Package 'stgam'

Extracts varying coefficient estimates (for SVC, TVC and STVC models).

Description

Extracts varying coefficient estimates (for SVC, TVC and STVC models).

Usage

calculate_vcs(input_data, model, terms)

Arguments

input_data	the data used to create the GAM model in data.frame, tibble or sf format
model	a GAM model with smooths created using the mgcv package
terms	a vector of names starting with "Intercept" plus the names of the covariates used in the GAM model (these are the names of the variables in data )

Value

A data.frame of the input data and the coefficient and standard error estimates for each covariate.

Examples

library(dplyr)
library(mgcv)
# SVC
data(productivity)
input_data = productivity |> dplyr::filter(year == "1970") |> mutate(Intercept = 1)
gam.svc.mod = gam(privC ~ 0 + Intercept +
                  s(X, Y, bs = 'gp', by = Intercept) +
                  unemp + s(X, Y, bs = "gp", by = unemp) +
                  pubC + s(X, Y, bs = "gp", by = pubC),
                  data = input_data)
terms = c("Intercept", "unemp", "pubC")
svcs = calculate_vcs(input_data, gam.svc.mod, terms)
head(svcs)

---

Undertake undertake coefficient averaging using Bayesian Model Avergaing (BMA), weighting different models by their probabilities

Description

Undertake undertake coefficient averaging using Bayesian Model Avergaing (BMA), weighting different models by their probabilities

Usage

do_bma(model_table, terms, thresh = 0.1, relative = FALSE, input_data)

Arguments

model_table	a table of competing models generated by the gam_model_probs() function
terms	a vector of names starting with "Intercept" plus the names of the covariates used in the GAM model (these are the names of the variables in input_data )
thresh	a probability threshold value above which to combine competing models
relative	logical to indicate whether the probabilities in data are relative (Pr(M)) or absolute (Pr(M|D))
input_data	the input data with a named Intercept term, in data.frame, tibble or sf format

Value

A tibble of the input data plus the probability weighted averaged coefficient estimates from multiple models, all starting with ⁠b_<covariate name>⁠

Examples

library(cols4all)
library(dplyr)
library(sf)
library(glue)
library(purrr)
library(mgcv)
library(sf)
library(ggplot2)
# data
data(productivity)
input_data = productivity |> filter(year == "1970") |> mutate(Intercept = 1)
# create and evaluate multiple models
svc_res_gam = evaluate_models(input_data, STVC = FALSE)
# determine their probabilities
mod_comp_svc <- gam_model_probs(svc_res_gam)
# combine the model coefficients
svc_bma <- do_bma(model_table = mod_comp_svc,
                  terms = c("Intercept", "unemp", "pubC"),
                  thresh = 0.1,
                  relative = FALSE,
                  input_data = input_data)
head(svc_bma)
# join back to spatial layer
data(us_data)
svc_bma_sf <-
  us_data |>
  select(GEOID) |>
  left_join(svc_bma)
# and map
tit =expression(paste(""*beta[`Public Capital`]*" "))
ggplot(data = svc_bma_sf, aes(fill=b_pubC)) +
  geom_sf() +
  scale_fill_continuous_c4a_div(palette="brewer.yl_or_rd",name=tit) +
  coord_sf() +
  theme_linedraw()

---

Creates and evaluates multiple varying coefficient GAM GP smooth models (SVC or STVC)

Description

Creates and evaluates multiple varying coefficient GAM GP smooth models (SVC or STVC)

Usage

evaluate_models(
  input_data,
  target_var = "privC",
  covariates = c("unemp", "pubC"),
  coords_x = "X",
  coords_y = "Y",
  STVC = FALSE,
  time_var = NULL,
  ncores = 2
)

Arguments

input_data	a data.frame, tibble sf containing the target variables, covariates and coordinate variables
target_var	the name of the target variable in data
covariates	the name of the covariates (predictor variables) in data
coords_x	the name of the X, Easting or Longitude variable in data
coords_y	the name of the Y, Northing or Latitude variable in data
STVC	a logical operator to indicate whether the models Space-Time (TRUE) or just Space (FALSE)
time_var	the name of the time variable if undertaking STVC model evaluations
ncores	the number of cores to use in parallelised approaches (default is 2 to overcome CRAN package checks). This can be determined for your computer by running parallel::detectCores()-1. Parallel approaches are only undertaken if the number of models to evaluate is greater than 30.

Value

A data table in data.frame format of all possible model combinations with each covariate specified in all possible ways, with the BIC of the model and the model formula.

Examples

library(dplyr)
library(glue)
library(purrr)
library(doParallel)
library(mgcv)
data("productivity")
input_data = productivity |> filter(year == "1970")
svc_res_gam =
  evaluate_models(input_data = input_data,
                  target_var = "privC",
                  covariates = c("unemp", "pubC"),
                  coords_x = "X",
                  coords_y = "Y",
                  STVC = FALSE)
head(svc_res_gam)

---

Calculates the ⁠model probabilities of the different GAM models generated by ⁠evaluate_models'

Description

Calculates the ⁠model probabilities of the different GAM models generated by ⁠evaluate_models'

Usage

gam_model_probs(res_tab, n = 10)

Arguments

res_tab	a table generated by evaluate_models
n	the number of models to retain and generate probabilities for

Value

A ranked data table in tibble format of the top n models, their form, BIC and model or (Pr(M|D)) or relative (Pr(M)) probability value. Model probability indicates the probability of the each model being the correct model and the relative probabilities provide a measure of the doubt about the differences in model specification, when compared to the best or highest ranked model. The relative probabilities are needed when large BIC values generate near zero probability values.

Examples

library(dplyr)
library(purrr)
library(glue)
library(mgcv)
data(productivity)
input_data = productivity |> filter(year == "1970")
svc_res_gam = evaluate_models(input_data, STVC = FALSE)
mod_comp_svc <- gam_model_probs(svc_res_gam, n = 10)
# print out the terms and probabilities
mod_comp_svc|> select(-f)

---

Plots a 1-Dimensional GAM smooth

Description

Plots a 1-Dimensional GAM smooth

Usage

plot_1d_smooth(mod, ncol = NULL, nrow = NULL, fills = "lightblue")

Arguments

mod	a GAM model with smooths created using the mgcv package
ncol	the number of columns in the compound plot
nrow	the number of rows in the compound plot
fills	the fill colours (single or vector)

Value

A compound plot of the GAM 1-dimensional smooths (rendered using cowplot::plot_grid()).

Examples

library(mgcv)
library(ggplot2)
library(dplyr)
library(cowplot)
# 1. from the `mgcv` `gam` function help
set.seed(2) ## simulate some data...
dat <- gamSim(1,n=400,dist="normal",scale=2)
b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
plot_1d_smooth(b, ncol = 2, fills = c("lightblue", "lightblue3"))
dev.off()
# 2. using a TVC
data(productivity)
data = productivity |> mutate(Intercept = 1)
gam.tvc.mod = gam(privC ~ 0 + Intercept +
                  s(year, bs = 'gp', by = Intercept) +
                  unemp + s(year, bs = "gp", by = unemp) +
                  pubC + s(year, bs = "gp", by = pubC),
                  data = data)
plot_1d_smooth(gam.tvc.mod, fills = "lightblue")

---

Plots a 2-Dimensional GAM smooth

Description

Plots a 2-Dimensional GAM smooth

Usage

plot_2d_smooth(mod, filled = FALSE, outline = NULL, ncol = NULL, nrow = NULL)

Arguments

mod	a GAM model with smooths created using the mgcv package
filled	logical value to indicate whether a filled plot should be created (TRUE) or not (FALSE)
outline	the name of an sf object to be plotted (NULL is the default)
ncol	the number of columns for the compound plot
nrow	the number of rows for the compound plot

Value

A compound plot of the 2-dimensional smooths (rendered using cowplot::plot_grid).

Examples

library(mgcv)
library(ggplot2)
library(dplyr)
library(metR)
library(cowplot)
set.seed(2) ## simulate some data...
dat <- gamSim(1,n=400,dist="normal",scale=2)
# use x1 and x2 as the coordinates
b <- gam(y~s(x0, x1, bs = 'gp', by = x2),data=dat)
plot_2d_smooth(b, filled = TRUE)

---

US States Economic Productivity Data (1970-1985)

Description

A dataset of annual economic productivity data for the 48 contiguous US states (with Washington DC merged into Maryland), from 1970 to 1985 (17 years) in long format. The data productivity data table was extracted from the plm package.

Usage

productivity

Format

A tibble with 816 rows and 14 columns.

state

The name of the state

GEOID

The state code

year

The year of observation

region

The region

pubC

Public capital which is composed of highways and streets (hwy) water and sewer facilities (water) and other public buildings and structures (util)

hwy

Highway and streets assets

water

Water utility assets

util

Other public buildings and structures

privC

Private captial stock

gsp

Gross state product

emp

Labour input measured by the employment in non-agricultural payrolls

unemp

State unemployment rate capture elements of the business cycle

X

Easting in metres from USA Contiguous Equidistant Conic projection (ESRI:102005)

Y

Northing in metres from USA Contiguous Equidistant Conic projection (ESRI:102005)

Source

Croissant, Yves, Giovanni Millo, and Kevin Tappe. 2022. Plm: Linear Models for Panel Data

Examples

data(productivity)

---

US States boundaries

Description

A dataset of of the boundaries of 48 contiguous US states (with Washington DC merged into Maryland), extracted from the spData package.

Usage

us_data

Format

A sf polygon dataset with 48 rows and 6 fields.

GEOID

The state code

NAME

The name of the state

REGION

The US region

AREA

The area in km^2

total_pop_10

Population in 2010

total_pop_15

Population in 2015

Source

Bivand, Roger, Jakub Nowosad, and Robin Lovelace. 2019. spData: Datasets for Spatial Analysis. R package

Examples

data(us_data)

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