rankComb {opticut} | R Documentation |
Ranking Based Binary Partitions
Description
Blindly fitting a model to all possible partitions is wasteful use of resources. Instead, one can rank the K levels (strata) based on expected response values to explore only K-1 binary partitions along the gradient defined by the ranks of the expected values.
Usage
oComb(x, collapse)
rankComb(Y, X, Z, dist = "gaussian", collapse, ...)
Arguments
Y |
numeric, vector of observations. |
X |
numeric, design matrix. |
Z |
factor, must have at least 2 unique levels. |
dist |
character, distribution argument passed to underlying functions,
see listed on the help page of |
x |
and a numeric vector. |
collapse |
character, what to paste between levels.
Defaults to |
... |
other arguments passed to the underlying functions, see |
Value
oComb
returns the 'contrast' matrix based on the rank vector as input.
Ranked from lowest to highest expected value among the partitions.
The function rankComb
fits the model with multiple (K > 2) factor levels
to find out the ranking, and returns a binary classification matrix
as returned by oComb
corresponding to the ranking.
Author(s)
Peter Solymos <psolymos@gmail.com>
See Also
allComb
for alternative partitioning algorithm.
opticut
for the user interface.
Examples
## simulate some data
set.seed(1234)
n <- 200
x0 <- sample(1:4, n, TRUE)
x1 <- ifelse(x0 %in% 1:2, 1, 0)
x2 <- rnorm(n, 0.5, 1)
lam <- exp(0.5 + 0.5*x1 + -0.2*x2)
Y <- rpois(n, lam)
## binary partitions
head(rc <- rankComb(Y, model.matrix(~x2), as.factor(x0), dist="poisson"))
attr(rc, "est") # expected values in factor levels
aggregate(exp(0.5 + 0.5*x1), list(x0=x0), mean) # true values
## simple example
oComb(1:4, "+")
## using estimates
oComb(attr(rc, "est"))