INLAutils

A package containing utility functions for the R-INLA package.

There's a fair bit of overlap with inlabru.

Installation

To install, first install INLA.

install.packages("INLA", repos="https://www.math.ntnu.no/inla/R/stable")

then install INLAutils

# From github
library(devtools)
install_github('timcdlucas/INLAutils')
# Load packages
library(INLA)
library(INLAutils)

Overview

Plotting

I find the the plot function in INLA annoying and I like ggplot2. So INLAutils provides an autoplot method for INLA objects.

 data(Epil)
##Define the modelformula=y~Trt+Age+V4+
f(Ind, model="iid") + f(rand,model="iid")
result= inla(formula, family="poisson", data=Epil, control.predictor=list(compute=TRUE))
p<- autoplot(result)

Because these are ggplot2 objects, we can easily modify them.

# Find data names with names(p[[1]]$data)p[[1]] + geom_line(aes(colour=var), size=1.3) +palettetown::scale_colour_poke(pokemon='Oddish', spread=4)

There is an autoplot method for INLA SPDE meshes.

m=100points=matrix(runif(m*2), m, 2)
mesh= inla.mesh.create.helper(
points=points,
cutoff=0.05,
offset= c(0.1, 0.4),
max.edge= c(0.05, 0.5))
autoplot(mesh)

There are functions for plotting more diagnostic plots.

 data(Epil)
observed<-Epil[1:30, 'y']
Epil<- rbind(Epil, Epil[1:30, ])
Epil[1:30, 'y'] <-NA## make centered covariatesformula=y~Trt+Age+V4+
f(Ind, model="iid") + f(rand,model="iid")
result= inla(formula, family="poisson", data=Epil,
control.predictor=list(compute=TRUE, link=1))
ggplot_inla_residuals(result, observed, binwidth=0.1)

 ggplot_inla_residuals2(result, observed, se=FALSE)

## `geom_smooth()` using method = 'loess'

Finally there is a function for combining shapefiles, rasters (or INLA projections) and meshes. For more fine grained control the geoms defined in inlabru might be useful.

# Create inla projectorn<-20loc<-matrix(runif(n*2), n, 2)
mesh<- inla.mesh.create(loc, refine=list(max.edge=0.05))
projector<- inla.mesh.projector(mesh)
field<- cos(mesh$loc[,1]*2*pi*3)*sin(mesh$loc[,2]*2*pi*7)
projection<- inla.mesh.project(projector, field)
# And a shape filecrds<-loc[chull(loc), ]
SPls<- SpatialPolygons(list(Polygons(list(Polygon(crds)), ID='a')))
# plot
ggplot_projection_shapefile(projection, projector, SPls, mesh)

Analysis

There are some helper functions for general analyses.

INLAstep runs stepwise variable selection with INLA.

 data(Epil)
stack<- inla.stack(data=list(y=Epil$y),
A=list(1),
effects=list(data.frame(Intercept=1, Epil[3:5])))
result<- INLAstep(fam1="poisson", Epil,
in_stack=stack,
invariant="0 + Intercept",
direction='backwards',
include=3:5,
y='y',
y2='y',
powerl=1,
inter=1,
thresh=2)
result$best_formula

## y ~ 0 + Intercept + Base + Age + V4
## <environment: 0xb6b15b0>

 autoplot(result$best_model, which=1)

makeGAM helps create a function object for fitting GAMs with INLA.

 data(Epil)
formula<- makeGAM('Age', invariant='', linear= c('Age', 'Trt', 'V4'), returnstring=FALSE)
formula

## y ~ +Age + Trt + V4 + f(inla.group(Age), model = "rw2")
## <environment: 0xc1d0688>

result= inla(formula, family="poisson", data=Epil)

Spatial leave-one-out cross-validation (sloo-cv)

The package INLAutils provides an approach to run sloo-cv for INLA objects.

# generate a dataframe and INLA output for the function
set.seed(10)
coords<-data.frame(long= c(rnorm(70), rnorm(30, 3)), lat= rnorm(100))
x<-data.frame(x1= rnorm(100), x2= rnorm(100))# x1 no relat., x2 pos. relat.y<-x$x2*2+ rnorm(100)
dataf1<-sp::SpatialPointsDataFrame(coords=coords, data=data.frame(y=y, x))
mesh<-INLA::inla.mesh.2d(loc=sp::coordinates(dataf1), max.edge= c(3, 3),cutoff=1.3)
spde<-INLA::inla.spde2.matern(mesh, alpha=2)#SPDE model is definedA<-INLA::inla.spde.make.A(mesh, loc=sp::coordinates(dataf1))#projector matrixdataframe<-data.frame(dataf1) #generate dataframe with response and covariatemodform<-stats::as.formula(paste('y ~ -1+ x1 + x2 + y.intercept + f(spatial.field, model=spde)'))
stk<-INLA::inla.stack(data=list(y=dataframe$y), A=list(A, 1),
effects=list(list(spatial.field=1:spde$n.spde),
list(y.intercept= rep(1, length(dataframe$y)),
covariate=dataframe[c(-1)])), tag='est')
out<-INLA::inla(modform, family='normal',Ntrials=1, data=INLA::inla.stack.data(stk, spde=spde),
control.predictor=list(A=INLA::inla.stack.A(stk),link=1),
control.compute=list( config=TRUE),control.inla=list(int.strategy='eb'))
out.field<-INLA::inla.spde2.result(out,'spatial.field', spde, do.transf=TRUE)
range.out<-INLA::inla.emarginal(function(x) x, out.field$marginals.range.nominal[[1]])
# parameters for the SLOO processss<-20# sample size to process (number of SLOO runs)rad<- min(range.out, max(dist(coords)) /4) # define the radius of the spatial buffer surrounding the removed point. Make sure it isn't bigger than 25% of the study area (Le Rest 2014)modform<-y~-1+y.intercept+x1+x2+ f(spatial.field, model=spde)
alpha<-0.05# rmse and mae confidence intervals (1-alpha)# run the functioncv<- inlasloo(dataframe=dataframe, long='long', lat='lat',
y='y', ss=ss, rad=rad, modform=modform,
mesh=mesh, family='normal',
mae=TRUE)