l0ara (version 0.1.5)

l0ara: fit a generalized linear model with l0 penalty

Description

An adaptive ridge algorithm for feature selection with L0 penalty.

Usage

l0ara(x, y, family, lam, standardize, maxit, eps)

Arguments

x

Input matrix, of dimension nobs x nvars; each row is an observation vector.

y

Response variable. Quantitative for family="gaussian"; positive quantitative for family="gamma" or family="inv.gaussian" ; a factor with two levels for family="logit"; non-negative counts for family="poisson".

family

Response type(see above).

lam

A user supplied lambda value. If you have a lam sequence, use cv.l0ara first to select optimal tunning and then refit with lam.min . To use AIC, set lam=2; to use BIC, set lam=log(n).

standardize

Logical flag for data normalization. If standardize=TRUE(default), independent variables in the design matrix x will be standardized with mean 0 and standard deviation 1.

maxit

Maximum number of passes over the data for lambda. Default value is 1e3.

eps

Convergence threshold. Default value is 1e-4.

Value

An object with S3 class "l0ara" containing:

beta

A vector of coefficients

df

Number of nonzero coefficients

iter

Number of iterations

lambda

The lambda used

x

Design matrix

y

Response variable

Details

The sequence of models indexed by the parameter lambda is fit using adptive ridge algorithm. The objective function for generalized linear models (including family above) is defined to be $$-(log likelihood)+(\lambda/2)*|\beta|_0$$ \(|\beta|_0\) is the number of non-zero elements in \(\beta\). To select the "best" model with AIC or BIC criterion, let lambda to be 2 or log(n). This adaptive ridge algorithm is developed to approximate L0 penalized generalized linear models with sequential optimization and is efficient for high-dimensional data.

See Also

cv.l0ara, predict.l0ara, coef.l0ara, plot.l0ara methods.

Examples

# NOT RUN {
# Linear regression
# Generate design matrix and response variable
n <- 100
p <- 40
x <- matrix(rnorm(n*p), n, p)
beta <- c(1,0,2,3,rep(0,p-4))
noise <- rnorm(n)
y <- x%*%beta+noise
# fit sparse linear regression using BIC 
res.gaussian <- l0ara(x, y, family="gaussian", log(n))

# predict for new observations
print(res.gaussian)
predict(res.gaussian, newx=matrix(rnorm(3,p),3,p))
coef(res.gaussian)

# Logistic regression
# Generate design matrix and response variable
n <- 100
p <- 40
x <- matrix(rnorm(n*p), n, p)
beta <- c(1,0,2,3,rep(0,p-4))
prob <- exp(x%*%beta)/(1+exp(x%*%beta))
y <- rbinom(n, rep(1,n), prob)
# fit sparse logistic regression
res.logit <- l0ara(x, y, family="logit", 0.7)

# predict for new observations
print(res.logit)
predict(res.logit, newx=matrix(rnorm(3,p),3,p))
coef(res.logit)

# Poisson regression
# Generate design matrix and response variable
n <- 100
p <- 40
x <- matrix(rnorm(n*p), n, p)
beta <- c(1,0,0.5,0.3,rep(0,p-4))
mu <- exp(x%*%beta)
y <- rpois(n, mu)
# fit sparse Poisson regression using AIC
res.pois <- l0ara(x, y, family="poisson", 2)

# predict for new observations
print(res.pois)
predict(res.pois, newx=matrix(rnorm(3,p),3,p))
coef(res.pois)
# }

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