Builds predictive model based GLM.

The method provides main functionality on building GLM models with automatic variables transformation. The transformations are based on specified single variable responses for selected black-box model. See details in vignette("xspliner").

xspline(object, ...)

model_surrogate_xspliner(object, ...)

# S3 method for default
xspline(object, lhs = NULL, response = NULL,
  predictors = NULL, data = NULL, form = "additive", bare = NULL,
  env = parent.frame(), ...)

# S3 method for formula
xspline(object, model, data = NULL,
  consider = "specials", env = parent.frame(), ...)

# S3 method for explainer
xspline(object, env = parent.frame(), ...)

Arguments

object	Predictive model, formula or explainer (see DALEX) object.
...	Other arguments passed to xspline methods or build_xspliner.
lhs	Left-hand side of model formula. Can be transformed response.
response	Name of response variable of model.
predictors	Predictor values that should be used in final model.
data	Training data of model.
form	Can be 'additive' (default) or 'multiplicative'. Specifies formula form in final model.
bare	Variable names that mustn't be transformed in final model.
env	Environment in which optional variables passed into parameters are stored. variables transformation. See vignette("xspliner") for details.
model	When object is formula - predictive model. Basic model used for extracting predictors transformation.
consider	One of c("specials", "all"). If "specials", only components with xs or xf call are considered in transition.

Value

GLM object of class 'xspliner'.

Details

model_surrogate_xspliner is a wrapper of xspline method to assure consistency with https://github.com/ModelOriented/DrWhy tools

Examples

# preparing blackbox model
library(randomForest)
rf_iris <- randomForest(
  Petal.Width ~  Sepal.Length + Petal.Length + Species,
  data = iris)

# formula based xspliner
xs_iris <- xspline(
  Petal.Width ~ xs(Sepal.Length) + xs(Petal.Length) + xf(Species),
  model = rf_iris)
summary(xs_iris)
#> 
#> Call:
#> stats::glm(formula = Petal.Width ~ xs(Sepal.Length) + xs(Petal.Length) + 
#>     xf(Species), family = family, data = data)
#> 
#> Deviance Residuals: 
#>      Min        1Q    Median        3Q       Max  
#> -0.67489  -0.07877  -0.03048   0.09804   0.46780  
#> 
#> Coefficients:
#>                       Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)           -1.40810    0.30407  -4.631 8.03e-06 ***
#> xs(Sepal.Length)       0.39342    0.36819   1.069   0.2871    
#> xs(Petal.Length)       1.67365    0.35387   4.730 5.28e-06 ***
#> xf(Species)versicolor  0.08582    0.17800   0.482   0.6304    
#> xf(Species)virginica   0.41758    0.23705   1.762   0.0802 .  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> (Dispersion parameter for gaussian family taken to be 0.03102474)
#> 
#>     Null deviance: 86.5699  on 149  degrees of freedom
#> Residual deviance:  4.4986  on 145  degrees of freedom
#> AIC: -88.349
#> 
#> Number of Fisher Scoring iterations: 2
#> 
plot(xs_iris, "Sepal.Length")

# passing just the model
xs_iris <- xspline(rf_iris)
summary(xs_iris)
#> 
#> Call:
#> stats::glm(formula = Petal.Width ~ xs(Sepal.Length) + xs(Petal.Length) + 
#>     xf(Species), family = family, data = data)
#> 
#> Deviance Residuals: 
#>      Min        1Q    Median        3Q       Max  
#> -0.67489  -0.07877  -0.03048   0.09804   0.46780  
#> 
#> Coefficients:
#>                       Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)           -1.40810    0.30407  -4.631 8.03e-06 ***
#> xs(Sepal.Length)       0.39342    0.36819   1.069   0.2871    
#> xs(Petal.Length)       1.67365    0.35387   4.730 5.28e-06 ***
#> xf(Species)versicolor  0.08582    0.17800   0.482   0.6304    
#> xf(Species)virginica   0.41758    0.23705   1.762   0.0802 .  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> (Dispersion parameter for gaussian family taken to be 0.03102474)
#> 
#>     Null deviance: 86.5699  on 149  degrees of freedom
#> Residual deviance:  4.4986  on 145  degrees of freedom
#> AIC: -88.349
#> 
#> Number of Fisher Scoring iterations: 2
#> 
plot(xs_iris, "Sepal.Length")

# using DALEX
library(DALEX)
#> Welcome to DALEX (version: 0.4.8).
#> Find examples and detailed introduction at: https://pbiecek.github.io/PM_VEE/
xs_iris_explainer <- explain(rf_iris)
#> Preparation of a new explainer is initiated
#>   -> model label       :  randomForest  (  default  )
#>   -> data              :  150  rows  4  cols (extracted from the model)
#>   -> target variable   :  not specified! (  WARNING  )
#>   -> predict function  :  yhat.randomForest  will be used (  default  )
#>   -> predicted values  :  numerical, min =  0.1977761 , mean =  1.199116 , max =  2.143874  
#>   -> residual function :  difference between y and yhat (  default  )
#>   A new explainer has been created!  
xs_iris <- xspline(rf_iris)
summary(xs_iris)
#> 
#> Call:
#> stats::glm(formula = Petal.Width ~ xs(Sepal.Length) + xs(Petal.Length) + 
#>     xf(Species), family = family, data = data)
#> 
#> Deviance Residuals: 
#>      Min        1Q    Median        3Q       Max  
#> -0.67489  -0.07877  -0.03048   0.09804   0.46780  
#> 
#> Coefficients:
#>                       Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)           -1.40810    0.30407  -4.631 8.03e-06 ***
#> xs(Sepal.Length)       0.39342    0.36819   1.069   0.2871    
#> xs(Petal.Length)       1.67365    0.35387   4.730 5.28e-06 ***
#> xf(Species)versicolor  0.08582    0.17800   0.482   0.6304    
#> xf(Species)virginica   0.41758    0.23705   1.762   0.0802 .  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> (Dispersion parameter for gaussian family taken to be 0.03102474)
#> 
#>     Null deviance: 86.5699  on 149  degrees of freedom
#> Residual deviance:  4.4986  on 145  degrees of freedom
#> AIC: -88.349
#> 
#> Number of Fisher Scoring iterations: 2
#> 
plot(xs_iris, "Sepal.Length")