Exploratory linear models

Load packages, read data and source custom scripts

rm(list = ls())
library(dplyr)

#> 
#> Attaching package: 'dplyr'

#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag

#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

library(mgcv)

#> Loading required package: nlme

#> 
#> Attaching package: 'nlme'

#> The following object is masked from 'package:dplyr':
#> 
#>     collapse

#> This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.

library(purrr)

path_proj <- day2day::git_path()
path_data <- file.path(path_proj, "data")
path_processed <- file.path(path_data, "processed")
path_modelled <- file.path(path_data, "modelled")

land_cover <- fst::read_fst(file.path(path_processed, "uk_1km_df_model_maskout.fst"))
land_cover <- sample_n(land_cover, 5000)

Exploratory model of land classes with covariates

make_form <- function(land_type, term) {
    paste0("cbind(", land_type, ", count_no_urban - ", land_type, ") ~ s(", term, ")")
}

data_model <- as_tibble(expand.grid(
    land_type = tidyselect::vars_select(names(land_cover), matches("^count_[1-5]")),
    term = c("elev", "slope_nb8", "gdd", "max_tasmax,min_tasmin", "smd,sms"),
    stringAsFactors = FALSE))

data_model <- data_model %>%
    mutate(
        formula = map(make_form(land_type, term), as.formula),
        model = map(formula, safely(~ gam(., binomial(), land_cover)))
    )

save(data_model, file = file.path(path_modelled, "explo-binoms-uni.rds"))

Time to execute the task

Only useful when executed with Rscript.

proc.time()

#>    user  system elapsed 
#>  14.811   0.156  14.986