print.cv.npmr {npmr}

summarize a "cv.npmr" object

Description

Print (1) the call that produced the cv.npmr object; (2) the value of the regularization parameter lambda that led to the minimum cross validation error; (3) the rank of the fitted regression coefficient matrix; and (4) the per-observation cross validation error using the optimal lambda.

Usage

## S3 method for class 'cv.npmr'
print(x, ...)

Arguments

x

an object of class cv.npmr

...

ignored

Author(s)

Scott Powers, Trevor Hastie, Rob Tibshirani

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.

See Also

cv.npmr, print.npmr

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))
fold = sample(rep(1:10, length = nrow(X)))

# 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 = cv.npmr(X, Y, lambda = exp(seq(7, -2)), foldid = fold)

# Print the NPMR fit:
fit2