print.npmr | R Documentation |
Print the call that produced the npmr
object and a dataframe
showing, for each value of the regularization parameter on
which the NPMR object was fit, the rank of the resulting regression coefficient
matrix and the corresponding value of the NPMR objective function.
## S3 method for class 'npmr'
print(x, ...)
x |
an object of class |
... |
ignored |
Scott Powers, Trevor Hastie, Rob Tibshirani
Scott Powers, Trevor Hastie and Rob Tibshirani (2016). “Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball.” In prep.
npmr
, print.cv.npmr
# 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
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