Generates residuals and fitted values of a fitted gaimFit object.
Usage
# S3 method for class 'gaimFit'
augment(x, ...)Examples
library(dplyr)
library(tibble)
library(tidyr)
library(tsibble)
# Simulate data
n = 1005
set.seed(123)
sim_data <- tibble(x_lag_000 = runif(n)) |>
mutate(
# Add x_lags
x_lag = lag_matrix(x_lag_000, 5)) |>
unpack(x_lag, names_sep = "_") |>
mutate(
# Response variable
y = (0.9*x_lag_000 + 0.6*x_lag_001 + 0.45*x_lag_003)^3 + rnorm(n, sd = 0.1),
# Add an index to the data set
inddd = seq(1, n)) |>
drop_na() |>
select(inddd, y, starts_with("x_lag")) |>
# Make the data set a `tsibble`
as_tsibble(index = inddd)
# Predictors taken as index variables
index.vars <- colnames(sim_data)[3:7]
# Assign group indices for each predictor
index.ind = c(rep(1, 3), rep(2, 2))
# Predictors taken as non-linear variables not entering indices
s.vars = "x_lag_005"
# Model fitting
gaimModel <- model_gaim(data = sim_data,
yvar = "y",
index.vars = index.vars,
index.ind = index.ind,
s.vars = s.vars)
# Obtain residuals and fitted values
augment(gaimModel)
#> # A tibble: 1,000 × 3
#> Index .resid .fitted
#> <int> <dbl> <dbl>
#> 1 6 0.160 0.407
#> 2 7 -0.230 0.995
#> 3 8 0.381 3.24
#> 4 9 -0.162 1.41
#> 5 10 -0.0843 0.997
#> 6 11 0.403 3.18
#> 7 12 0.0542 1.80
#> 8 13 -0.0269 1.33
#> 9 14 0.249 2.24
#> 10 15 -0.0629 0.213
#> # ℹ 990 more rows