README.Rmd

INLAutils
==========

[![Build Status](https://travis-ci.org/timcdlucas/INLAutils.svg)](https://travis-ci.org/timcdlucas/INLAutils)
[![codecov.io](https://codecov.io/github/timcdlucas/INLAutils/coverage.svg?branch=master)](https://codecov.io/github/timcdlucas/INLAutils?branch=master)
[![cran version](http://www.r-pkg.org/badges/version/INLAutils)](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 CRAN
install.packages('INLAutils')

# 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))
     
      autoplot(result)
```


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)
```





### Domain specific applications



To do list
----------

* `inla.sdm`
* ggplot2 version of `plot.inla.tremesh`
* Make good `plot` and `ggplot` functions for plotting the Gaussian Random Field with value and uncertainty.
* `stepINLA`