example.prediction {COST} R Documentation

## example for new location prediction

### Description

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.

### Usage

```example.prediction(n,n.total,seed1)
```

### Arguments

 `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

### Value

 `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

### Author(s)

Yanlin Tang and Huixia Judy Wang

### References

Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.

### Examples

```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
#
# \$COST.t.pre.ML #length of the prediction interval
#  1.445576 2.146452 2.260688 2.706681
#
# \$COST.t.pre.med.error #point prediction error, using conditional median
#   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
#
# \$COST.G.pre.ML #length of the prediction interval
#  1.445576 2.432646 2.260688 2.914887
#
# \$COST.G.pre.med.error #point prediction error, using conditional median
#  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
#
# \$GP.pre.ML #length of the prediction interval
#  0.8345359 1.4096642 1.5948724 2.3419428
#
# \$GP.pre.med.error #point prediction error, using conditional median
#  0.09447685 -0.05889409 -0.08923935  0.58494684
```

[Package COST version 0.1.0 Index]