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A package containing utility functions for the R-INLA package.
There's a fair bit of overlap with inlabru.
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)
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 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.
# Find data names with names(p[[1]]$data) p[[1]] + geom_line(aes(colour = var), size = 1.3) + palettetown::scale_colour_poke(pokemon = 'Oddish', spread = 4)
There is an autoplot method for INLA SPDE meshes.
m = 100 points = matrix(runif(m * 2), m, 2) mesh = inla.mesh.create.helper( points = points, cutoff = 0.05, offset = c(0.1, 0.4), max.edge = c(0.05, 0.5)) autoplot(mesh)
There are functions for plotting more diagnostic plots.
data(Epil) observed <- Epil[1:30, 'y'] Epil <- rbind(Epil, Epil[1:30, ]) Epil[1:30, 'y'] <- NA ## make centered covariates formula = y ~ Trt + Age + V4 + f(Ind, model="iid") + f(rand,model="iid") result = inla(formula, family="poisson", data = Epil, control.predictor = list(compute = TRUE, link = 1)) ggplot_inla_residuals(result, observed, binwidth = 0.1)
ggplot_inla_residuals2(result, observed, se = FALSE)
## `geom_smooth()` using method = 'loess'
Finally there is a function for combining shapefiles, rasters (or INLA projections) and meshes. For more fine grained control the geoms defined in inlabru might be useful.
# Create inla projector n <- 20 loc <- matrix(runif(n*2), n, 2) mesh <- inla.mesh.create(loc, refine=list(max.edge=0.05)) projector <- inla.mesh.projector(mesh) field <- cos(mesh$loc[,1]*2*pi*3)*sin(mesh$loc[,2]*2*pi*7) projection <- inla.mesh.project(projector, field) # And a shape file crds <- loc[chull(loc), ] SPls <- SpatialPolygons(list(Polygons(list(Polygon(crds)), ID = 'a'))) # plot ggplot_projection_shapefile(projection, projector, SPls, mesh)
There are some helper functions for general analyses.
INLAstep runs stepwise variable selection with INLA.
data(Epil) stack <- inla.stack(data = list(y = Epil$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)
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$x2 * 2 + rnorm(100) dataf1 <- sp::SpatialPointsDataFrame(coords = coords, data = data.frame(y = y, x)) mesh <- INLA::inla.mesh.2d(loc = sp::coordinates(dataf1), max.edge = c(3, 3),cutoff = 1.3) spde <- INLA::inla.spde2.matern(mesh, alpha=2)#SPDE model is defined A <- INLA::inla.spde.make.A(mesh, loc = sp::coordinates(dataf1))#projector matrix dataframe <- data.frame(dataf1) #generate dataframe with response and covariate modform<-stats::as.formula(paste('y ~ -1+ x1 + x2 + y.intercept + f(spatial.field, model=spde)')) stk <- INLA::inla.stack(data = list(y=dataframe$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)
