mmsscore_int: Calculate leave-n-out cross validation (LNOCV) score

In reumandc/mms: Model Selection Via Resampling for Matrix Models

Description

This function is used to calculate an LNOCV score for a given model, based on mean squared error, out of sample. Same as mmsscore but with no error checking.

Usage

mmsscore_int(mats, pred, n, maxruns)

Arguments

mats	A list of matrices, all assumed to be the same dimensions. Only the lower triangles are used. NA/NaNs are allowed. The first entry taken to be the response.
pred	The indices in mats of predictor variables in the more complex of the two models to be compared, should not include 1. Input is numeric value(s), e.g. pred=2, pred =2:3, pred =c(2,3,5).
n	The number of sampling locations to leave out, must be at least 2.
maxruns	The maximum number of leave-n-outs (LNOs) to do. To be used if choose(dim(mats[[1]]),n) is very large. Inf to use (or try to use) all LNOs. If maxruns is a number, then LNOs are selected randomly and hence may include repeats.

Value

mmsscore return an object of class list consisting of

lno.score	The out-of-sample forecast accuracy (mean squared error)
num.pos	The possible number of LNOs for the given n and number of locations
num.att	The total number of LNOs attempted
num.rnk	The number of LNOs that did not result in a rank deficiency regression problem, and so could be used for testing out-of-sample predictions
num.usd	The number of LNOs that could be used in the end (possibly less than num.rnk because of NAs in the input matrices)

Author(s)

Tom Anderson, [e-mail]; Daniel Reuman, [e-mail]; Jon Walter, [e-mail]

reumandc/mms documentation built on May 28, 2019, 5:39 p.m.