Print and summary methods for objects of class "escalc".

# S3 method for escalc
print(x, digits=attr(x,"digits"), ...)

# S3 method for escalc
summary(object, out.names=c("sei","zi","pval","ci.lb","ci.ub"), var.names,
        H0=0, append=TRUE, replace=TRUE, level=95, olim, digits, transf, ...)

Arguments

x

an object of class "escalc".

object

an object of class "escalc".

digits

integer to specify the number of decimal places to which the printed results should be rounded (the default is to take the value from the object).

out.names

character string with four elements to specify the variable names for the standard errors, test statistics, and lower/upper confidence interval bounds.

var.names

character string with two elements to specify the variable names for the observed effect sizes or outcomes and the sampling variances (the default is to take the value from the object if possible).

H0

numeric value to specify the value of the effect size or outcome under the null hypothesis (the default is 0).

append

logical to specify whether the data frame specified via the object argument should be returned together with the additional variables that are calculated by the summary function (the default is TRUE).

replace

logical to specify whether existing values for sei, zi, ci.lb, and ci.ub in the data frame should be replaced or not. Only relevant when the data frame already contains these variables. If replace=TRUE (the default), all of the existing values will be overwritten. If replace=FALSE, only NA values will be replaced.

level

numeric value between 0 and 100 to specify the confidence interval level (the default is 95).

olim

optional argument to specify observation/outcome limits. If unspecified, no limits are used.

transf

optional argument to specify a function that should be used to transform the observed effect sizes or outcomes and interval bounds (e.g., transf=exp; see also transf). If unspecified, no transformation is used. Any additional arguments needed for the function specified here can be passed via ....

...

other arguments.

Value

The print.escalc function formats and prints the data frame, so that the observed effect sizes or outcomes and sampling variances are rounded (to the number of digits specified).

The summary.escalc function creates an object that is a data frame containing the original data (if append=TRUE) and the following components:

yi

observed effect sizes or outcomes (transformed if transf is specified).

vi

corresponding sampling variances.

sei

correponding standard errors.

zi

test statistics for testing \(\mbox{H}_0{:}\; \theta_i = \mbox{H0}\) (i.e., (yi-H0)/sei).

pval

corresponding p-values.

ci.lb

lower confidence interval bounds (transformed if transf is specified).

ci.ub

upper confidence interval bounds (transformed if transf is specified).

When the transf argument is specified, elements vi, sei, zi, and pval are not included (since these only apply to the untransformed effect sizes or outcomes). Note that the actual variable names above depend on the out.names (and var.names) arguments. If the data frame already contains variables with names as specified by the out.names argument, the values for these variables will be overwritten when replace=TRUE (which is the default). By setting replace=FALSE, only values that are NA will be replaced. The print.escalc function again formats and prints the data frame, rounding the added variables to the number of digits specified.

Note

If some transformation function has been specified for the transf argument, then yi, ci.lb, and ci.ub will be transformed accordingly. However, vi and sei then still reflect the sampling variances and standard errors of the untransformed values.

The summary.escalc function computes level % Wald-type confidence intervals, which may or may not be the most accurate method for computing confidence intervals for the chosen effect size or outcome measure.

If the outcome measure used is bounded (e.g., correlations are bounded between -1 and +1, proportions are bounded between 0 and 1), one can use the olim argument to enforce those observation/outcome limits (the observed outcomes and confidence intervals cannot exceed those bounds then).

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

See also

Examples

### calculate log risk ratios and corresponding sampling variances dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) dat
#> trial author year tpos tneg cpos cneg ablat alloc #> 1 1 Aronson 1948 4 119 11 128 44 random #> 2 2 Ferguson & Simes 1949 6 300 29 274 55 random #> 3 3 Rosenthal et al 1960 3 228 11 209 42 random #> 4 4 Hart & Sutherland 1977 62 13536 248 12619 52 random #> 5 5 Frimodt-Moller et al 1973 33 5036 47 5761 13 alternate #> 6 6 Stein & Aronson 1953 180 1361 372 1079 44 alternate #> 7 7 Vandiviere et al 1973 8 2537 10 619 19 random #> 8 8 TPT Madras 1980 505 87886 499 87892 13 random #> 9 9 Coetzee & Berjak 1968 29 7470 45 7232 27 random #> 10 10 Rosenthal et al 1961 17 1699 65 1600 42 systematic #> 11 11 Comstock et al 1974 186 50448 141 27197 18 systematic #> 12 12 Comstock & Webster 1969 5 2493 3 2338 33 systematic #> 13 13 Comstock et al 1976 27 16886 29 17825 33 systematic #> yi vi #> 1 -0.8893 0.3256 #> 2 -1.5854 0.1946 #> 3 -1.3481 0.4154 #> 4 -1.4416 0.0200 #> 5 -0.2175 0.0512 #> 6 -0.7861 0.0069 #> 7 -1.6209 0.2230 #> 8 0.0120 0.0040 #> 9 -0.4694 0.0564 #> 10 -1.3713 0.0730 #> 11 -0.3394 0.0124 #> 12 0.4459 0.5325 #> 13 -0.0173 0.0714
### apply summary function summary(dat)
#> trial author year tpos tneg cpos cneg ablat alloc #> 1 1 Aronson 1948 4 119 11 128 44 random #> 2 2 Ferguson & Simes 1949 6 300 29 274 55 random #> 3 3 Rosenthal et al 1960 3 228 11 209 42 random #> 4 4 Hart & Sutherland 1977 62 13536 248 12619 52 random #> 5 5 Frimodt-Moller et al 1973 33 5036 47 5761 13 alternate #> 6 6 Stein & Aronson 1953 180 1361 372 1079 44 alternate #> 7 7 Vandiviere et al 1973 8 2537 10 619 19 random #> 8 8 TPT Madras 1980 505 87886 499 87892 13 random #> 9 9 Coetzee & Berjak 1968 29 7470 45 7232 27 random #> 10 10 Rosenthal et al 1961 17 1699 65 1600 42 systematic #> 11 11 Comstock et al 1974 186 50448 141 27197 18 systematic #> 12 12 Comstock & Webster 1969 5 2493 3 2338 33 systematic #> 13 13 Comstock et al 1976 27 16886 29 17825 33 systematic #> yi vi sei zi pval ci.lb ci.ub #> 1 -0.8893 0.3256 0.5706 -1.5586 0.1191 -2.0077 0.2290 #> 2 -1.5854 0.1946 0.4411 -3.5941 0.0003 -2.4500 -0.7208 #> 3 -1.3481 0.4154 0.6445 -2.0917 0.0365 -2.6113 -0.0849 #> 4 -1.4416 0.0200 0.1415 -10.1908 <.0001 -1.7188 -1.1643 #> 5 -0.2175 0.0512 0.2263 -0.9613 0.3364 -0.6611 0.2260 #> 6 -0.7861 0.0069 0.0831 -9.4599 <.0001 -0.9490 -0.6232 #> 7 -1.6209 0.2230 0.4722 -3.4323 0.0006 -2.5465 -0.6953 #> 8 0.0120 0.0040 0.0629 0.1899 0.8494 -0.1114 0.1353 #> 9 -0.4694 0.0564 0.2376 -1.9760 0.0482 -0.9350 -0.0038 #> 10 -1.3713 0.0730 0.2702 -5.0747 <.0001 -1.9010 -0.8417 #> 11 -0.3394 0.0124 0.1114 -3.0460 0.0023 -0.5577 -0.1210 #> 12 0.4459 0.5325 0.7297 0.6111 0.5412 -0.9843 1.8762 #> 13 -0.0173 0.0714 0.2672 -0.0648 0.9483 -0.5410 0.5064
summary(dat, transf=exp)
#> trial author year tpos tneg cpos cneg ablat alloc #> 1 1 Aronson 1948 4 119 11 128 44 random #> 2 2 Ferguson & Simes 1949 6 300 29 274 55 random #> 3 3 Rosenthal et al 1960 3 228 11 209 42 random #> 4 4 Hart & Sutherland 1977 62 13536 248 12619 52 random #> 5 5 Frimodt-Moller et al 1973 33 5036 47 5761 13 alternate #> 6 6 Stein & Aronson 1953 180 1361 372 1079 44 alternate #> 7 7 Vandiviere et al 1973 8 2537 10 619 19 random #> 8 8 TPT Madras 1980 505 87886 499 87892 13 random #> 9 9 Coetzee & Berjak 1968 29 7470 45 7232 27 random #> 10 10 Rosenthal et al 1961 17 1699 65 1600 42 systematic #> 11 11 Comstock et al 1974 186 50448 141 27197 18 systematic #> 12 12 Comstock & Webster 1969 5 2493 3 2338 33 systematic #> 13 13 Comstock et al 1976 27 16886 29 17825 33 systematic #> yi ci.lb ci.ub #> 1 0.4109 0.1343 1.2574 #> 2 0.2049 0.0863 0.4864 #> 3 0.2597 0.0734 0.9186 #> 4 0.2366 0.1793 0.3121 #> 5 0.8045 0.5163 1.2536 #> 6 0.4556 0.3871 0.5362 #> 7 0.1977 0.0784 0.4989 #> 8 1.0120 0.8946 1.1449 #> 9 0.6254 0.3926 0.9962 #> 10 0.2538 0.1494 0.4310 #> 11 0.7122 0.5725 0.8860 #> 12 1.5619 0.3737 6.5284 #> 13 0.9828 0.5821 1.6593