## ----setup, echo=FALSE--------------------------------------------------------
set.seed(0)
## -----------------------------------------------------------------------------
if (requireNamespace("microbenchmark", quietly = TRUE)) {
x <- runif(100)
microbenchmark::microbenchmark(sqrt(x), x ^ .5)
} else {
"microbenchmark not available on your computer"
}
## -----------------------------------------------------------------------------
library(comparer)
mbc(mean(rnorm(10)), mean(rnorm(100)))
## -----------------------------------------------------------------------------
mbc(mean(rnorm(10)), mean(rnorm(100)), times=100)
## -----------------------------------------------------------------------------
mbc(mean(x), median(x), input=list(x=rexp(30)))
## -----------------------------------------------------------------------------
mbc(mean(x), median(x), inputi={x=rexp(30)})
## -----------------------------------------------------------------------------
mbc(mean, median, inputi=rexp(30))
## -----------------------------------------------------------------------------
n <- 20
x <- seq(0, 1, length.out = n)
y <- 1.8 * x - .6
ynoise <- y + rnorm(n, 0, .2)
## -----------------------------------------------------------------------------
mbc(predict(lm(ynoise ~ x), data.frame(x)),
predict(lm(ynoise ~ x - 1), data.frame(x)),
target = y)
## -----------------------------------------------------------------------------
mbc(predict(lm(ynoise ~ x), data.frame(x)),
predict(lm(ynoise ~ x - 1), data.frame(x)),
inputi={ynoise <- y + rnorm(n, 0, .2)},
target = y)
## -----------------------------------------------------------------------------
mbc(ynoise ~ x,
ynoise ~ x - 1,
evaluator=predict(lm(.), data.frame(x)),
inputi={ynoise <- y + rnorm(n, 0, .2)},
target = y)
## ----kfold_cars_ex------------------------------------------------------------
mbc({mod <- lm(dist ~ speed, data=cars[ki,])
p <- predict(mod,cars[-ki,])
sqrt(mean((p - cars$dist[-ki])^2))
},
kfold=c(nrow(cars), 5),
times=30)
## ----kfold_cars_ex2-----------------------------------------------------------
mbc(lm(dist ~ speed, data=cars[ki,]),
targetin=cars[-ki,], target="dist",
kfold=c(nrow(cars), 5),
times=30)
## ----kfold_cars_metric_t------------------------------------------------------
mbc(lm(dist ~ speed, data=cars[ki,]),
targetin=cars[-ki,], target="dist",
kfold=c(nrow(cars), 5),
times=30,
metric='t')
## -----------------------------------------------------------------------------
f1 <- ffexp$new(
a=1:3,
b=c("a","b","c"),
eval_func=paste
)
## -----------------------------------------------------------------------------
f1$run_all()
## -----------------------------------------------------------------------------
f1$outcleandf
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