Add residuals from a GAM to a data frame

Usage

# S3 method for gam
add_residuals(data, model, value = ".residual", type = "deviance", ...)

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 residuals to return. See mgcv::residuals.gam() for options.
...: additional arguments passed to mgcv::residuals.gam().

Value

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

Examples

load_mgcv()
# \dontshow{
op <- options(pillar.sigfig = 3, cli.unicode = FALSE)
# }
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_residuals(df, m)
#> # A tibble: 400 x 6
#>          y     x0     x1     x2    x3 .residual
#>      <dbl>  <dbl>  <dbl>  <dbl> <dbl>     <dbl>
#>  1  3.34   0.266  0.659  0.859  0.367   -2.56  
#>  2 -0.0758 0.372  0.185  0.0344 0.741   -3.22  
#>  3 10.7    0.573  0.954  0.971  0.934    2.40  
#>  4  8.73   0.908  0.898  0.745  0.673    0.0785
#>  5 15.0    0.202  0.944  0.273  0.701   -0.693 
#>  6  7.67   0.898  0.724  0.677  0.848   -0.714 
#>  7  7.58   0.945  0.370  0.348  0.706   -0.259 
#>  8  8.51   0.661  0.781  0.947  0.859    1.78  
#>  9 10.6    0.629  0.0111 0.339  0.446    1.50  
#> 10  3.72   0.0618 0.940  0.0317 0.677   -3.32  
#> # i 390 more rows
# \dontshow{
options(op)
# }