In lcgodoy/smile: Spatial Misalignment: Interpolation, Linkage, and Estimation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

If interested in the theory involving this calculations, take a look at this vignette.

These functions will work only for planar projections. If working with a spherical projection, we recommend using a powered exponential covariance function with $\kappa \in (0, 1)$.

library(smile)
library(ggplot2)
library(sf)

Considering the same setup used in the vignette about converting sf to spm objects, we are going to predict the Life Expectation at Birth (LEF) score (observed at the Middle Super Output Areas (MSOA), in Liverpool, into the Lower Super Output Area (LSOA). In this second level, we have the Life Expectancy at Birth for each area. This data is available in the package and was made available by @johnson2020dealing.

The code below loads the data and converts the LSOA sf object into a spm object. We are going to use numerical integration (based on a 500 points grid) with the precision of the integrals varying according to the size of each MSOA region. We are interested in a variable called "leb_est" available only at the liv_msoa dataset. ```{R load-data} data(liv_msoa) data(liv_lsoa)

workaround for compatibility with different PROJ versions

st_crs(liv_msoa) <- st_crs(liv_msoa)$input

st_crs(liv_lsoa) <- st_crs(liv_lsoa)$input

msoa from sf to spm

spm_msoa <- sf_to_spm(sf_obj = liv_msoa, n_pts = 500, type = "regular", by_polygon = FALSE, poly_ids = "msoa11cd", var_ids = "leb_est")

Next, we assume the following model, at the grid level, drives the data^[To
check how the model at the grid level relates to the data at the area level
check the [Theory vignette](https://lcgodoy.me/smile/articles/theory.html)] 

$$Y(\mathbf{s}) = \mu + S(\mathbf{s}) + \varepsilon(\mathbf{s}), $$ 

where $S(\cdot) \sim \mathrm{GP}(0, \mathrm{C}(\cdot ; \,
\boldsymbol{\theta}))$, and $\varepsilon(\cdot) \overset{\mathrm{i.i.d.}}{\sim}
\mathrm{N}(0, \tau)$, with $S(\cdot) \perp \varepsilon(\cdot)$. The form of the
covariance functions considered here are written as follows $\mathrm{C}(\cdot ;
\, \boldsymbol{\theta} ) = \sigma^2 \rho(\cdot ; \, \boldsymbol{\theta})$. Where
$\rho(\cdot ; \, \boldsymbol{\theta})$ is a stationary and isotropic correlation
function. To check the families of correlation functions available in our
package, check [this
link](https://lcgodoy.me/smile/articles/sp-cov-functions.html). Another
important detail is that, if we make $\alpha = \tau / \sigma^2$, the estimation
process is slightly easier. The package allows for the two parametrizations.


For this problem we are going to consider the the Matérn covariance
function. The model will be fitted with $\nu \in \{0.5, 1.5, 2.5\}$, this the parameter
controls the smoothness of the spatial process. Also, for simplicity, we are
going to ignore $\tau$. Next, we evaluate the AIC associated with three
models. The function `fit_spm` fits the models and needs starting values for
$\phi$ (and $\alpha$ or $\tau$, if you decide not to ignore the small scale
variation), the covariance model to be used is informed as a string in the
argument `model`, the available options are `c("matern", "pexp", "gaussian",
"spherical")`. Notice that, when not inputting an initial value for $\alpha$ (or
$\tau$), we are forcing this parameter to be 0. Also, the `theta_st` argument
(which takes the initial values for the parameters), must be a named vector. If
we input initial values for $\mu$, $\sigma^2$, $\phi$, and $\alpha$ (or $\tau$),
the likelihood is optimized numerically for all the parameters. If we omit $\mu$
and $\sigma^2$, then a profile likelihood approach is used to find numerically
$\hat{\phi}$ and, if wished, $\hat{\alpha}$. While, $\hat{\mu}$ and
$\hat{\sigma}^2$ have closed form expressions [@diggle2007model]. Additionally,
the commented part of the code below shows how to proceed to estimate $\alpha$
(or $\tau$) from the data. The argument `apply_exp` is a workaround for
parameters that cannot be negative. The arguments `opt_method` and `control` are
passed to the function `optim`. `opt_method` controls the optimization algorithm
to be used, while `control` inputs control arguments for such optimization
algorithms, for more details see
[optim](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/optim.html).
```{R fit-pred-1}
theta_st_msoa <- c("phi" = 1)

## 1) it is important to NAME the initial values for each parameter
## 2) to estimate "nu" from the data we only need to provide an initial value for such
## parameter
## 3) uncomment the code below to do so.
## 4) Note that it is possible to set the boundaries for the parameter space on
## which we want to optmize the likelihood.
## theta_st_msoa <- c("phi" = 1, "nu" = 1)
## fit_msoa1 <-
##     fit_spm(x = spm_msoa,
##             theta_st = theta_st_msoa,
##             model = "matern",
##             nu = .5,
##             lower = c(1e-16, 1e-16),
##             upper = c(Inf, 1),
##             opt_method = "L-BFGS-B",
##             control   = list(maxit = 500))

fit_msoa1 <-
    fit_spm(x = spm_msoa,
            theta_st = theta_st_msoa,
            model = "matern",
            nu = .5,
            apply_exp = TRUE,
            opt_method = "L-BFGS-B",
            control   = list(maxit = 500))

fit_msoa2 <-
    fit_spm(x = spm_msoa,
            theta_st = theta_st_msoa,
            model = "matern",
            nu = 1.5,
            apply_exp = TRUE,
            opt_method = "L-BFGS-B",
            control    = list(maxit = 500))

fit_msoa3 <-
    fit_spm(x = spm_msoa,
            theta_st = theta_st_msoa,
            model = "matern",
            nu = 2.5,
            apply_exp = TRUE,
            opt_method = "L-BFGS-B",
            control   = list(maxit = 500))

The AIC^[BIC works for our models too.] associated with each model is given below. According to this criterion, the best model is the one with $\nu = .5$.

c("m1" = AIC(fit_msoa1), "m2" = AIC(fit_msoa2), "m3" = AIC(fit_msoa3))

We can retrieve the estimated parameters and the associated (1 - sig) % confidence intervals by using the function summary_spm_fit as follows ```{R summary-fit1} summary_spm_fit(fit_msoa1, sig = .05)

Finally, we have almost everything we need to perform predictions at the LSOA
areas. The function `predict_spm` calculates the predictions associated to the
`spm_obj` (which contains the results associated with a fitted model) into a
`sf` object given as an input for the argument `x`. We need to specify whether
we want to "aggregate" (i.e. average) the predicted surface over the polygons
associated with the `sf` data. The number of points and the type of integration
for prediction need to be set as well. In this case we are specifying a finer
grid (3500 points) because the LSOA areas are smaller than the MSOA areas.
```{R predict-fit1}
pred_lsoa <- predict_spm(x = liv_lsoa, spm_obj = fit_msoa1, id_var = "lsoa11cd")

The resulting object is a list with entries "mu_pred", "sig_pred", "pred_grid", "pred_agg". The first two positions correspond to the mean and covariance matrix at the predicted locations, respectively. "pred_grid" can be seen as the predicted surface over the study region, while "pred_agg" contains the (integrals) averages of such surface within each LSOA area. The chunk of code below plots the predicted life expectancy at the LSOA areas. ```{R manip-pred-1, fig.alt="Estimated LEB at LSOA in Liverpool."} ggplot(data = pred_lsoa$pred_agg, aes(fill = mu_pred)) + geom_sf(color = 1, lwd = .1) + scale_fill_viridis_c(option = "H") + guides(fill = "none") + theme_bw() + theme(axis.text = element_blank(), axis.ticks = element_blank())

Next, we plot the standard errors associated with the predictions.
```{R se-ggplot2, fig.alt="Standard errors of the estimates for LEB at LSOA in Liverpool."}
ggplot(data = pred_lsoa$pred_agg,
       aes(fill  = se_pred)) +
    geom_sf(color = 1,
            lwd = .1) +
    scale_fill_viridis_c(option = "H") +
    guides(fill = "none") +
    theme_bw() +
    theme(axis.text  = element_blank(),
          axis.ticks = element_blank())