convert_es | R Documentation |
This function converts a variety of effect sizes to correlations, Cohen's d values, or common language effect sizes, and calculates sampling error variances and effective sample sizes.
convert_es( es, input_es = c("r", "d", "delta", "g", "t", "p.t", "F", "p.F", "chisq", "p.chisq", "or", "lor", "Fisherz", "A", "auc", "cles"), output_es = c("r", "d", "A", "auc", "cles"), n1 = NULL, n2 = NULL, df1 = NULL, df2 = NULL, sd1 = NULL, sd2 = NULL, tails = 2 )
es |
Vector of effect sizes to convert. |
input_es |
Scalar. Metric of input effect sizes. Currently supports correlations, Cohen's d, independent samples t values (or their p values), two-group one-way ANOVA F values (or their p values), 1-df χ-squared values (or their p values), odds ratios, log odds ratios, Fisher z, and the common language effect size (CLES, A, AUC). |
output_es |
Scalar. Metric of output effect sizes. Currently supports correlations, Cohen's d values, and common language effect sizes (CLES, A, AUC). |
n1 |
Vector of total sample sizes or sample sizes of group 1 of the two groups being contrasted. |
n2 |
Vector of sample sizes of group 2 of the two groups being contrasted. |
df1 |
Vector of input test statistic degrees of freedom (for t and χ-squared) or between-groups degree of freedom (for F). |
df2 |
Vector of input test statistic within-group degrees of freedom (for F). |
sd1 |
Vector of pooled (within-group) standard deviations or standard deviations of group 1 of the two groups being contrasted. |
sd2 |
Vector of standard deviations of group 2 of the two groups being contrasted. |
tails |
Vector of the tails for p values when |
A data frame of class es
with variables:
|
The converted effect sizes |
|
The effective total sample size |
|
The total number of cases (original sample size) |
|
If applicable, subgroup sample sizes |
|
The estimated sampling error variance |
Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine, 19(22), 3127–3131. doi: 10.1002/1097-0258(20001130)19:22<3127::AID-SIM784>3.0.CO;2-M
Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Sage.
Ruscio, J. (2008). A probability-based measure of effect size: Robustness to base rates and other factors. Psychological Methods, 13(1), 19–30. doi: 10.1037/1082-989X.13.1.19
Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings (3rd ed.). Sage. doi: 10.4135/9781483398105
convert_es(es = 1, input_es="d", output_es="r", n1=100) convert_es(es = 1, input_es="d", output_es="r", n1=50, n2 = 50) convert_es(es = .2, input_es="r", output_es="d", n1=100, n2=150) convert_es(es = -1.3, input_es="t", output_es="r", n1 = 100, n2 = 140) convert_es(es = 10.3, input_es="F", output_es="d", n1 = 100, n2 = 150) convert_es(es = 1.3, input_es="chisq", output_es="r", n1 = 100, n2 = 100) convert_es(es = .021, input_es="p.chisq", output_es="d", n1 = 100, n2 = 100) convert_es(es = 4.37, input_es="or", output_es="r", n1=100, n2=100) convert_es(es = 4.37, input_es="or", output_es="d", n1=100, n2=100) convert_es(es = 1.47, input_es="lor", output_es="r", n1=100, n2=100) convert_es(es = 1.47, input_es="lor", output_es="d", n1=100, n2=100)
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