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:
- The effect size, whether being
f, (Cox) odds ratios or (Cox) logits, is always named
- The standard error of the effect size,
- The variance of the effect size,
- The lower and upper confidence limits
- The weight factor, based on the inverse-variance,
- The total sample size
- The effect size measure,
measure, which is typically specified via the
- Information on the effect-size conversion,
- A string with the study name, if the
study-argument was specified in function calls.
Correlation Effect Size
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.
Odds Ratio Effect Size
For odds ratios, the variance and standard error are always returned on the log-scale!
esc package offers the S3 methods
Combining results into a single data frame
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) > study es weight sample.size se var ci.lo ci.hi measure > 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.
Latest development build
To install the latest development snapshot (see latest changes below), type following commands into the R console:
Officiale, stable release
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.