npmr (version 1.3.1)

npmr-package: Nuclear penalized multinomial regression

Description

As an alternative to an l1- or l2-penalty on multinomial logistic regression, this package fits multinomial regression with a penalty on the nuclear norm of the fitted regression coefficient matrix. The result is often a matrix of reduced rank, leveraging structure among the response classes so that the likelihood of one class informs the likelihood of other classes. Proximal gradient descent is used to solve the NPMR optimization problem.

Author

Scott Powers, Trevor Hastie, Rob Tibshirani

Maintainer: Scott Powers <[e-mail]>

Details

The primary functions in the package are npmr, which solves nuclear penalized multinomial regression for a sequence of input values for the regularization parameter lambda, and cv.npmr, which chooses the optimal value of the regularization parameter lambda via cross validation. Both npmr and cv.npmr have predict and plot methods.

References

Scott Powers, Trevor Hastie and Rob Tibshirani (2016). ``Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball.'' In prep.

Examples

#   Fit NPMR to simulated data

K = 5
n = 1000
m = 10000
p = 10
r = 2

# Simulated training data
set.seed(8369)
A = matrix(rnorm(p*r), p, r)
C = matrix(rnorm(K*r), K, r)
B = tcrossprod(A, C)            # low-rank coefficient matrix
X = matrix(rnorm(n*p), n, p)    # covariate matrix with iid Gaussian entries
eta = X 
P = exp(eta)/rowSums(exp(eta))
Y = t(apply(P, 1, rmultinom, n = 1, size = 1))

# Simulate test data
Xtest = matrix(rnorm(m*p), m, p)
etatest = Xtest 
Ptest = exp(etatest)/rowSums(exp(etatest))
Ytest = t(apply(Ptest, 1, rmultinom, n = 1, size = 1))

# Fit NPMR for a sequence of lambda values without CV:
fit2 = npmr(X, Y, lambda = exp(seq(7, -2)))

# Print the NPMR fit:
fit2

# Produce a biplot:
plot(fit2, lambda = 20)

# Compute mean test error using the predict function (for each value of lambda):
getloss = function(pred, Y) {
    -mean(log(rowSums(Y*pred)))
}
apply(predict(fit2, Xtest), 3, getloss, Ytest)