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README.Rmd
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README.Rmd
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INLAutils
==========
[](https://travis-ci.org/timcdlucas/INLAutils)
[](https://codecov.io/github/timcdlucas/INLAutils?branch=master)
[](https://cran.rstudio.com/web/packages/INLAutls)
A package containing utility functions for the `R-INLA` package.
There's a fair bit of overlap with [inlabru](http://www.github.com/fbachl/inlabru).
Installation
-------------
To install, first install `INLA`.
```{r installINLA, eval = FALSE}
install.packages("INLA", repos="https://www.math.ntnu.no/inla/R/stable")
```
then install `INLAutils`
```{r installINLAutils, eval = FALSE}
# From github
library(devtools)
install_github('timcdlucas/INLAutils')
# Load packages
library(INLA)
library(INLAutils)
```
```{r loadINLAutils, eval = TRUE, echo = FALSE, results = 'hide', message=FALSE, warning=FALSE}
# Now actually load the packages. This chunk is hidden
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.
```{r autoplot, eval = TRUE, echo = TRUE}
data(Epil)
##Define the model
formula = 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.
```{r autoplot2, eval = TRUE, echo = TRUE}
# Find data names with names(p[[1]]$data)
p[[1]] +
geom_line(aes(colour = var), size = 1.3) +
palettetown::scale_colour_poke(pokemon = 'Oddish', spread = 4)
```
There is an autoplot method for INLA SPDE meshes.
```{r autoplot_mesh, eval = TRUE, echo = TRUE}
m = 100
points = matrix(runif(m * 2), m, 2)
mesh = inla.mesh.create.helper(
points = points,
cutoff = 0.05,
offset = c(0.1, 0.4),
max.edge = c(0.05, 0.5))
autoplot(mesh)
```
There are functions for plotting more diagnostic plots.
```{r plot_residuals, eval = TRUE, echo = TRUE}
data(Epil)
observed <- Epil[1:30, 'y']
Epil <- rbind(Epil, Epil[1:30, ])
Epil[1:30, 'y'] <- NA
## make centered covariates
formula = y ~ Trt + Age + V4 +
f(Ind, model="iid") + f(rand,model="iid")
result = inla(formula, family="poisson", data = Epil,
control.predictor = list(compute = TRUE, link = 1))
ggplot_inla_residuals(result, observed, binwidth = 0.1)
ggplot_inla_residuals2(result, observed, se = FALSE)
```
Finally there is a function for combining shapefiles, rasters (or INLA projections) and meshes.
For more fine grained control the geoms defined in [inlabru](http://www.github.com/fbachl/inlabru) might be useful.
```{r shapefileraster,eval = TRUE, echo = TRUE, message = FALSE}
# Create inla projector
n <- 20
loc <- matrix(runif(n*2), n, 2)
mesh <- inla.mesh.create(loc, refine=list(max.edge=0.05))
projector <- inla.mesh.projector(mesh)
field <- cos(mesh$loc[,1]*2*pi*3)*sin(mesh$loc[,2]*2*pi*7)
projection <- inla.mesh.project(projector, field)
# And a shape file
crds <- loc[chull(loc), ]
SPls <- SpatialPolygons(list(Polygons(list(Polygon(crds)), ID = 'a')))
# plot
ggplot_projection_shapefile(projection, projector, SPls, mesh)
```
### Analysis
There are some helper functions for general analyses.
`INLAstep` runs stepwise variable selection with INLA.
```{r INLAstep, eval = TRUE, echo = TRUE, message = FALSE}
data(Epil)
stack <- inla.stack(data = list(y = Epil$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$best_formula
autoplot(result$best_model, which = 1)
```
`makeGAM` helps create a function object for fitting GAMs with INLA.
```{r GAM, eval = TRUE, echo = TRUE}
data(Epil)
formula <- makeGAM('Age', invariant = '', linear = c('Age', 'Trt', 'V4'), returnstring = FALSE)
formula
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.
```{r inlasloo, eval = TRUE, echo = TRUE, results = 'hide', message = FALSE}
# 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$x2 * 2 + rnorm(100)
dataf1 <- sp::SpatialPointsDataFrame(coords = coords, data = data.frame(y = y, x))
mesh <- INLA::inla.mesh.2d(loc = sp::coordinates(dataf1), max.edge = c(3, 3),cutoff = 1.3)
spde <- INLA::inla.spde2.matern(mesh, alpha=2)#SPDE model is defined
A <- INLA::inla.spde.make.A(mesh, loc = sp::coordinates(dataf1))#projector matrix
dataframe <- data.frame(dataf1) #generate dataframe with response and covariate
modform<-stats::as.formula(paste('y ~ -1+ x1 + x2 + y.intercept + f(spatial.field, model=spde)'))
stk <- INLA::inla.stack(data = list(y=dataframe$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 process
ss <- 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 function
cv <- inlasloo(dataframe = dataframe,
long = 'long', lat = 'lat',
y = 'y', ss = ss,
rad = rad, modform = modform,
mesh = mesh, family = 'normal',
mae = TRUE)
```