More Random Samples
These functions simplify and unify sampling in various ways.
resample(..., replace = TRUE) deal(...) shuffle(x, replace = FALSE, prob = NULL, groups = NULL, orig.ids = FALSE) sample(x, size, replace = FALSE, ...) ## Default S3 method: sample( x, size, replace = FALSE, prob = NULL, groups = NULL, orig.ids = FALSE, ... ) ## S3 method for class 'data.frame' sample( x, size, replace = FALSE, prob = NULL, groups = NULL, orig.ids = TRUE, fixed = names(x), shuffled = c(), invisibly.return = NULL, ... ) ## S3 method for class 'matrix' sample( x, size, replace = FALSE, prob = NULL, groups = NULL, orig.ids = FALSE, ... ) ## S3 method for class 'factor' sample( x, size, replace = FALSE, prob = NULL, groups = NULL, orig.ids = FALSE, drop.unused.levels = FALSE, ... ) ## S3 method for class 'lm' sample( x, size, replace = FALSE, prob = NULL, groups = NULL, orig.ids = FALSE, drop.unused.levels = FALSE, parametric = FALSE, transformation = NULL, ... )
... |
additional arguments passed to
|
replace |
Should sampling be with replacement? |
x |
Either a vector of one or more elements from which to choose, or a positive integer. |
prob |
A vector of probability weights for obtaining the elements of the vector being sampled. |
groups |
a vector (or variable in a data frame) specifying groups to sample within. This will be recycled if necessary. |
orig.ids |
a logical; should original ids be included in returned data frame? |
size |
a non-negative integer giving the number of items to choose. |
fixed |
a vector of column names. These variables are shuffled en masse, preserving associations among these columns. |
shuffled |
a vector of column names.
these variables are reshuffled individually (within groups if |
invisibly.return |
a logical, should return be invisible? |
drop.unused.levels |
a logical, should unused levels be dropped? |
parametric |
A logical indicating whether the resampling should be done parametrically. |
transformation |
NULL or a function providing a transformation to be applied to the
synthetic responses. If NULL, an attempt it made to infer the appropriate transformation
from the original call as recorded in |
These functions are wrappers around sample() providing different defaults and
natural names.
# 100 Bernoulli trials -- no need for replace=TRUE
resample(0:1, 100)
tally(resample(0:1, 100))
if (require(mosaicData)) {
Small <- sample(KidsFeet, 10)
resample(Small)
tally(~ sex, data=resample(Small))
tally(~ sex, data=resample(Small))
# fixed marginals for sex
tally(~ sex, data=Small)
tally(~ sex, data=resample(Small, groups=sex))
# shuffled can be used to reshuffle some variables within groups
# orig.id shows where the values were in original data frame.
Small <- mutate(Small,
id1 = paste(sex,1:10, sep=":"),
id2 = paste(sex,1:10, sep=":"))
resample(Small, groups=sex, shuffled=c("id1","id2"))
}
deal(Cards, 13) # A Bridge hand
shuffle(Cards)
model <- lm(width ~length * sex, data = KidsFeet)
KidsFeet %>% head()
resample(model) %>% head()
Boot <- do(500) * lm(width ~ length * sex, data = resample(KidsFeet))
df_stats(~ Intercept + length + sexG + length.sexG, data = Boot, sd)
head(Boot)
summary(coef(model))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.