set.seed(290875)
n <- 100
x1 <- rnorm(n)
x2 <- rnorm(n) + 0.25 * x1
x3 <- as.factor(sample(0:1, 100, replace = TRUE))
x4 <- gl(4, 25)
y <- 3 * sin(x1) + x2^2 + rnorm(n)
weights <- drop(rmultinom(1, n, rep.int(1, n) / n))
### set up base-learners
spline1 <- bbs(x1, knots = 20, df = 4)
attributes(spline1)
knots.x2 <- quantile(x2, c(0.25, 0.5, 0.75))
spline2 <- bbs(x2, knots = knots.x2, df = 5)
attributes(spline2)
attributes(ols3 <- bols(x3))
attributes(ols4 <- bols(x4))
### compute base-models
drop(ols3$dpp(weights)$fit(y)$model) ## same as:
coef(lm(y ~ x3, weights = weights))
drop(ols4$dpp(weights)$fit(y)$model) ## same as:
coef(lm(y ~ x4, weights = weights))
### fit model, component-wise
mod1 <- mboost_fit(list(spline1, spline2, ols3, ols4), y, weights)
### more convenient formula interface
mod2 <- mboost(y ~ bbs(x1, knots = 20, df = 4) +
bbs(x2, knots = knots.x2, df = 5) +
bols(x3) + bols(x4))
all.equal(coef(mod1), coef(mod2))
### grouped linear effects
model <- gamboost(y ~ bols(x1, x2, intercept = FALSE) +
bols(x1, intercept = FALSE) +
bols(x2, intercept = FALSE),
control = boost_control(mstop = 400))
coef(model, which=1) # one base-learner for x1 and x2
coef(model, which=2:3) # two separate base-learners for x1 and x2
### example for bspatial
x1 <- runif(250,-pi,pi)
x2 <- runif(250,-pi,pi)
y <- sin(x1) * sin(x2) + rnorm(250, sd = 0.4)
spline3 <- bspatial(x1, x2, knots=12)
attributes(spline3)
## specify number of knots separately
form2 <- y ~ bspatial(x1, x2, knots=list(x1=12, x2=12))
## decompose spatial effect into parametric part and
## deviation with one df
form2 <- y ~ bols(x1) + bols(x2) + bols(x1*x2) +
bspatial(x1, x2, knots = 12, center = TRUE, df = 1)
### random intercept
id <- factor(rep(1:10, each = 5))
raneff <- brandom(id)
attributes(raneff)
## random intercept with non-observed category
set.seed(1907)
y <- rnorm(50, mean = rep(rnorm(10), each = 5), sd = 0.1)
plot(y ~ id)
# category 10 not observed
obs <- c(rep(1, 45), rep(0, 5))
model <- gamboost(y ~ brandom(id), weights = obs)
coef(model)
fitted(model)[46:50] # just the grand mean as usual for
# random effects models
### random slope
z <- runif(50)
raneff <- brandom(id, by=z)
attributes(raneff)
### remove intercept from base-learner
### and add explicit intercept to the model
tmpdata <- data.frame(x = 1:100, y = rnorm(1:100), int = rep(1, 100))
mod <- gamboost(y ~ bols(int, intercept = FALSE) +
bols(x, intercept = FALSE),
data = tmpdata,
control = boost_control(mstop = 2500))
cf <- unlist(coef(mod))
cf[1] <- cf[1] + mod$offset
cf
coef(lm(y ~ x, data = tmpdata))
### large data set with ties
nunique <- 100
xindex <- sample(1:nunique, 1000000, replace = TRUE)
x <- runif(nunique)
y <- rnorm(length(xindex))
w <- rep.int(1, length(xindex))
### brute force computations
op <- options()
options(mboost_indexmin = Inf, mboost_useMatrix = FALSE)
## data pre-processing
b1 <- bbs(x[xindex])$dpp(w)
## model fitting
c1 <- b1$fit(y)$model
options(op)
### automatic search for ties, faster
b2 <- bbs(x[xindex])$dpp(w)
c2 <- b2$fit(y)$model
### manual specification of ties, even faster
b3 <- bbs(x, index = xindex)$dpp(w)
c3 <- b3$fit(y)$model
all.equal(c1, c2)
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