# Manual In varycoef: Modeling Spatially Varying Coefficients

knitr::opts_chunk$set(echo = TRUE) library(varycoef)  ## Introduction The package varycoef contains methods to model and estimate varying coefficients. In its current version r packageVersion("varycoef") it supports: • only spatially varying coefficient (SVC) • different MLE approaches to model SVC and to give predictions. The methodology is based on [@Dambon2020]. ### Spatially Varying Coefficient Models We define a full SVC model as $$y(s) = x^{(1)}(s)\beta_1(s) + ... + x^{(p)}(s)\beta_p(s) + \epsilon(s)$$ with the coefficients represented by Gaussian random fields (GRF)$\beta_j(\cdot) \sim \mathcal N (\mu_j \textbf 1_n, C_j(\cdot, \cdot))$. That is, every coefficient$j = 1, ..., p$is distinctly defined by a mean$\mu_j$and a covariance matrix defined by an underlying covariance function$C_j(s_k, s_l) = \sigma_j^2 \phi_{\rho_j}(s_k, s_l)$, where$\sigma_j^2$is the variance and$\rho_j$is the scale of the GRF. Further,$\epsilon$is a nugget effect with variance$\tau^2$. However, there are some cases, where the assumption of a full SVC model is not applicable. We want to give options for covariates w.r.t.: 1. mixed SVC, i.e. as above with its respective mean (fixed effect) and random effect described by its GRF. 2. fixed effect, i.e. without an GRF modelling its spatial structure. 3. mean-zero SVC, i.e. only a zero-mean random effect modelling the spatial structure. That is why we generalize the model above. First, note that we can write it in matrix form as $$\textbf y(\textbf s) = \textbf X \beta(\textbf s) + \epsilon( \textbf s)$$ where$\textbf X$is our model matrix. Then we can write down the model divided into an fixed effect part and a random effect part: $$\textbf y(\textbf s) = \textbf X \mu + \textbf W \eta(\textbf s) + \epsilon( \textbf s)$$ where$\eta$is the joint mean-zero GRF. Note that both model are the same if$\textbf X = \textbf W$. Thus, we can specify options 1 to 3 from above by in- or excluding columns of$\textbf X$or$\textbf W$, respectively. ### Example To give a simple example, we start by sampling artificial data. So define an full SVC model as given above and sample from a regular grid using a help function: # number of SVC p <- 3 (pars <- data.frame(mean = rep(0, p), var = c(0.1, 0.2, 0.3), scale = c(0.3, 0.1, 0.2))) nugget.var <- 0.05 # sqrt of total number of observations m <- 10; n <- m^2 locs <- expand.grid( x = seq(0, 1, length.out = m), y = seq(0, 1, length.out = m) ) # function to sample SVCs sp.SVC <- sample_fullSVC( df.pars = pars, nugget.sd = sqrt(nugget.var), locs = as.matrix(locs), cov.name = "exp" ) str(sp.SVC) head(cbind(y = sp.SVC$y, X = sp.SVC$X))  ## MLE in varycoef The main function of this package is SVC_mle. Its function call starts the MLE but it requires some preparation and settings of control parameters. We go through each argument of the SVC_mle function and its control parameter SVC_mle.control. ### The Function SVC_mle As one might see in the help file of the SVC_mle function, it has 3 mandatory arguments: y, the response; X, the data matrix and locs, the locations. If we do not change W, i.e. W = NULL, then we use W = X and are in the case of a full SVC. We will give examples for different kinds of models. ### Control Parameters As for the control parameters for SVC_mle, we go through them as they are implemented in the current version of varycoef. By calling SVC_mle_control, we create an list with all needed arguments to start a simple SVC_mle. control <- SVC_mle_control() str(control)  #### Covariance Function Here we define the covariance function$C_j(s_k, s_l) = \sigma_j^2 \phi_{\rho_j}(s_k, s_l)$. In its current version r packageVersion("varycoef"), varycoef supports only exponential covariance functions, i.e.$\phi_{\rho}(s_k, s_l) = \exp\left(\frac{\|s_k - s_l\|}{\rho}\right)$. #### Tapering Covariance tapering goes back to [@Furrer2006] and is a technique to challenge the "big$n$problem" when dealing with spatial data. When working with$n$observations, the covariance matrix has dimension$n \times n$. The likelihood function of a single GRF or, in particular, the SVC models as described above, includes the inverse as well as the determinant of a covariance matrix. The computation of both is based on the Cholesky decomposition which has run-time$\mathcal O(n^3)$. There are several ways on how to deal with this computational burden. With covariance tapering, we introduce a sparsity structure on the covariance matrix. This sparsity structure can then be used to our advantage with the Cholesky decomposition implemented in the package spam, [@Rspam]. In a nutshell, this decomposition becomes faster as the sparsity increases. However, this comes with a trade-off in the precision of the MLE of the covariance parameters with finitely many observation. Asymptotically, the procedure is consistent. By default, the tapering entry is NULL, i.e. no tapering is applied. If a scalar is provided, then this is taper range is applies. In other words, every spatial dependency is cut for distances larger than tapering. We illustrate the difference between both the untapered and tapered covariance matrix of the SVC on the regular grid example from above. The function fullSVC_reggrid is used to sample SVCs for a full SVC model. W <- sp.SVC$X
q <- dim(W)
r <- c(0.5, 0.3, 0.1)
out <- lapply(c(list(NULL), as.list(r)), function(taper.range) {

d <- varycoef:::own_dist(x = locs, taper = taper.range)

# get covariance function
raw.cov.func <- varycoef:::MLE.cov.func("exp")

# covariance function
cov.func <- function(x) raw.cov.func(d, x)

outer.W <- lapply(1:q, function(k) {
if (is.null(taper.range)) {
W[, k]%o%W[, k]
} else {
W[, k]%o%W[, k]*spam::cov.wend1(d, c(taper.range, 1, 0))
}
})

# tapering?
taper <- if(is.null(taper.range)) {
# without tapering
NULL
} else {
# with tapering
spam::cov.wend1(d, c(taper.range, 1, 0))
}

x <- c(rep(1, 2*p+1), rep(0, p))

S_y <- varycoef:::Sigma_y(x, cov.func, outer.W, taper)

nll <- function() varycoef:::n2LL(x, cov.func, outer.W, sp.SVC$y, sp.SVC$X, W, mean.est = NULL, taper = taper)

list(d, taper, S_y, nll)
})

oldpar <- par(mfrow = c(2, 2))

image(out[][])
title(main = "No tapering applied")
image(out[][])
title(main = paste0("Taper range = ", r))
image(out[][])
title(main = paste0("Taper range = ", r))
image(out[][])
title(main = paste0("Taper range = ", r))

par(oldpar)


Finally, we show the time differences of evaluating the likelihood function between the different taper ranges. We use the microbenchmark package:

library(microbenchmark)

(mb <- microbenchmark(no_tapering  = out[][](),
tapering_0.5 = out[][](),
tapering_0.3 = out[][](),
tapering_0.1 = out[][]())
)

boxplot(mb, unit = "ms", log = TRUE, xlab = "tapering", ylab = "time (milliseconds)")


#### Regularizing Priors

We implemented a usage of penalizing complexity priors. The argument pc.prior takes a vector of length 4 as an input where the values are $\rho_0, \alpha_\rho, \sigma_0, \alpha_\sigma$ to compute penalized complexity priors. One wants to penalize the lower tail on the range parameter as well as the upper tail of the standard deviation:

$$P(\rho < \rho_0) = \alpha_\rho, \quad P(\sigma > \sigma_0) = \alpha_\sigma.$$

#### Parallelization

With version 0.2.10 varycoef is now able to parallelize the likelihood optimization. In each iteration step the objective function, i.e., a modified likelihood, has to be evaluated at several points in a small neighborhood. Using the package optimParallel [@FG2019], is can be done simultaneously. The procedure to do so is the following:

1. Initialize a cluster by parallel::makeCluster.

2. Create list containing this cluster, as one would with optimParallel::optimParallel. In this list other arguments towards the function optimParallel can be passed, see help file.

3. Set argument parallel to the created list.

4. Run SVC_mle as usual.

5. Make sure to stop cluster afterwards.

The code looks something like that:

require(varycoef)
require(parallel)
require(optimParallel)

# step 1: initialize cluster
cl <- makeCluster(detectCores()-1)

# step 2: create optimParallel control
parallel.control <- list(cl = cl, forward = TRUE, loginfo = FALSE)

# step 3: add control containing optimParallel controls
control.p <- control
control.p$parallel <- parallel.control # step 4: run SVC_mle fit.p <- SVC_mle(y = y, X = X, locs = coordinates(sp.SVC), control = control.p) # step 5: stop cluster stopCluster(cl); rm(cl) summary(fit.p) rm(control.p, fit.p)  #### Extract Function In some situations, it is useful to extract the objective function before starting the optimization itself. For instance, [@FG2019] states that the overhead of the parallelization set up results in a faster optimization only if the evaluation time of a single objective function is greater than 0.05 seconds. Another example where the extracted function is needed are machine specific issues regarding the optimization. ### Example: MLE of full SVC We can now start the MLE . The following function call takes a few second. control <- SVC_mle_control() fit <- SVC_mle(y = sp.SVC$y, X = sp.SVC$X, locs = as.matrix(locs), control = control)  The received object fit is of class r class(fit). For this class, there are numerous methods such as: # estimated ... # ... covariance parameters cov_par(fit) # ... mean effects coef(fit) # summary summary(fit) # residual plots oldpar <- par(mfrow = c(1, 2)) plot(fit, which = 1:2) par(mfrow = c(1, 1)) par(oldpar)  Now, we can use our fit object to make predictions: # calling predictions without specifying new locations (newlocs) or # new covariates (newX) gives estimates of SVC only at the training locations. pred.SVC <- predict(fit)  Since we know the true SVC, we can compute the error in prediction and compare it to the true values. library(sp) colnames(pred.SVC)[1:p] <- paste0("pred.",colnames(pred.SVC)[1:p]) coordinates(pred.SVC) <- ~loc_1+loc_2 all.SVC <- cbind(pred.SVC, sp.SVC$beta)

# compute errors
all.SVC$err.SVC_1 <- all.SVC$pred.SVC_1 - all.SVC$X1 all.SVC$err.SVC_2 <- all.SVC$pred.SVC_2 - all.SVC$X2
all.SVC$err.SVC_3 <- all.SVC$pred.SVC_3 - all.SVC$X3 colnames(all.SVC@data) <- paste0(rep(c("pred.", "true.", "err."), each = p), "SVC_", rep(1:p, 3)) spplot(all.SVC[, paste0(rep(c("true.", "err.", "pred."), each = p), "SVC_", 1:p)], colorkey = TRUE)  In this small example we already can see that the predicted SVC takes the general spatial structure of the true SVC. The error does not appear to have spatial structure for the SVC 2 and 3, respectively. However, the error for the intercept seems to have some spatial structure. If we increase the number of observations, the picture changes: knitr::include_graphics("figures/SVCs_result_n2500_p3.png")  We do not run the code since it takes a couple hours to do the MLE without parallelization, but here is the code to reproduce the figure: # new m m <- 50 # new SVC model sp.SVC <- fullSVC_reggrid(m = m, p = p, cov_pars = pars, nugget = nugget.var) spplot(sp.SVC, colorkey = TRUE) # total number of observations n <- m^2 X <- matrix(c(rep(1, n), rnorm((p-1)*n)), ncol = p) y <- apply(X * as.matrix(sp.SVC@data[, 1:p]), 1, sum) + sp.SVC@data[, p+1] fit <- SVC_mle(y = y, X = X, locs = coordinates(sp.SVC)) sp2500 <- predict(fit) colnames(sp2500)[1:p] <- paste0("pred.",colnames(sp2500)[1:p]) coordinates(sp2500) <- ~loc_x+loc_y all.SVC <- cbind(sp2500, sp.SVC[, 1:3]) # compute errors all.SVC$err.SVC_1 <- all.SVC$pred.SVC_1 - all.SVC$SVC_1
all.SVC$err.SVC_2 <- all.SVC$pred.SVC_2 - all.SVC$SVC_2 all.SVC$err.SVC_3 <- all.SVC$pred.SVC_3 - all.SVC$SVC_3

colnames(all.SVC@data) <- paste0(rep(c("pred.", "true.", "err."), each = p), "SVC_", rep(1:p, 3))

png(filename = "figures/SVCs_result_n2500_p3.png", width = 960, height = 960)
spplot(all.SVC[, paste0(rep(c("true.", "err.", "pred."), each = p),
"SVC_", 1:p)], colorkey = TRUE,
as.table = TRUE, layout = c(3, 3))
dev.off()


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varycoef documentation built on June 3, 2021, 5:10 p.m.