do() provides a natural syntax for repetition tuned to assist
with replication and resampling methods.
do(object, ...)
# S3 method for class 'numeric'
do(object, ...)
# Default S3 method
do(object, ...)
Do(n = 1L, cull = NULL, mode = "default", algorithm = 1, parallel = TRUE)
# S3 method for class 'repeater'
print(x, ...)
# S4 method for class 'repeater,ANY'
e1 * e2an object
additional arguments
number of times to repeat
function for culling output of objects being repeated. If NULL,
a default culling function is used. The default culling function is
currently aware of objects of types
lme,
lm,
htest,
table,
cointoss, and
matrix.
target mode for value returned
a number used to select the algorithm used. Currently numbers below 1 use an older algorithm and numbers >=1 use a newer algorithm which is faster in some situations.
a logical indicating whether parallel computation should be attempted using the parallel package (if it is installed and loaded).
an object created by do.
an object (in cases documented here, the result of running do)
an object (in cases documented here, an expression to be repeated)
do returns an object of class repeater which is only useful in
the context of the operator *. See the examples.
do is a thin wrapper around Do to avoid collision with
dplyr::do() from the dplyr package.
The names used in the object returned from do() are inferred from the
objects created in each replication. Roughly, this the strategy employed.
If the objects have names, those names are inherited, if possible.
If the objects do not have names, but do() is used with a simple
function call, the name of that function is used.
Example: do(3) * mean(~height, data = Galton) produces a data frame with
a variable named mean.
In cases where names are not easily inferred and a single result is produced,
it is named result.
To get different names, one can rename the objects as they are created, or
rename the result returned from do(). Example of the former:
do(3) * c(mean_height = mean(~height, data = resample(Galton))).
do(3) * rnorm(1)
#> rnorm
#> 1 0.9537124
#> 2 -0.3084539
#> 3 -0.1936198
do(3) * "hello"
#> hello
#> 1 hello
#> 2 hello
#> 3 hello
do(3) * 1:4
#> V1 V2 V3 V4
#> 1 1 2 3 4
#> 2 1 2 3 4
#> 3 1 2 3 4
do(3) * mean(rnorm(25))
#> mean
#> 1 -0.32228041
#> 2 0.13824416
#> 3 0.04960635
do(3) * lm(shuffle(height) ~ sex + mother, Galton)
#> Intercept sexM mother sigma r.squared F numdf dendf
#> 1 72.74839 -0.1303453 -0.09238138 3.580112 0.0037920883 1.703419 2 895
#> 2 64.77094 -0.1727158 0.03244444 3.585030 0.0010531400 0.471777 2 895
#> 3 65.71198 -0.1644362 0.01769318 3.585709 0.0006749052 0.302224 2 895
#> .row .index
#> 1 1 1
#> 2 1 2
#> 3 1 3
do(3) * anova(lm(shuffle(height) ~ sex + mother, Galton))
#> source df SS MS F pval .row
#> sex...1 sex 1 1.200378e+01 12.0037764 0.93463244 0.3339232 1
#> mother...2 mother 1 8.294034e+00 8.2940336 0.64578618 0.4218366 2
#> Residuals...3 Residuals 895 1.149476e+04 12.8433124 NA NA 3
#> sex...4 sex 1 1.623471e+00 1.6234711 0.12621332 0.7224747 1
#> mother...5 mother 1 1.130675e+00 1.1306750 0.08790193 0.7669303 2
#> Residuals...6 Residuals 895 1.151231e+04 12.8629142 NA NA 3
#> sex...7 sex 1 1.758369e-01 0.1758369 0.01366807 0.9069572 1
#> mother...8 mother 1 8.987272e-01 0.8987272 0.06985945 0.7916024 2
#> Residuals...9 Residuals 895 1.151399e+04 12.8647908 NA NA 3
#> .index
#> sex...1 1
#> mother...2 1
#> Residuals...3 1
#> sex...4 2
#> mother...5 2
#> Residuals...6 2
#> sex...7 3
#> mother...8 3
#> Residuals...9 3
do(3) * c(sample.mean = mean(rnorm(25)))
#> sample.mean
#> 1 -0.07759286
#> 2 0.22312550
#> 3 0.04760878
# change the names on the fly
do(3) * mean(~height, data = resample(Galton))
#> mean
#> 1 66.61247
#> 2 66.69477
#> 3 66.88731
do(3) * c(mean_height = mean(~height, data = resample(Galton)))
#> mean_height
#> 1 66.80846
#> 2 66.41080
#> 3 66.75022
set.rseed(1234)
do(3) * tally( ~sex|treat, data=resample(HELPrct))
#> female.no male.no female.yes male.yes
#> 1 45 184 59 165
#> 2 52 179 58 164
#> 3 52 163 43 195
set.rseed(1234) # re-using seed gives same results again
do(3) * tally( ~sex|treat, data=resample(HELPrct))
#> female.no male.no female.yes male.yes
#> 1 45 184 59 165
#> 2 52 179 58 164
#> 3 52 163 43 195