same, for the replication study; if empty or NULL, the second row of argument original is assumed to contain the information about the replication.
testlevel	level of statistical tests
rlv.threshold	threshold of relevance; if this is a vector, the first element will be used.

Value

A list of class inference and replication containing the results of the comparison between the studies and, as an attribute, the results for the replication.

Author(s)

Werner A. Stahel

References

Werner A. Stahel (2020). Measuring Significance and Relevance instead of p-values. Submitted; available in the documentation.

See Also

relevance

Examples

data(d.osc15Onesample)
tx <- structure(d.osc15Onesample[,c("effecto","teststatistico","no")],
      names=c("effect","teststatistic","n"))
ty <- structure(d.osc15Onesample[,c("effectr","teststatisticr","nr")],
      names=c("effect","teststatistic","n"))
replication(tx, ty, rlv.threshold=0.1)

relevance documentation built on May 1, 2023, 5:20 p.m.