Predict from a locally-weighted PLS model

For every row of new_data, a weighted PLS model is fitted around that sample and used to predict it.

Usage

# S3 method for class 'lwpls_fit'
predict(object, new_data, type = NULL, num_comp = NULL, ...)

Arguments

object

A lwpls_fit object created by lwpls_fit().

new_data

A data frame or matrix of new predictors.

type

A single character string, or NULL. One of:

• "numeric" for numeric predictions (regression; the default).

• "class" for hard class predictions (classification; the default).

• "prob" for class probabilities (classification).

• "raw" for an array with the predictions of the local models with 1, ..., num_comp components (see Value).

num_comp

The number of PLS components to use. Defaults to the value used in lwpls_fit(). Since the model is lazy, any value can be used; values larger than the maximum possible number of components give the same predictions as that maximum.

...

Not used, but required for extensibility.

Value

A tibble of predictions with one row per row of new_data, following the tidymodels conventions: .pred (or .pred_{outcome} for multiple outcomes), .pred_class, or .pred_{level} columns.

For type = "raw", a numeric array of dimension nrow(new_data) x q x num_comp, where q is the number of outcomes (or of classes, for the predicted class indicators), on the original outcome scale.

Rows of new_data with missing values get missing predictions.

See also

lwpls_fit(), multi_predict._lwpls_fit()

Examples

train <- mtcars[-(1:5), ]
test <- mtcars[1:5, ]

fit <- lwpls_fit(mpg ~ ., data = train, num_comp = 3, localization = 0.5)
predict(fit, test)
#> # A tibble: 5 × 1
#>   .pred
#>   <dbl>
#> 1  21.1
#> 2  20.7
#> 3  27.0
#> 4  18.8
#> 5  17.4
predict(fit, test, num_comp = 1)
#> # A tibble: 5 × 1
#>   .pred
#>   <dbl>
#> 1  21.7
#> 2  21.4
#> 3  27.6
#> 4  18.7
#> 5  16.7
predict(fit, test, type = "raw")
#> , , 1
#> 
#>           mpg
#> [1,] 21.66757
#> [2,] 21.37123
#> [3,] 27.64895
#> [4,] 18.73608
#> [5,] 16.65921
#> 
#> , , 2
#> 
#>           mpg
#> [1,] 21.33833
#> [2,] 21.13447
#> [3,] 27.35273
#> [4,] 18.62587
#> [5,] 17.52832
#> 
#> , , 3
#> 
#>           mpg
#> [1,] 21.14190
#> [2,] 20.70355
#> [3,] 27.03485
#> [4,] 18.75206
#> [5,] 17.44782
#>