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Objects, summaries and plotting

GLBFP objects are ordinary S3 objects.

Object type Class Main helpers
Pointwise estimator "glbfp_fit" print(), summary(), predict()
Grid estimator "glbfp_grid" print(), summary(), predict(), plot(), as.data.frame() 
library(GLBFP)

x <- matrix(rnorm(200), ncol = 1)
b <- compute_bi_optim(x, m = 1)

point_fit <- glbfp(0, x, b = b, m = 1)
class(point_fit)
#> [1] "glbfp_fit" "GLBFP"
summary(point_fit)
#> Method: GLBFP 
#> Dimension: 1 
#> Point: 0 
#> Estimation: 0.4829175 
#> Standard error: 0.08419406 
#> 95% CI: 0.454717631827197, 0.511117292426919 
#> Bandwidths (b): 0.171212730111743 
#> Shifts (m): 1 
#> Relative grid coordinate (u): 0.53632342188152 
#> Visited cells: 2 
#> Prefix nodes: 2
predict(point_fit)
#> [1] 0.4829175

Grid fits support summary(), predict(), plot() and as.data.frame().

grid_fit <- glbfp_estimate(x, b = b, m = 1, grid_size = 60)

class(grid_fit)
#> [1] "glbfp_grid"     "GLBFP_estimate"
summary(grid_fit)
#> Method: GLBFP 
#> Dimension: 1 
#> Grid points: 60 
#> Grid type: rectangular 
#> Grid dimensions: 60 
#> Bandwidths (b): 0.171212730111743 
#> Shifts (m): 1 
#> Density range: 0.00255212027556414 to 0.49645841138404 
#> Density quartiles: 0.0627489310286446, 0.137498703743326, 0.301536680584891 
#> Density median: 0.1374987 
#> Density mean: 0.1821547 
#> Zero densities: 0 
#> Standard error median: 0.04981216 
#> Median visited cells: 2 
#> Median prefix nodes: 2

grid_df <- as.data.frame(grid_fit)
head(grid_df)
#>          V1    density         sd   IC_lower   IC_upper visited prefix_nodes
#> 1 -2.574410 0.13141546 0.09795130 0.09860780 0.16422312       1            2
#> 2 -2.482236 0.08424967 0.04773746 0.06826056 0.10023879       1            2
#> 3 -2.390062 0.03708388 0.02354223 0.02919868 0.04496908       1            2
#> 4 -2.297888 0.01680318 0.01976786 0.01018216 0.02342420       1            2
#> 5 -2.205714 0.09541283 0.03904341 0.08233569 0.10848997       1            2
#> 6 -2.113540 0.12361294 0.04990770 0.10689693 0.14032895       2            2

Prediction from a grid fit uses nearest-grid lookup. This is intended as a lightweight helper for exploratory workflows.

new_points <- matrix(c(-1, 0, 1), ncol = 1)
predict(grid_fit, newdata = new_points)
#> [1] 0.1574531 0.4840192 0.1908743

Plot methods return ggplot objects for one-dimensional grids and for two-dimensional contour plots.

plot(grid_fit)

For two-dimensional grids, contour = TRUE gives a static ggplot object. The default two-dimensional display uses plotly.

x2 <- cbind(rnorm(120), rnorm(120))
grid_2d <- glbfp_estimate(x2, b = c(0.8, 0.8), m = c(1, 1), grid_size = 12)

plot(grid_2d, contour = TRUE)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.