inlabru 时空分析

library(INLA) # 用于之后的建模

library(MASS) # 用于真实的多元正态分布模拟

library(tidyverse)

library(inlabru)

library(fdmr)

library(sf)

library(inlatools) 

covid19_data <- fdmr::load_tutorial_data(dataset = "priors", filename = "covid19_dataBris.rds")

covid19_sf <- covid19_data |> st_as_sf(coords=c("LONG", "LAT"))

coordinates <- st_coordinates(covid19_sf)

locs <- unique(coordinates)

# 计算初始参数

initial_range <- diff(range(locs[, 1])) / 3

max_edge <- initial_range / 2

# 2. 构建改进的网格，mesh从地图构建，此处不对。

mesh <- fm_mesh_2d(

  loc = locs,

  max.edge = c(1, 2) * max_edge,

  offset = c(initial_range, initial_range),

  cutoff = max_edge / 5

)

# 3. 定义SPDE模型

spde <- inla.spde2.pcmatern(

  mesh = mesh,

  prior.range = c(initial_range, 0.5),

  prior.sigma = c(1, 0.01)

)

# 4. 设置时间趋势先验，

rw1_prior <- list(theta = list(prior = "pc.prec", param = c(1, 0.01)))

# week需要从1开始

model1 <- bru(cases ~ Intercept(1)+ Population + prevalence +

    site(st_coordinates(geometry),model=spde)+  #site(st_coordinates)

    trend(main=week,model='rw1',hyper = list(theta = rw1_prior)),

    formula = cases ~ Intercept + Population + prevalence + site + trend, #可省略

      data = covid19_sf,

      family = "poisson")

summary(model1)