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Generate fitted values from a estimated GAM

Source: R/fitted_values.R
fitted_values.Rd

Generate fitted values from a estimated GAM

Usage

fitted_values(object, ...)

# S3 method for gam
fitted_values(
  object,
  data = NULL,
  scale = c("response", "link", "linear predictor"),
  ci_level = 0.95,
  ...
)

# S3 method for gamm
fitted_values(object, ...)

# S3 method for scam
fitted_values(object, ...)

Arguments

object

a fitted model. Currently only models fitted by mgcv::gam() and mgcv::bam() are supported.

...

arguments passed to mgcv::predict.gam(). Note that type, newdata, and se.fit are already used and passed on to mgcv::predict.gam().

data

optional data frame of covariate values for which fitted values are to be returned.

scale

character; what scale should the fitted values be returned on? "linear predictor" is a synonym for "link" if you prefer that terminology.

ci_level

numeric; a value between 0 and 1 indicating the coverage of the credible interval.

Value

A tibble (data frame) whose first m columns contain either the data used to fit the model (if data was NULL), or the variables supplied to data. Four further columns are added:

  • fitted: the fitted values on the specified scale,

  • se: the standard error of the fitted values (always on the link scale),

  • lower, upper: the limits of the credible interval on the fitted values, on the specified scale.

Models fitted with certain families will include additional variables

  • mgcv::ocat() models: when scale = "repsonse", the returned object will contain a row column and a category column, which indicate to which row of the data each row of the returned object belongs. Additionally, there will be nrow(data) * n_categories rows in the returned object; each row is the predicted probability for a single category of the response.

Note

For most families, regardless of the scale on which the fitted values are returned, the se component of the returned object is on the link (linear predictor) scale, not the response scale. An exception is the mgcv::ocat() family, for which the se is on the response scale if scale = "response".

Examples

load_mgcv()
# \dontshow{
op <- options(cli.unicode = FALSE, pillar.sigfig = 6)
# }
sim_df <- data_sim("eg1", n = 400, dist = "normal", scale = 2, seed = 2)
m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = sim_df, method = "REML")
fv <- fitted_values(m)
fv
#> # A tibble: 400 x 9
#>     .row       x0        x1         x2       x3  .fitted      .se .lower_ci
#>    <int>    <dbl>     <dbl>      <dbl>    <dbl>    <dbl>    <dbl>     <dbl>
#>  1     1 0.184882 0.617142  0.415244   0.132410  8.73875 0.354677   8.04360
#>  2     2 0.702374 0.569064  0.531439   0.365331  7.62581 0.337779   6.96378
#>  3     3 0.573326 0.153970  0.00324621 0.454532  3.12106 0.591862   1.96103
#>  4     4 0.168052 0.0348332 0.252100   0.537114 11.1124  0.402378  10.3237 
#>  5     5 0.943839 0.997953  0.155229   0.185495 14.0533  0.452947  13.1655 
#>  6     6 0.943475 0.835574  0.878840   0.449276  6.13080 0.364521   5.41635
#>  7     7 0.129159 0.586562  0.203511   0.256527 12.4838  0.355808  11.7864 
#>  8     8 0.833449 0.339117  0.583528   0.618458  6.25215 0.344700   5.57655
#>  9     9 0.468019 0.166883  0.804473   0.880744  4.21463 0.372003   3.48552
#> 10    10 0.549984 0.807410  0.264717   0.317747 15.5283  0.369999  14.8031 
#> # i 390 more rows
#> # i 1 more variable: .upper_ci <dbl>
# \dontshow{
options(op)
# }