laggar
laggar.RmdThe laggar R package is designed to assist in data preparation for scalar-on-function regression within the mgcv package. Scalar-on-function regression can also be termed zone-of-influence modelling, or modelling over lags, and has a variety of applications. For more information on the statistical method, see the preprint of Miller et al 2025.
# install laggar if not already installed
# remotes::install_github("NERC-CEH/laggar")
library(laggar)
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
library(ggplot2)
library(mgcv)
#> Loading required package: nlme
#> This is mgcv 1.9-4. For overview type '?mgcv'.Points data
Simulate data
set.seed(100230)
boundary <- st_polygon(list(matrix(c(0,1000,1000,0,0,
0,0,1000,1000,0),ncol=2)))
adults <- st_sample(boundary, size = 300)
seedtraps <- data.frame(
x = rep(seq(50,950,100), 10),
y = rep(seq(50,950,100), each = 10)
) |>
st_as_sf(coords = c("x","y"), remove = FALSE)
ggplot() + geom_sf(data = boundary) + geom_sf(data = adults) +
geom_sf(data = seedtraps, colour = "red") +
theme_void()
Now simulate number of seeds by using number of trees within distance (ignoring boundary effects of polygon for this):
vshort_distance <- sum_within_dist(seedtraps, adults, boundary, 10)
short_distance <- sum_within_dist(seedtraps, adults, boundary, 25)
medium_distance <- sum_within_dist(seedtraps, adults, boundary, 75)
long_distance <- sum_within_dist(seedtraps, adults, boundary, 125)
vlong_distance <- sum_within_dist(seedtraps, adults, boundary, 200)
total_pred <- -0.5*vshort_distance[[1]] + 1*short_distance[[1]] +
0.2*medium_distance[[1]] +
0.05*long_distance[[1]] + 0.01*vlong_distance[[1]]
nseeds <- rpois(100, total_pred)Now do data prep
Establish sequences of distance of interest
dist_seq <- seq(10,200,10)
points_parea <- lg_points(seedtraps, adults,
dist_seq = dist_seq, bound_poly = boundary)
points_perha <- 1e4*points_parea$value_pareaAssemble into lagR object:
lgr <- lg_assembly(response = nseeds,
covariate = points_perha,
index_seq = dist_seq)Graphical checks:
lg_plot(lgr)
Model
modfit <- gam(response ~ te(index, by = covariate, bs = "tp", k = 10),
data = lgr,
family = "poisson", method = "REML")
summary(modfit)
#>
#> Family: poisson
#> Link function: log
#>
#> Formula:
#> response ~ te(index, by = covariate, bs = "tp", k = 10)
#>
#> Parametric coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.5613 0.4660 1.205 0.228
#>
#> Approximate significance of smooth terms:
#> edf Ref.df Chi.sq p-value
#> te(index):covariate 4.567 5.387 50.31 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> R-sq.(adj) = 0.289 Deviance explained = 31.3%
#> -REML = 186.56 Scale est. = 1 n = 100
plot(modfit)
Raster data
Simulate data
Load in a couple of extra packages for data simulation and plotting:
library(prioritizr)
library(tidyterra)
#>
#> Attaching package: 'tidyterra'
#> The following object is masked from 'package:stats':
#>
#> filter
library(terra)
#> terra 1.9.1We’ll use the sampling locations created above, for simplicity. But we’ll now add a raster layer over the area of interest:
# background rainfall data
r <- terra::rast(vect(boundary), nrow = 100, ncol = 100, vals = 1, crs = "EPSG:32630")
renv <- simulate_data(x = r, n = 1, scale = 0.8, intensity = 22, sd = 1)
st_crs(seedtraps) <- "EPSG:32630"
ggplot() +
geom_spatraster(data = renv) +
geom_point(data = seedtraps, aes(x, y), colour = "black") +
scale_fill_viridis_c() +
theme_void()
Simulate a monotonically decreasing effect of the raster layer with increasing distance from the measurement point:
val_distance <- values_within_dist_rast(seedtraps, renv, func = "mean",
dist_seq = c(50,100,150,200),
plot_doughnuts = FALSE)
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total_pred <- 1*val_distance$value[,1] +
0.3*val_distance$value[,2] +
0.05*val_distance$value[,3] +
0.01*val_distance$value[,3]
growth_rate <- rnorm(100, total_pred,0.25)Data prep
Establish sequences of distance of interest.
dist_seq <- seq(20,200,20)Extract the summary values over the sequence of distances:
rast_sum <- lg_rast(x = seedtraps,
y = renv,
func = "mean",
dist_seq = dist_seq, mindist = NULL, maxdist = NULL, incdist = NULL,
plot_doughnuts = TRUE, object_n = c(1, 10, 20), bg_layer = renv)
#> Maximum number of buffers for plotting is 9. Recycling colours, plot may look odd.
#> plotting object indices c(1, 101, 201, 301, 401, 501, 601, 701, 801, 901), c(10, 110, 210, 310, 410, 510, 610, 710, 810, 910), c(20, 120, 220, 320, 420, 520, 620, 720, 820, 920)
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lgr <- lg_assembly(response = growth_rate,
covariate = rast_sum$value_parea,
index_seq = dist_seq)Graphical checks:
lg_plot(lgr)
Model
summary(modfit)
#>
#> Family: gaussian
#> Link function: identity
#>
#> Formula:
#> response ~ te(index, by = covariate, bs = "tp", k = 10)
#>
#> Parametric coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2.0602 0.6196 3.325 0.00126 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Approximate significance of smooth terms:
#> edf Ref.df F p-value
#> te(index):covariate 5.416 6.106 374.9 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> R-sq.(adj) = 0.959 Deviance explained = 96.1%
#> -REML = 6.2732 Scale est. = 0.06581 n = 100
plot(modfit)