Description Usage Arguments Details Value Examples
This method computes any effect size from raw values from a data frame. Convenient method to compute multiple effect sizes at once, when the required information to calculate effects sizes are stored in a table (i.e. data frame).
1 2 3 4 5 6 | effect_sizes(
data,
...,
fun,
es.type = c("d", "g", "or", "logit", "r", "f", "eta", "cox.or", "cox.log")
)
|
data |
A data frame with columns that contain the values that are passed to one of the esc-functions. |
... |
Named arguments. The name (left-hand side) is the name of one of
esc functions' argument, the argument (right-hand side) is the
name of the column in |
fun |
Name of one of the esc-functions, as string, where arguments
in |
es.type |
Type of effect size that should be returned.
|
This function rowwise iterates data
and calls the function named
in fun
for the values taken from each row of data
.
The column names in data
that contain the necessary values to compute
the effect sizes should be passed as unquoted value for the arguments.
The argument names should match those arguments for the esc-function
that should be called from within effect_sizes()
.
Example:
If you want to compute effect sizes from chi-squared values, you
would call esc_chisq()
. This function name is used for the
fun
-argument: fun = "esc_chisq"
. esc_chisq()
requires one of chisq
or p
as arguments, and totaln
.
Now data
must have columns with values for either chisq
or p
, and effect_sizes()
automatically selects the
first non-missing value from data
(see 'Examples').
A data frame with the effect sizes computed for all data from data
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | tmp <- data.frame(
tvalue = c(3.3, 2.9, 2.3),
n = c(250, 200, 210),
studyname = c("Study 1", "Study 2", "Study 3")
)
effect_sizes(tmp, t = tvalue, totaln = n, study = studyname, fun = "esc_t")
# missing effect size results are dropped,
# shorter function name, calls "esc_t()"
tmp <- data.frame(
tvalue = c(3.3, 2.9, NA, 2.3),
n = c(250, 200, 210, 210),
studyname = c("Study 1", "Study 2", NA, "Study 4")
)
effect_sizes(tmp, t = tvalue, totaln = n, study = studyname, fun = "t")
tmp <- data.frame(
coefficient = c(0.4, 0.2, 0.6),
se = c(.15, .1, .2),
treat = c(50, 60, 50),
cntrl = c(45, 70, 40),
author = c("Smith 2000", "Smith 2010 2", "Smith 2012")
)
effect_sizes(tmp, beta = coefficient, sdy = se, grp1n = treat, grp2n = cntrl,
study = author, fun = "esc_beta", es.type = "or")
# the "esc_chisq" function requires *either* the chisq-argument *or*
# the pval-argument. If at least one of these values is present,
# effect size can be calculated. You can specify both arguments,
# and the first non-missing required value from "data" is taken.
tmp <- data.frame(
chisqquared = c(NA, NA, 3.3, NA, 2.9),
pval = c(.003, .05, NA, .12, NA),
n = c(250, 200, 210, 150, 180),
studyname = c("Study 1", "Study 2", "Study 3", "Study 4", "Study 5")
)
effect_sizes(tmp, chisq = chisqquared, p = pval, totaln = n,
study = studyname, fun = "esc_chisq")
# if all required information are missing, data will be removed
tmp <- data.frame(
chisqquared = c(NA, NA, 3.3, NA, NA),
pval = c(.003, .05, NA, .12, NA),
n = c(250, 200, 210, 150, 180),
studyname = c("Study 1", "Study 2", "Study 3", "Study 4", "Study 5")
)
effect_sizes(tmp, chisq = chisqquared, p = pval, totaln = n,
study = studyname, fun = "chisq")
|
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