Add partial residuals

Add partial residuals

Usage

add_partial_residuals(data, model, ...)

# S3 method for class 'gam'
add_partial_residuals(data, model, select = NULL, partial_match = FALSE, ...)

# S3 method for class 'gamm4'
add_partial_residuals(data, model, ...)

# S3 method for class 'scam'
add_partial_residuals(data, model, ...)

Arguments

data

a data frame containing values for the variables used to fit the model, in fitting order. Supply either the retained fitting rows or, for na.exclude, the original fitting rows after any subset. This does not compute residuals for new or reordered observations.

model

a fitted model for which a stats::residuals() method is available. S3 method dispatch is performed on the model argument.

...

arguments passed to other methods.

select

character, logical, or numeric; which smooths to plot. If NULL, the default, then all model smooths are drawn. Numeric select indexes the smooths in the order they are specified in the formula and stored in object. Character select matches the labels for smooths as shown for example in the output from summary(object). Logical select operates as per numeric select in the order that smooths are stored.

partial_match

logical; should smooths be selected by partial matches with select? If TRUE, select can only be a single string to match against.

Details

Partial residuals require working residuals and working weights from the fitted model. Families that do not provide these, including the location-scale families in mgcv, are not supported, even for terms in the location predictor.

See also

gamm4_support

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 partial residuals
add_partial_residuals(df, m)
#> # A tibble: 400 x 9
#>          y     x0     x1     x2    x3 `s(x0)` `s(x1)` `s(x2)` `s(x3)`
#>      <dbl>  <dbl>  <dbl>  <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
#>  1  3.34   0.266  0.659  0.859  0.367  -2.38   -2.00   -5.36  -2.52  
#>  2 -0.0758 0.372  0.185  0.0344 0.741  -2.79   -5.15   -6.45  -3.29  
#>  3 10.7    0.573  0.954  0.971  0.934   2.99    5.75   -1.07   2.28  
#>  4  8.73   0.908  0.898  0.745  0.673  -0.734   2.84   -1.11   0.0287
#>  5 15.0    0.202  0.944  0.273  0.701  -0.752   2.54    3.94  -0.750 
#>  6  7.67   0.898  0.724  0.677  0.848  -1.46    0.432  -0.567 -0.812 
#>  7  7.58   0.945  0.370  0.348  0.706  -1.33   -1.57    2.08  -0.318 
#>  8  8.51   0.661  0.781  0.947  0.859   2.21    3.44   -1.42   1.68  
#>  9 10.6    0.629  0.0111 0.339  0.446   2.01   -0.445   4.13   1.51  
#> 10  3.72   0.0618 0.940  0.0317 0.677  -4.02   -0.123  -6.67  -3.37  
#> # i 390 more rows

## add partial residuals for selected smooths
add_partial_residuals(df, m, select = "s(x0)")
#> # A tibble: 400 x 6
#>          y     x0     x1     x2    x3 `s(x0)`
#>      <dbl>  <dbl>  <dbl>  <dbl> <dbl>   <dbl>
#>  1  3.34   0.266  0.659  0.859  0.367  -2.38 
#>  2 -0.0758 0.372  0.185  0.0344 0.741  -2.79 
#>  3 10.7    0.573  0.954  0.971  0.934   2.99 
#>  4  8.73   0.908  0.898  0.745  0.673  -0.734
#>  5 15.0    0.202  0.944  0.273  0.701  -0.752
#>  6  7.67   0.898  0.724  0.677  0.848  -1.46 
#>  7  7.58   0.945  0.370  0.348  0.706  -1.33 
#>  8  8.51   0.661  0.781  0.947  0.859   2.21 
#>  9 10.6    0.629  0.0111 0.339  0.446   2.01 
#> 10  3.72   0.0618 0.940  0.0317 0.677  -4.02 
#> # i 390 more rows