cocoPredict {cocons}R Documentation

Prediction for coco objects

Description

Computes the conditional expectation and standard errors based on the conditional Gaussian distribution for nonstationary spatial models.

Usage

cocoPredict(coco.object, newdataset, newlocs, type = 'mean', ...)

Arguments

coco.object

(S4) A fitted coco object.

newdataset

(data.frame) A data.frame containing the covariates present in model.list at the prediction locations.

newlocs

(matrix) A matrix specifying the prediction locations, matching newdataset index.

type

(character) Specifies whether to return only the point prediction ('mean') or both the point prediction and prediction standard errors ('pred').

...

Additional arguments. If coco.object contains multiple realizations, the argument index.pred can be used to specify which realization of coco.object@z should be used for predictions.

Value

A list containing:

Author(s)

Federico Blasi

Examples

## Not run: 

# Stationary model

model.list_stat <- list('mean' = 0,
'std.dev' = formula( ~ 1),
'scale' = formula( ~ 1),
'aniso' = 0,
'tilt' = 0,
'smooth' = 3/2,
'nugget' = -Inf)

 
model.list_ns <- list('mean' = 0,
'std.dev' = formula( ~ 1 + cov_x + cov_y),
'scale' = formula( ~ 1 + cov_x + cov_y),
'aniso' = 0,
'tilt' = 0,
'smooth' = 3/2,
'nugget' = -Inf)

coco_object <- coco(type = 'dense',
data = holes[[1]][1:100, ],
locs = as.matrix(holes[[1]][1:100, 1:2]),
z = holes[[1]][1:100, ]$z,
model.list = model.list_stat)

optim_coco_stat <- cocoOptim(coco_object,
boundaries = getBoundaries(coco_object,
lower.value = -3, 3))

coco_preds_stat <- cocoPredict(optim_coco_stat, newdataset = holes[[2]],
newlocs = as.matrix(holes[[2]][, 1:2]),
type = "pred")

# Update model
coco_object@model.list <- model.list_ns

optim_coco_ns <- cocoOptim(coco_object,
boundaries = getBoundaries(coco_object,
lower.value = -3, 3))

coco_preds_ns <- cocoPredict(optim_coco_ns, newdataset = holes[[2]],
newlocs = as.matrix(holes[[2]][, 1:2]),
type = "pred")

par(mfrow = c(1, 3))

fields::quilt.plot(main = "full data", holes[[1]][, 1:2], 
holes[[1]]$z, xlim = c(-1, 1), ylim = c(-1, 1))

fields::quilt.plot(main = "stationary se", holes[[2]][, 1:2], 
coco_preds_stat$sd.pred, xlim = c(-1, 1), ylim = c(-1, 1))
fields::quilt.plot(main = "nonstationary se", holes[[2]][, 1:2], 
coco_preds_ns$sd.pred, xlim = c(-1, 1), ylim = c(-1, 1))



## End(Not run)


[Package cocons version 0.1.4 Index]