Description Usage Arguments Details Value Author(s) References See Also Examples
This function fits a logistic regression model penalizing the size of the L2 norm of the coefficients.
1 2 3 
x 
matrix of features 
y 
binary response 
weights 
optional vector of weights for observations 
offset.subset 
optional vector of indices for the predictors for which the
coefficients are preset to 
offset.coefficients 
optional vector of preset coefficient values for the predictors in

lambda 
regularization parameter for the L2 norm of the coefficients. The
minimizing criterion in 
cp 
complexity parameter to be used when computing the
score. 
We proposed using logistic regression with a quadratic penalization on
the coefficients for detecting gene interactions as described in
"Penalized Logistic Regression for Detecting Gene Interactions (2008)"
by Park and Hastie. However, this function plr
may be used for
a general purpose.
A plr
object is returned. predict, print,
and
summary
functions can be applied.
coefficients 
vector of the coefficient estimates 
covariance 
sandwich estimate of the covariance matrix for the coefficients 
deviance 
deviance of the fitted model 
null.deviance 
deviance of the null model 
df 
degrees of freedom of the fitted model 
score 
deviance + cp*df 
nobs 
number of observations 
cp 
complexity parameter used when computing the score 
fitted.values 
fitted probabilities 
linear.predictors 
linear predictors computed with the estimated coefficients 
level 
If any categorical factors are input, level  the list of level sets
 is automatically generated and returned. See 
Mee Young Park and Trevor Hastie
Mee Young Park and Trevor Hastie (2008) Penalized Logistic Regression for Detecting Gene Interactions
predict.plr, step.plr
1 2 3 4 5 6 7 8 9 10 11 12 13  n < 100
p < 10
x < matrix(rnorm(n * p), nrow=n)
y < sample(c(0, 1), n, replace=TRUE)
fit < plr(x, y, lambda=1)
p < 3
z < matrix(sample(seq(3), n * p, replace=TRUE), nrow=n)
x < data.frame(x1=factor(z[, 1]), x2=factor(z[, 2]), x3=factor(z[, 3]))
y < sample(c(0, 1), n, replace=TRUE)
fit < plr(x, y, lambda=1)
# 'level' is automatically generated. Check 'fit$level'.

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