Description Usage Arguments Value Author(s) References Examples
Example for new location prediction, Gaussian process method, and our COST method with Gaussian and t copulas, where the parameters are assumed to be known; the parameters can be obtained by the “optim" function. Data are generated at 13 locations and n time points, and assume that 9 locations are observed, and 4 new locations need prediction at time n, conditional on 9 locations at time points n-1 and n.
1 | example.prediction(n,n.total,seed1)
|
n |
number of time points for parameter estimation |
n.total |
number of total time points, with a burning sequence |
seed1 |
random seed to generate a data set, for reproducibility |
COST.t.pre.ECP |
a vector of length K=4 (number of new locations), with value 1 or 0, 1 means the verifying value from the corresponding location lies in the 95% prediction interval, 0 means not |
COST.t.pre.ML |
a vector of length K=4, each element is the length of prediction interval of the corresponding location |
COST.t.pre.med.error |
prediction error based on conditional median |
COST.G.pre.ECP |
same as COST.t.pre.ECP |
COST.G.pre.ML |
same as COST.t.pre.ML |
COST.G.pre.med.error |
same as COST.t.pre.med.error |
GP.pre.ECP |
same as COST.t.pre.ECP |
GP.pre.ML |
same as COST.t.pre.ML |
GP.pre.med.error |
same as COST.t.pre.med.error |
Yanlin Tang and Huixia Judy Wang
Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | library(COST)
#settings
n.total = 101 #number of total time points, including the burning sequence
n = 50 #number of time points we observed
seed1 = 22222
example.prediction(n,n.total,seed1)
#OUTPUTS
# $COST.t.pre.ECP #whether the prediction interval includes the true value, time point n
# [1] 1 1 1 1
#
# $COST.t.pre.ML #length of the prediction interval
# [1] 1.445576 2.146452 2.260688 2.706681
#
# $COST.t.pre.med.error #point prediction error, using conditional median
# [1] 0.01127162 -0.03222058 -0.22081051 0.57831480
#
# $COST.G.pre.ECP #whether the prediction interval includes the true value, time point n
# [1] 1 1 1 1
#
# $COST.G.pre.ML #length of the prediction interval
# [1] 1.445576 2.432646 2.260688 2.914887
#
# $COST.G.pre.med.error #point prediction error, using conditional median
# [1] 0.01127162 -0.03222058 -0.22081051 0.57831480
#
# $GP.pre.ECP #whether the prediction interval includes the true value, time point n
# [1] 1 1 1 1
#
# $GP.pre.ML #length of the prediction interval
# [1] 0.8345359 1.4096642 1.5948724 2.3419428
#
# $GP.pre.med.error #point prediction error, using conditional median
# [1] 0.09447685 -0.05889409 -0.08923935 0.58494684
|
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