Autoplot Method for fastsae Objects

Creates diagnostic and comparison plots for Small Area Estimation (SAE) models fitted with fastsae. This extends the generic autoplot from ggplot2.

Usage

# S3 method for class 'fastsae'
autoplot(
  object,
  type = c("comparison", "ribbon", "mse", "estimates", "scatter", "rse", "map"),
  ...
)

# S3 method for class 'list'
autoplot(object, type = c("comparison", "mse", "scatter", "map"), ...)

Arguments

object

An object of class fastsae, or a (named) list of fastsae objects.

type

Type of plot to create.

• For a single fastsae object: "comparison", "ribbon", "mse", "estimates", "scatter", "rse", or "map".

• For a list of fastsae objects: "comparison", "mse", "scatter", or "map".

...

Additional arguments passed to internal plotting helpers (e.g. title) or to ggplot2 layers.

Value

A ggplot object.

Examples

library(fastsae)

# Single model plot
fit_fh <- eblup_fh(y ~ x1 + x2 + x3, data = mys, vardir = "vardir")
#> 
#> ── Fast Small Area Estimation (fastsae) ────────────────────────────────────────
#> Call:
#> eblup_fh(formula = y ~ x1 + x2 + x3, vardir = "vardir", data = mys)
#> 
#> ✔ Convergence: Yes (in 6 iterations)
#> Model: Fay-Herriot (Area-level)
#> Method: eblup
#> Random effect variance (sigma2_u): 2.608103 
#> 
#> Fixed Effects Coefficients:
#>                   beta  std.error     zvalue pvalue
#> (Intercept)  3.1077510  0.7697687  4.0372527 0.0001
#> x1          -0.0019323  0.0098886 -0.1954019 0.8451
#> x2           0.0555184  0.0614129  0.9040187 0.3660
#> x3           0.0335344  0.0580013  0.5781663 0.5632
#> 
#> EBLUP Estimates (First 6 domains):
#>   domain        y    eblup    vardir random_effect       mse       rse
#> 1      1 8.359527 7.612738 0.6618838    2.94266461 0.5616180  9.844182
#> 2      2 7.599650 6.782316 0.8374691    2.54539588 0.6784873 12.144869
#> 3      3 5.514137 5.187060 0.8822257    0.96692722 0.7190378 16.347620
#> 4      4 3.869326 4.201545 0.6581716   -1.31646412 0.5619314 17.841554
#> 5      5 6.305063 6.323679 1.2788021   -0.03796677 0.9370273 15.307573
#> 6      6 3.926807 4.048590 0.3878004   -0.81904048 0.3548310 14.713194
#> ... and 36 more rows.
#> 
autoplot(fit_fh, type = "estimates")

autoplot(fit_fh, type = "rse")
#> Warning: Removed 10 rows containing missing values or values outside the scale range
#> (`geom_point()`).


# Compare two models
fit_sfh <- eblup_sfh(y ~ x1 + x2 + x3, data = mys, vardir = "vardir", W = mys_proxmat)
#> 
#> ── Fast Small Area Estimation (fastsae) ────────────────────────────────────────
#> Call:
#> eblup_sfh(formula = y ~ x1 + x2 + x3, vardir = "vardir", data = mys, W =
#> mys_proxmat)
#> 
#> ✔ Convergence: Yes (in 8 iterations)
#> Model: Spatial Fay-Herriot (Area-level SAR)
#> Method: REML
#> Random effect variance (sigma2_u): 1.493692 
#> Spatial autocorrelation (rho): -1 
#> 
#> Fixed Effects Coefficients:
#>                    beta   std.error      zvalue pvalue
#> (Intercept)  3.03659649  0.62669962  4.84537788 0.0000
#> x1          -0.00085935  0.00824230 -0.10426151 0.9170
#> x2           0.04983809  0.05273618  0.94504545 0.3446
#> x3           0.03929798  0.05006522  0.78493574 0.4325
#> 
#> EBLUP Estimates (First 6 domains):
#>   domain        y    vardir    eblup random_effect       mse       rse
#> 1      1 8.359527 0.6618838 7.282308   2.605985416 0.4797992  9.511756
#> 2      2 7.599650 0.8374691 6.423856   2.212974942 0.6156692 12.214562
#> 3      3 5.514137 0.8822257 5.023421   0.892393688 0.5519476 14.789360
#> 4      4 3.869326 0.6581716 4.324716  -1.127251956 0.4811841 16.039766
#> 5      5 6.305063 1.2788021 6.344991  -0.002629647 0.7315241 13.479795
#> 6      6 3.926807 0.3878004 4.087497  -0.704885769 0.3403445 14.272560
#> ... and 36 more rows.
#> 
autoplot(list("Fay-Herriot" = fit_fh, "Spatial FH" = fit_sfh), type = "comparison")