Methods for 'escalc' Objects

Methods for objects of class "escalc".

# S3 method for class 'escalc'
x[i, ...]
# S3 method for class 'escalc'
x$name <- value
# S3 method for class 'escalc'
cbind(..., deparse.level=1)
# S3 method for class 'escalc'
rbind(..., deparse.level=1)

Arguments

x

an object of class "escalc".

...

other arguments.

Note

For the `[` method, any variables specified as part of the i argument will be searched for within object x first (see ‘Examples’).

Author

Wolfgang Viechtbauer (wvb@metafor-project.org, https://www.metafor-project.org).

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03

Examples

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### select rows where variable 'alloc' is equal to 'random'
dat[dat$alloc == "random",]
#> 
#>   trial            author year tpos  tneg cpos  cneg ablat  alloc      yi     vi 
#> 1     1           Aronson 1948    4   119   11   128    44 random -0.8893 0.3256 
#> 2     2  Ferguson & Simes 1949    6   300   29   274    55 random -1.5854 0.1946 
#> 3     3   Rosenthal et al 1960    3   228   11   209    42 random -1.3481 0.4154 
#> 4     4 Hart & Sutherland 1977   62 13536  248 12619    52 random -1.4416 0.0200 
#> 7     7  Vandiviere et al 1973    8  2537   10   619    19 random -1.6209 0.2230 
#> 8     8        TPT Madras 1980  505 87886  499 87892    13 random  0.0120 0.0040 
#> 9     9  Coetzee & Berjak 1968   29  7470   45  7232    27 random -0.4694 0.0564 
#> 

### variables specified are automatically searched for within the object itself
dat[alloc == "random",]
#> 
#>   trial            author year tpos  tneg cpos  cneg ablat  alloc      yi     vi 
#> 1     1           Aronson 1948    4   119   11   128    44 random -0.8893 0.3256 
#> 2     2  Ferguson & Simes 1949    6   300   29   274    55 random -1.5854 0.1946 
#> 3     3   Rosenthal et al 1960    3   228   11   209    42 random -1.3481 0.4154 
#> 4     4 Hart & Sutherland 1977   62 13536  248 12619    52 random -1.4416 0.0200 
#> 7     7  Vandiviere et al 1973    8  2537   10   619    19 random -1.6209 0.2230 
#> 8     8        TPT Madras 1980  505 87886  499 87892    13 random  0.0120 0.0040 
#> 9     9  Coetzee & Berjak 1968   29  7470   45  7232    27 random -0.4694 0.0564 
#> 

### note: this behavior is specific to 'escalc' objects; this doesn't work for regular data frames