predict.npmr | R Documentation |
Return predicted reponse class probabilities from a fitted NPMR model, for each value of lambda on which the NPMR model was originally fit.
## S3 method for class 'npmr'
predict(object, newx, ...)
object |
an object of class |
newx |
covariate matrix on which for which to make response class probability
predictions. Must have same number of columns as |
... |
ignored |
a 3-dimensional array, with dimensions
(nrow(newx), ncol(Y), length(lambda)
).
For each lambda, this array stores for that value of lambda the predicted
response class probabilites for each observation.
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
, predict.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)))
# 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)
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