effect_differences.study: Calculates two difference tables with two distinct methods to...
In StefanThoma/ReplicationRelevance: Calculates Relevance for Many Labs Replication Projects
effect_differences.study R Documentation
Calculates two difference tables with two distinct methods to calculate CI.
wald: Uses pooled SD to create normal CI
newcombe: Uses newcombe method to create CI
Description
Calculates two difference tables with two distinct methods to calculate CI.
wald: Uses pooled SD to create normal CI
newcombe: Uses newcombe method to create CI
Usage
effect_differences.study(object, coverage_prob = 0.95, standardise = NULL)
Arguments
object
Study object.
coverage_prob
Double. Coverage probability of the CIs. Defaults to .95
Value
CI. numeric vector containing the difference in effect sizes and the
lower and upper boundaries of a confidence interval for this difference.
Author(s)
Stefan Thoma, adapted from Lorenz Herger
StefanThoma/ReplicationRelevance documentation built on Feb. 6, 2023, 4:03 a.m.
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| effect_differences.study | R Documentation |
Calculates two difference tables with two distinct methods to calculate CI. wald: Uses pooled SD to create normal CI newcombe: Uses newcombe method to create CI
Description
Calculates two difference tables with two distinct methods to calculate CI. wald: Uses pooled SD to create normal CI newcombe: Uses newcombe method to create CI
Usage
effect_differences.study(object, coverage_prob = 0.95, standardise = NULL)
Arguments
object |
Study object. |
coverage_prob |
Double. Coverage probability of the CIs. Defaults to .95 |
Value
CI. numeric vector containing the difference in effect sizes and the lower and upper boundaries of a confidence interval for this difference.
Author(s)
Stefan Thoma, adapted from Lorenz Herger
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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