Description Usage Arguments Details Examples
This function calculates p-values and confidence intervals
based on sampled values and weights obtained by the iboost
function.
1 | format_iboost_res(res, alpha, method, vT, this_var, computeCI, fac = 2, y)
|
res |
list of sampled values either directly obtained in the |
alpha |
numeric; level for confidence interval(s) |
method |
character; method used for sampling |
vT |
list of test vector(s) |
this_var |
numeric; variance to be used for inference |
computeCI |
logical; whether or not to compute confidence intervals |
fac |
numeric; used in inverse search for confidence interval limits |
y |
vector; response vector |
The specific use of this function is to (internally) compute inference based
on samples in the iboost
function, to recalculate inference for
given samples dist
from an iboost
object or to calculate inference in
the first place when iboost
has been ran with argument returnSamples = TRUE
.
In the first and third case, use format_iboost_res
, otherwise format_iboost
.
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 | if(require("mboost")){
set.seed(0)
n <- 200
x1 <- rnorm(n)
x2 <- rnorm(n) + 0.25 * x1
x3 <- rnorm(n)
eta <- 3 * sin(x1) + x2^2
y <- scale(eta + rnorm(n), scale = FALSE)
spline1 <- bbs(x1, knots = 20, df = 4)
knots.x2 <- quantile(x2, c(0.25, 0.5, 0.75))
spline2 <- bbs(x2, knots = knots.x2, df = 4)
spline3 <- bbs(x3, knots = 20, df = 4)
data <- data.frame(y=y, x1=x1, x2=x2, x3=x3)
mod1 <- mboost(y ~ spline1 + spline2 + spline3,
control=boost_control(mstop = 73), offset = 0,
data = data)
# calculate p-values and intervals for model with
# fixed stopping iteration:
# this is done with only B = 100 samples for
# demonstrative purposes and should be increased
# for actual research questions
res <- iboost(mod1, method = "impsamp", B = 100)
# recalculate inference for different variance or alpha level
format_iboost(res, alpha = 0.1, this_var = var(y)*5)
}
|
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