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boost-R / gamboostLSS

Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).
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selected.mboostLSS is broken #39

Closed hofnerb closed 7 years ago

hofnerb commented 7 years ago 
set.seed(1907)
x1 <- rnorm(500)
x2 <- rnorm(500)
x3 <- rnorm(500)
x4 <- rnorm(500)
x5 <- rnorm(500)
x6 <- rnorm(500)
mu    <- exp(1.5 +1 * x1 +0.5 * x2 -0.5 * x3 -1 * x4)
sigma <- exp(-0.4 * x3 -0.2 * x4 +0.2 * x5 +0.4 * x6)
y <- numeric(500)
for( i in 1:500)
    y[i] <- rnbinom(1, size = sigma[i], mu = mu[i])
dat <- data.frame(x1, x2, x3, x4, x5, x6, y)

model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
                     control = boost_control(mstop = 10),
                     center = TRUE, method = "cyclic")
selected(model) # ok (at least in principle)
selected(model, merge = TRUE) # ok

model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
                     control = boost_control(mstop = 10),
                     center = TRUE, method = "noncyclic")
selected(model) # ok (at least in principle)
selected(model, merge = TRUE) ## BROKEN
hofnerb commented 7 years ago

Also (partially) broken when sigma is selected:

set.seed(1907)
x1 <- rnorm(500)
x2 <- rnorm(500)
x3 <- rnorm(500)
x4 <- rnorm(500)
x5 <- rnorm(500)
x6 <- rnorm(500)
mu    <- exp(1.5 +1 * x1 +0.5 * x2 -0.5 * x3 -1 * x4)
sigma <- exp(-0.4 * x3 -0.2 * x4 +0.2 * x5 + 1 * x6)
y <- numeric(500)
for( i in 1:500)
    y[i] <- rnbinom(1, size = sigma[i], mu = mu[i])
dat <- data.frame(x1, x2, x3, x4, x5, x6, y)

model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
                     control = boost_control(mstop = 10),
                     center = TRUE, method = "cyclic")
selected(model) # ok (at least in principle)
selected(model, merge = TRUE) # ok

model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
                     control = boost_control(mstop = 10),
                     center = TRUE, method = "noncyclic")
selected(model) # ok (at least in principle)
selected(model, merge = TRUE) ## BROKEN