Overview

RASCO is an R package that allow the practioners to fit restricted spatial models for three class of models:

  • Generalized linear mixed models

  • Shared component models

  • Spatial frailty models

Installing LSB Core

You may need to install the LSB Core

lsb_release is part of a software package called the LSB core, which is not necessarily installed on your system by default. To install it, run the command below that corresponds to your specific system:

Ubuntu, Debian

sudo apt-get update && sudo apt-get install lsb-core

CentOS

sudo yum update && sudo yum install redhat-lsb-core

Fedora

sudo dnf update && sudo dnf install redhat-lsb-core

OpenSUSE

sudo zypper update && sudo zypper install lsb-core

Arch

pacman -Syu lsb-release

Installation

# Install from CRAN (when available)
install.packages("RASCO")
# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("DouglasMesquita/RASCO")
library(RASCO)
library(spdep)

set.seed(11022020)

Restricted generalized linear mixed models

##-- Spatial structure
data("neigh_RJ")

beta <- c(-0.1, 0.7)
tau <- 1

##-- Data ----
family <- "poisson"
data <- rglmm(beta = beta, tau = tau, family = family,
              confounding = "linear", neigh = neigh_RJ,
              scale = TRUE)

##-- Models ----
sglm_mod <- rsglmm(data = data, formula = Y ~ X1 + X2,
                   family = family,
                   proj = "none", nsamp = 1000)

sglmm_mod <- rsglmm(data = data, formula = Y ~ X1 + X2,
                    area = "reg", model = "besag", neigh = neigh_RJ,
                    family = family,
                    proj = "none", nsamp = 1000)

rglmm_mod <- rsglmm(data = data, formula = Y ~ X1 + X2,
                    area = "reg", model = "r_besag", neigh = neigh_RJ,
                    family = family,
                    proj = "rhz", nsamp = 1000)

sglm_mod$unrestricted$summary_fixed
#>                  mean    median      mode       sd     lower    upper
#> (Intercept)  0.290180  0.293232  0.297064 0.096921  0.084517 0.464152
#> X1          -0.112440 -0.111390 -0.110643 0.083823 -0.270183 0.053553
#> X2           0.422433  0.422807  0.416497 0.095397  0.221108 0.604123
sglmm_mod$unrestricted$summary_fixed
#>                  mean    median      mode       sd     lower    upper
#> (Intercept)  0.193310  0.192793  0.195883 0.112536 -0.019229 0.408931
#> X1          -0.105333 -0.108120 -0.116720 0.097176 -0.299285 0.075365
#> X2           0.554684  0.524568  0.511294 0.254681  0.046469 1.043592
rglmm_mod$unrestricted$summary_fixed
#>                  mean    median      mode       sd     lower    upper
#> (Intercept)  0.192251  0.189350  0.183941 0.110390 -0.024940 0.390233
#> X1          -0.104634 -0.107476 -0.105396 0.096672 -0.302749 0.074206
#> X2           0.553324  0.518179  0.485339 0.268085  0.053777 1.120851
rglmm_mod$restricted$summary_fixed
#>                  mean    median      mode       sd     lower    upper
#> (Intercept)  0.192251  0.189350  0.183941 0.110390 -0.024940 0.390233
#> X1          -0.120125 -0.118708 -0.119207 0.086898 -0.287106 0.054653
#> X2           0.428399  0.427712  0.426570 0.101527  0.214849 0.608716

sglmm_mod$unrestricted$summary_hyperpar
#>                       mean   median     mode       sd   lower    upper
#> Precision for reg 93.31269 2.227233 1.487469 464.0461 0.40322 415.3316

The SVIF function provides the coefficients variance ratio between two models. We can notice that the restricted model alleviates the variance inflation

SVIF(rglmm_mod$restricted, rglmm_mod$unrestricted)
#>     parameter      VIF
#> 1 (Intercept) 1.000000
#> 2          X1 1.237604
#> 3          X2 6.972394

Restricted shared component models

##-- Spatial structure
data("neigh_RJ")

##-- Parameters
alpha_1 <- 0.5
alpha_2 <- 0.1
beta_1 <- c(-0.5, -0.2)
beta_2 <- c(-0.8, -0.4)
tau_s <- 1
tau_1 <- tau_2 <- 10
delta <- 1.5

##-- Data
data <- rshared(alpha_1 = alpha_1, alpha_2 = alpha_2, beta_1 = beta_1, beta_2 = beta_2, delta = delta,
                tau_1 = tau_1, tau_2 = tau_2, tau_s = tau_s,
                confounding = "linear",
                neigh = neigh_RJ)

##-- Models
scm_mod <- rscm(data = data,
                formula1 = Y1 ~ X11 + X12,
                formula2 = Y2 ~ X21 + X12,
                E1 = E1, E2 = E2,
                family = c("poisson", "poisson"),
                area = "reg", neigh = neigh_RJ,
                priors = list(prior_prec = c(0.5, 0.05), 
                              prior_gamma = c(0, 0.5)),
                proj = "none", nsamp = 1000,
                random_effects = list(shared = TRUE, specific_1 = TRUE, specific_2 = TRUE))
#> priors$prior_prec should be a list of tau_s, tau_1 and tau_2 entries.
#>                      Setting all priors as Gamma(0.5, 0.05).

rscm_mod <- rscm(data = data,
                 formula1 = Y1 ~ X11 + X12,
                 formula2 = Y2 ~ X21 + X12,
                 E1 = E1, E2 = E2,
                 family = c("poisson", "poisson"),
                 area = "reg", neigh = neigh_RJ,
                 priors = list(prior_prec = list(tau_s = c(0.5, 0.05), 
                                                 tau_1 = c(0.5, 0.05), 
                                                 tau_2 = c(0.5, 0.05)), 
                               prior_gamma = c(0, 0.5)),
                 proj = "spock", nsamp = 1000,
                 random_effects = list(shared = TRUE, specific_1 = TRUE, specific_2 = TRUE))

##-- Summary
scm_mod$summary_fixed
#>             mean    median      mode       sd     lower     upper
#> alpha1  0.468548  0.470915  0.470332 0.066425  0.342601  0.599288
#> alpha2  0.093245  0.093468  0.096582 0.054381 -0.014831  0.197089
#> X11_1  -0.458648 -0.455249 -0.455418 0.107509 -0.670253 -0.251454
#> X12_1  -0.556626 -0.565192 -0.556368 0.727899 -2.011461  0.815949
#> X21_2  -0.908133 -0.908408 -0.910508 0.057989 -1.015133 -0.790359
#> X12_2  -0.385854 -0.374461 -0.361140 0.315915 -0.969523  0.239076
rscm_mod$summary_fixed
#>             mean    median      mode       sd     lower     upper
#> alpha1  0.514514  0.514852  0.518343 0.062271  0.391794  0.628163
#> alpha2  0.107683  0.108888  0.108218 0.052797  0.002684  0.205958
#> X11_1  -0.556916 -0.554289 -0.549720 0.111126 -0.769681 -0.333068
#> X12_1  -0.191676 -0.196055 -0.199189 0.150284 -0.487269  0.095290
#> X21_2  -0.945358 -0.946199 -0.946929 0.052217 -1.039213 -0.834244
#> X12_2  -0.099804 -0.098899 -0.095106 0.069917 -0.228329  0.037779

scm_mod$summary_hyperpar
#>                           mean    median      mode        sd    lower     upper
#> Precision for psi     2.297820  2.213172  2.137552  0.612074 1.233218  3.534963
#> Precision for phi1   13.357326  9.728952  7.277940 12.234102 0.807289 36.489783
#> Precision for phi2   17.315173 14.488147 12.275385 10.875393 3.090969 38.617265
#> Beta for psi_gamma    2.592330  2.574339  2.561000  0.282854 2.060594  3.161575
#> Delta                 1.602811  1.596515  1.591446  0.087321 1.434889  1.768535
#> Precision for psi SC  0.878675  0.866814  0.852711  0.174781 0.585320  1.266731
rscm_mod$summary_hyperpar
#>                           mean    median      mode        sd    lower     upper
#> Precision for psi     0.802387  0.775819  0.750927  0.201922 0.445629  1.208257
#> Precision for phi1   13.995620  9.359445  6.070808 14.971263 0.257390 42.276127
#> Precision for phi2   22.518171 16.917707 12.112072 19.809224 0.855579 60.670636
#> Beta for psi_gamma    2.607235  2.591025  2.577442  0.254488 2.127917  3.118987
#> Delta                 1.613391  1.612117  1.606086  0.077287 1.459759  1.756817
#> Precision for psi SC  0.305762  0.301283  0.293250  0.060613 0.199598  0.427545
SVIF(rscm_mod, scm_mod)
#>   parameter        VIF
#> 1    alpha1  1.1378669
#> 2    alpha2  1.0609035
#> 3     X11_1  0.9359621
#> 4     X12_1 23.4593921
#> 5     X21_2  1.2332962
#> 6     X12_2 20.4162006

Restricted spatial frailty models

#-- Spatial structure
data("neigh_RJ")

##-- Individuals and regions
n_reg <- length(neigh_RJ)
n_id <- sample(x = 3:5, size = n_reg, replace = T)
beta <- c(0.3, -0.3)
tau <- 0.75 # Scale of spatial effect

##-- Data
data <- rsurv(n_id = n_id,
              coefs = beta, cens = 0.5, scale = FALSE,
              cens_type = "right", hazard = "weibull",
              hazard_params = hazard_params <- list(weibull = list(alpha = 1.2, variant = 0)),
              spatial = "ICAR",
              neigh = neigh_RJ, tau = tau, confounding = "linear", proj = "none")

##-- Models
weibull_mod <- rsfm(data = data,
                    formula = surv(time = L, event = status) ~ X1 + X2,
                    model = "none", family = "weibull",
                    proj = "rhz", nsamp = 1000, approach = "inla")

rsfm_mod <- rsfm(data = data, area = "reg",
                 formula = surv(time = L, event = status) ~ X1 + X2,
                 model = "r_besag", neigh = neigh_RJ, family = "weibull",
                 proj = "rhz", nsamp = 1000, approach = "inla")

weibull_mod$unrestricted$summary_fixed
#>                  mean    median      mode       sd     lower     upper
#> (Intercept) -0.458939 -0.456202 -0.455831 0.077948 -0.609153 -0.308553
#> X1           0.366999  0.366651  0.368283 0.076112  0.217046  0.505295
#> X2          -0.652859 -0.656143 -0.660494 0.089487 -0.812906 -0.464613
rsfm_mod$unrestricted$summary_fixed
#>                  mean    median      mode       sd     lower     upper
#> (Intercept) -0.435600 -0.435694 -0.441499 0.076557 -0.581959 -0.288991
#> X1           0.369562  0.366782  0.366116 0.078220  0.221902  0.519001
#> X2          -0.641793 -0.642224 -0.649456 0.230732 -1.069026 -0.160841
rsfm_mod$restricted$summary_fixed
#>                  mean    median      mode       sd     lower     upper
#> (Intercept) -0.438975 -0.438133 -0.441816 0.075317 -0.573785 -0.280953
#> X1           0.374667  0.372933  0.375549 0.077295  0.234508  0.527128
#> X2          -0.691750 -0.689692 -0.687067 0.084866 -0.865797 -0.539728
SVIF(weibull_mod$unrestricted, rsfm_mod$unrestricted)
#>     parameter      VIF
#> 1 (Intercept) 0.964628
#> 2          X1 1.056159
#> 3          X2 6.648073
SVIF(weibull_mod$unrestricted, rsfm_mod$restricted)
#>     parameter       VIF
#> 1 (Intercept) 0.9336327
#> 2          X1 1.0313273
#> 3          X2 0.8993890