summary.autopls: Summary and print functions for autopls objects

In autopls: Partial Least Squares Regression with Backward Selection of Predictors

Description

Summary and print methods for autopls and slim objects.

Usage

  ## S3 method for class 'autopls'
summary(object, ...)
  ## S3 method for class 'slim'
summary(object, ...)
  ## S3 method for class 'autopls'
print(x, ...)
  ## S3 method for class 'slim'
print(x, ...)

Arguments

object	object of class autopls or slim respectively
x	object of class autopls or slim
...	Arguments to be passed to methods

Value

predictors	number of predictors used in the final model
lv	number of latent vectors used in the final model
rmse.cal	root mean squared errors in calibration
rmse.val	root mean squared errors in validation
r2.cal	R2 in calibration
r2.val	R2 in validation

Print returns a screen output and an unvisible object with the same content

Author(s)

Sebastian Schmidtlein

See Also

autopls, slim

Examples

  ## load predictor and response data to the current environment
  data (murnau.X)
  data (murnau.Y)
  
  ## call autopls with the standard options
  model <- autopls(murnau.Y ~ murnau.X)
  
  ## print and plot results
  print (model)

Example output

Loading required package: pls

Attaching package: 'pls'

The following object is masked from 'package:stats':

    loadings

autopls 1.3
1   Pred: 26  LV: 3   R2v: 0.74   RMSEv: 4.727  
2   Pred: 23  LV: 3   R2v: 0.742  RMSEv: 4.705  Criterion: A1
3   Pred: 20  LV: 3   R2v: 0.749  RMSEv: 4.645  Criterion: A4
4   Pred: 18  LV: 3   R2v: 0.752  RMSEv: 4.611  Criterion: A4
5   Pred: 16  LV: 3   R2v: 0.752  RMSEv: 4.61   Criterion: A4
6   Pred: 13  LV: 3   R2v: 0.76   RMSEv: 4.537  Criterion: A1
7   Pred: 11  LV: 3   R2v: 0.768  RMSEv: 4.466  Criterion: A4
8   Pred: 9   LV: 3   R2v: 0.775  RMSEv: 4.397  Criterion: A4

Predictors: 9   Observations: 40   Latent vectors: 3   Run: 8 
RMSE(CAL): 4.09   RMSE(LOO): 4.4   
R2(CAL): 0.805    R2(LOO): 0.775   

Predictors: 9   Observations: 40   Latent vectors: 3   Run: 8 
RMSE(CAL): 4.09   RMSE(LOO): 4.4   
R2(CAL): 0.805    R2(LOO): 0.775   

autopls documentation built on May 2, 2019, 9:39 a.m.