Add fitted values from a GAM to a data frame

Usage

# S3 method for class 'gam'
add_fitted(
  data,
  model,
  value = ".fitted",
  type = "response",
  ...,
  envir = NULL,
  marginalise = NULL,
  integration = c("quadrature", "monte_carlo"),
  n_quad = 40L,
  n_integrate = 5000L,
  seed = NULL
)

Arguments

data: a data frame containing values for the variables used to fit the model. Passed to stats::predict() as newdata.
model: a fitted model for which a stats::predict() method is available. S3 method dispatch is performed on the model argument.
value: character; the name of the variable in which model predictions will be stored.
type: character; the type of predictions to return. See mgcv::predict.gam() for options.
...: additional arguments passed to mgcv::predict.gam().
envir: environment or NULL; functions and constants needed to evaluate model expressions in prediction data. With NULL, use the model's formula environment. Supply raw covariates in data.
marginalise: NULL retains the usual predictions. TRUE selects all supported re and fs terms; character vectors select exact smooth labels. Fitted predictions integrate response means over new zero-mean Gaussian effects. Response samplers draw new effects before sampling responses. Smoothing parameters and dispersion remain fixed. Only ordinary gam() and bam() fits with a scalar response mean are supported.
integration: Integration method when marginalise is supplied: "quadrature" (the default) or "monte_carlo". Both integrate over the Gaussian distribution of the selected effects at each row.
n_quad: Initial Gauss-Hermite point count, between 2 and 1000. Successive doubled orders, up to four times this count, and an interlaced rule with one fewer point must agree within 1e-8 + 1e-6 * abs(value) for means and derivatives. Where the random-effect variance exceeds one, an independent adaptive rule also checks agreement. Failure to agree reports an error. Identity, log and probit links use their closed-form Gaussian integrals; their metadata records zero quadrature points and zero integration error.
n_integrate: Number of Monte Carlo integration draws, at least two. These are shared across rows and posterior coefficient draws. This count is separate from n_draws. Ignored for quadrature.
seed: Optional integer seed. An explicit seed preserves the caller's random-number state; NULL advances the current state.

Value

A data frame (tibble) formed from data and predictions from model.

Examples

load_mgcv()
df <- data_sim("eg1", seed = 1)
df <- df[, c("y", "x0", "x1", "x2", "x3")]
m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = df, method = "REML")

# add fitted values to our data
add_fitted(df, m)
#> # A tibble: 400 x 6
#>          y     x0     x1     x2    x3 .fitted
#>      <dbl>  <dbl>  <dbl>  <dbl> <dbl>   <dbl>
#>  1  3.34   0.266  0.659  0.859  0.367    5.90
#>  2 -0.0758 0.372  0.185  0.0344 0.741    3.15
#>  3 10.7    0.573  0.954  0.971  0.934    8.28
#>  4  8.73   0.908  0.898  0.745  0.673    8.65
#>  5 15.0    0.202  0.944  0.273  0.701   15.7 
#>  6  7.67   0.898  0.724  0.677  0.848    8.38
#>  7  7.58   0.945  0.370  0.348  0.706    7.84
#>  8  8.51   0.661  0.781  0.947  0.859    6.74
#>  9 10.6    0.629  0.0111 0.339  0.446    9.14
#> 10  3.72   0.0618 0.940  0.0317 0.677    7.04
#> # i 390 more rows

# with type = "terms" or "iterms"
add_fitted(df, m, type = "terms")
#> # A tibble: 400 x 10
#>          y     x0     x1     x2    x3 .constant `s(x0)` `s(x1)` `s(x2)` `s(x3)`
#>      <dbl>  <dbl>  <dbl>  <dbl> <dbl>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1  3.34   0.266  0.659  0.859  0.367      7.94  0.175    0.559  -2.81   0.0351
#>  2 -0.0758 0.372  0.185  0.0344 0.741      7.94  0.435   -1.92   -3.23  -0.0687
#>  3 10.7    0.573  0.954  0.971  0.934      7.94  0.593    3.35   -3.47  -0.122 
#>  4  8.73   0.908  0.898  0.745  0.673      7.94 -0.812    2.77   -1.19  -0.0498
#>  5 15.0    0.202  0.944  0.273  0.701      7.94 -0.0589   3.23    4.63  -0.0576
#>  6  7.67   0.898  0.724  0.677  0.848      7.94 -0.745    1.15    0.146 -0.0981
#>  7  7.58   0.945  0.370  0.348  0.706      7.94 -1.07    -1.31    2.34  -0.0589
#>  8  8.51   0.661  0.781  0.947  0.859      7.94  0.434    1.67   -3.20  -0.101 
#>  9 10.6    0.629  0.0111 0.339  0.446      7.94  0.512   -1.95    2.63   0.0132
#> 10  3.72   0.0618 0.940  0.0317 0.677      7.94 -0.695    3.20   -3.35  -0.0508
#> # i 390 more rows