AdaptiveSparsity (version 1.6)

aslm: Adaptive Sparse Linear Model

Description

implements the adaptive sparse linear model using Figueiredo's EM algorithm for adaptive sparsity (Jeffreys prior)

Usage

# S3 method for formula
aslm(formula, data=list(), na.action=na.omit, ...)
# S3 method for default
aslm(x, y, ...)
getSparseModel(object)

Arguments

formula

an object of class “formula” (or one that can be coerced to that class): a symbolic description of the model to be fitted. See lm Details for further information.

data

an optional data frame, list or environment containing the variables in the model.

na.action

action to use when data contains NAs. Options include na.omit, na.exclude, na.fail

x

design matrix

y

vector of observations

…

further arguments

object

an object of class “aslm”.

Value

aslm returns an object of class c("aslm", "lm").

An object of class “aslm” is a list containing at least the following components:

coefficients

a named vector of coefficients

residuals

the residuals, that is response minus fitted values.

fitted.values

the fitted mean values

rank

the numeric rnak of the fitted linear model

df

the residual degrees of freedom

call

the matched call

terms

the terms object used

sigma

getSparseModel returns an object of class "lm" that is a model consisting of only the sparse nonzero variables from the original model.

References

Figueiredo, M.A.T.; , “Adaptive sparseness for supervised learning”, Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.25, no.9, pp. 1150- 1159, Sept. 2003

See Also

summary.aslm, logLik.aslm, print.aslm

Examples

# NOT RUN {
s = aslm(Infant.Mortality~.,data=swiss)
m = getSparseModel(s)

summary(s)
coef(m)
# }