Implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson (< http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>) in R. Based on the input, the effect size can be returned as standardized mean difference, Cohen's f, Hedges' g, Pearson's r or Fisher's transformation z, odds ratio or log odds, or eta squared effect size.
This is an R implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson. The original calculator can be found at http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php.
Based on the input, the effect size can be returned as standardized mean difference (
eta squared, Hedges'
g, correlation coefficient effect size
r or Fisher's transformation
z, odds ratio or log odds effect size.
The return value of all functions has the same structure:
f, (Cox) odds ratios or (Cox) logits, is always named
measure, which is typically specified via the
study-argument was specified in function calls.
If the correlation effect size
r is computed, the transformed Fisher's z and their confidence intervals are also returned. The variance and standard error for the correlation effect size r are always based on Fisher's transformation.
For odds ratios, the variance and standard error are always returned on the log-scale!
esc package offers the S3 methods
combine_esc method is a convenient way to create pooled data frames of different effect size calculations, for further use. Here is an example of
combine_esc, which returns a
e1 <- esc_2x2(grp1yes = 30, grp1no = 50, grp2yes = 40, grp2no = 45, study = "Study 1") e2 <- esc_2x2(grp1yes = 30, grp1no = 50, grp2yes = 40, grp2no = 45, es.type = "or", study = "Study 2") e3 <- esc_t(p = 0.03, grp1n = 100, grp2n = 150, study = "Study 3") e4 <- esc_mean_sd(grp1m = 7, grp1sd = 2, grp1n = 50, grp2m = 9, grp2sd = 3, grp2n = 60, es.type = "logit", study = "Study 4") combine_esc(e1, e2, e3, e4) > 1 Study 1 -0.3930426 9.944751 165 0.3171050 0.10055556 -1.01455689 0.2284717 logit > 2 Study 2 0.6750000 9.944751 165 0.3171050 0.10055556 0.36256305 1.2566780 or > 3 Study 3 0.2817789 59.433720 250 0.1297130 0.01682547 0.02754605 0.5360117 d > 4 Study 4 -1.3981827 7.721145 110 0.3598812 0.12951447 -2.10353685 -0.6928285 logit
esc is still under development, i.e. not all effect size computation options are implemented yet. The remaining options will follow in further updates.
To install the latest development snapshot (see latest changes below), type following commands into the R console:
To install the latest stable release from CRAN, type following command into the R console:
In case you want / have to cite my package, please use
citation('esc') for citation information.
esc_mean_sd()tried to calculate the square root of negative values when computing the pooled standard deviance. In such cases, an alternative formular for the pooled SD is used.
combine_esc()did not work for effect sizes computed with
esc_t(), when the returned effect size was
effect_sizes()did not always properly extract study names and used numeric indices instead character values.
effect_sizes()to generate a data frame with results of multiple effect size computations, based on data from another data frame.
write_esc()as a convenient wrapper to write results to an Excel csv-file.