This is an R package, in development, for regularized interval regression.
To install the package, use the devtools package:
devtools::install_github("anujkhare/iregnet")# y can be a 2 column matrix.
X <- matrix(rnorm(50), 10, 5)
y <- matrix(rnorm(20), 10, 2)
y <- t(apply(y, 1, sort)) # intervals must be non-decreasing
fit1 <- iregnet(X, y)
# Surv objects from survival are also supported.
data("ovarian")
X <- cbind(ovarian$ecog.ps, ovarian$rx)
y <- Surv(ovarian$futime, ovarian$fustat)
fit2 <- iregnet(X, y)
# Log-Gaussian is same as Gaussian with log-transformed data
X <- matrix(rnorm(50), 10, 5)
y <- matrix(abs(rnorm(20)), 10, 2)
y <- t(apply(y, 1, sort)) # intervals must be non-decreasing
fit3 <- iregnet(X, log(y), "gaussian")
fit4 <- iregnet(X, y, "loggaussian")
# Scale parameter can be fixed by setting the estimate_scale flag.
X <- matrix(rnorm(50), 10, 5)
y <- matrix(rnorm(20), 10, 2)
y <- t(apply(y, 1, sort)) # intervals must be non-decreasing
fit5 <- iregnet(X, y, scale_init=1, estimate_scale=F)Detailed documentation of each parameter is provided in R help for iregnet.
For more details about the package, visit the following link: https://github.com/rstats-gsoc/gsoc2016/wiki/Regularized-interval-regression