inlabru (version 2.1.9)

devel.cvmeasure: Variance and correlations measures for prediction components

Description

Calculates local and integrated variance and correlation measures as introduced by Yuan et al. (2017).

Usage

devel.cvmeasure(joint, prediction1, prediction2, samplers = NULL,
  mesh = NULL)

Arguments

joint

A joint prediction of two latent model components.

prediction1

A prediction of first component.

prediction2

A prediction of the first component.

samplers

A SpatialPolygon object describing the area for which to compute the cummulative variance measure.

mesh

The inla.mesh for which the prediction was performed (required for cummulative Vmeasure).

Value

Variance and correlations measures.

References

Y. Yuan, F. E. Bachl, F. Lindgren, D. L. Brochers, J. B. Illian, S. T. Buckland, H. Rue, T. Gerrodette. 2017. Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales. https://arxiv.org/abs/1604.06013

Examples

# NOT RUN {
if (require("INLA", quietly = TRUE)) {
  
# Load Gorilla data

data("gorillas", package = "inlabru")

# Use RColorBrewer 

library(RColorBrewer)

# Fit a model with two components:
# 1) A spatial smoothe SPDE 
# 2) A spatial covariate effect (vegetation)

pcmatern <- inla.spde2.pcmatern(gorillas$mesh, 
                                prior.sigma = c(0.1, 0.01), 
                                prior.range = c(5, 0.01))

cmp <- coordinates ~ vegetation(map = gorillas$gcov$vegetation, model = "factor") +
  spde(map = coordinates, model = pcmatern, mesh = gorillas$mesh) -
  Intercept

fit <- lgcp(cmp, gorillas$nests, samplers = gorillas$boundary)

# Predict SPDE and vegetation at the mesh vertex locations

vrt = vertices(gorillas$mesh)
joint <- predict(fit, vrt,  ~ spde + vegetation)
field <- predict(fit, vrt, ~ spde)
veg <- predict(fit, vrt, ~ vegetation)

# Plot component mean 

multiplot(ggplot() + gg(gorillas$mesh, color = joint$mean) + 
            coord_equal() + theme(legend.position= "bottom"),
          ggplot() + gg(gorillas$mesh, color = field$mean) + 
            coord_equal() + theme(legend.position= "bottom"),
          ggplot() + gg(gorillas$mesh, color = veg$mean) + 
            coord_equal() + theme(legend.position = "bottom"),
          cols = 3)

# Plot component variance 

multiplot(ggplot() + gg(gorillas$mesh, color = joint$var) + 
            coord_equal() + theme(legend.position = "bottom"),
          ggplot() + gg(gorillas$mesh, color = field$var) + 
            coord_equal() + theme(legend.position = "bottom"),
          ggplot() + gg(gorillas$mesh, color = veg$var) + 
            coord_equal() + theme(legend.position = "bottom"),
          cols = 3)

# Calculate variance and correlation measure

vm <- devel.cvmeasure(joint, field, veg)
lprange <- range(vm$var.joint, vm$var1,vm$var2)

# Variance contribution of the components

csc <- scale_fill_gradientn(colours = brewer.pal(9,"YlOrRd"), limits = lprange)
boundary <- gorillas$boundary

plot.1 <- ggplot() + gg(gorillas$mesh, color = vm$var.joint, mask = boundary) + 
  csc + coord_equal() + ggtitle("joint") + theme(legend.position = "bottom")
plot.2 <- ggplot() + gg(gorillas$mesh, color = vm$var1, mask = boundary) + 
  csc + coord_equal() + ggtitle("SPDE") + theme(legend.position = "bottom")
plot.3 <- ggplot() + gg(gorillas$mesh, color = vm$var2, mask = boundary) + 
  csc + coord_equal() + ggtitle("vegetation") + theme(legend.position = "bottom")

multiplot(plot.1, plot.2, plot.3, cols = 3)

# Covariance of SPDE field and vegetation

ggplot() + gg(gorillas$mesh, color = vm$cov) 

# Correlation between field and vegetation

ggplot() + gg(gorillas$mesh, color = vm$cor) 

# Variance and correlation integrated over space

vm.int <- devel.cvmeasure(joint, field, veg, 
                          samplers = ipoints(gorillas$boundary, gorillas$mesh),
                          mesh = gorillas$mesh)
vm.int

}
# }