Supervised classification using learning vector quantization

Unified (formula-based) interface version of the learning vector quantization algorithms provided by class::olvq1(), class::lvq1(), class::lvq2(), and class::lvq3().

mlLvq(train, ...)

ml_lvq(train, ...)

# S3 method for class 'formula'
mlLvq(
  formula,
  data,
  k.nn = 5,
  size,
  prior,
  algorithm = "olvq1",
  ...,
  subset,
  na.action
)

# Default S3 method
mlLvq(train, response, k.nn = 5, size, prior, algorithm = "olvq1", ...)

# S3 method for class 'mlLvq'
summary(object, ...)

# S3 method for class 'summary.mlLvq'
print(x, ...)

# S3 method for class 'mlLvq'
predict(
  object,
  newdata,
  type = "class",
  method = c("direct", "cv"),
  na.action = na.exclude,
  ...
)

Arguments

train

a matrix or data frame with predictors.

...

further arguments passed to the classification method or its predict() method (not used here for now).

formula

a formula with left term being the factor variable to predict and the right term with the list of independent, predictive variables, separated with a plus sign. If the data frame provided contains only the dependent and independent variables, one can use the class ~ . short version (that one is strongly encouraged). Variables with minus sign are eliminated. Calculations on variables are possible according to usual formula convention (possibly protected by using I()).

data

a data.frame to use as a training set.

k.nn

k used for k-NN number of neighbor considered. Default is 5.

size

the size of the codebook. Defaults to min(round(0.4 \* nc \* (nc - 1 + p/2),0), n) where nc is the number of classes.

prior

probabilities to represent classes in the codebook (default values are the proportions in the training set).

algorithm

"olvq1" (by default, the optimized 'lvq1' version), or "lvq1", "lvq2", "lvq3".

subset

index vector with the cases to define the training set in use (this argument must be named, if provided).

na.action

function to specify the action to be taken if NAs are found. For [ml_lvq)] na.fail is used by default. The calculation is stopped if there is any NA in the data. Another option is na.omit, where cases with missing values on any required variable are dropped (this argument must be named, if provided). For the predict() method, the default, and most suitable option, is na.exclude. In that case, rows with NAs in newdata= are excluded from prediction, but reinjected in the final results so that the number of items is still the same (and in the same order as newdata=).

[ml_lvq)]: R:ml_lvq)

response

a vector of factor of the classes.

x, object

an mlLvq object

newdata

a new dataset with same conformation as the training set (same variables, except may by the class for classification or dependent variable for regression). Usually a test set, or a new dataset to be predicted.

type

the type of prediction to return. For this method, only "class" is accepted, and it is the default. It returns the predicted classes.

method

"direct" (default) or "cv". "direct" predicts new cases in newdata= if this argument is provided, or the cases in the training set if not. Take care that not providing newdata= means that you just calculate the self-consistency of the classifier but cannot use the metrics derived from these results for the assessment of its performances. Either use a different dataset in newdata= or use the alternate cross-validation ("cv") technique. If you specify method = "cv" then cvpredict() is used and you cannot provide newdata= in that case.

Value

ml_lvq()/mlLvq() creates an mlLvq, mlearning object containing the classifier and a lot of additional metadata used by the functions and methods you can apply to it like predict() or cvpredict(). In case you want to program new functions or extract specific components, inspect the "unclassed" object using unclass().

Examples

# Prepare data: split into training set (2/3) and test set (1/3)
data("iris", package = "datasets")
train <- c(1:34, 51:83, 101:133)
iris_train <- iris[train, ]
iris_test <- iris[-train, ]
# One case with missing data in train set, and another case in test set
iris_train[1, 1] <- NA
iris_test[25, 2] <- NA

iris_lvq <- ml_lvq(data = iris_train, Species ~ .)
summary(iris_lvq)
#> Codebook:
#>          Class Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 5       setosa     4.857229    3.289157     1.466867   0.2108434
#> 33      setosa     5.489655    4.074138     1.422414   0.2603448
#> 68  versicolor     5.652671    2.605862     3.875345   1.1383086
#> 66  versicolor     6.518048    3.077022     4.479406   1.3286514
#> 108  virginica     6.524326    2.925320     5.560317   2.0350052
#> 132  virginica     7.606122    3.132653     6.504082   2.0673469
predict(iris_lvq) # This object only returns classes
#>  [1] setosa     setosa     setosa     setosa     setosa     setosa    
#>  [7] setosa     setosa     setosa     setosa     setosa     setosa    
#> [13] setosa     setosa     setosa     setosa     setosa     setosa    
#> [19] setosa     setosa     setosa     setosa     setosa     setosa    
#> [25] setosa     setosa     setosa     setosa     setosa     setosa    
#> [31] setosa     setosa     setosa     versicolor versicolor versicolor
#> [37] versicolor versicolor versicolor versicolor versicolor versicolor
#> [43] versicolor versicolor versicolor versicolor versicolor versicolor
#> [49] versicolor versicolor versicolor versicolor versicolor versicolor
#> [55] versicolor versicolor versicolor versicolor versicolor versicolor
#> [61] versicolor versicolor versicolor versicolor versicolor versicolor
#> [67] virginica  virginica  virginica  virginica  virginica  virginica 
#> [73] versicolor virginica  virginica  virginica  virginica  virginica 
#> [79] virginica  virginica  virginica  virginica  virginica  virginica 
#> [85] virginica  versicolor virginica  virginica  virginica  versicolor
#> [91] virginica  virginica  versicolor versicolor virginica  virginica 
#> [97] virginica  virginica  virginica 
#> Levels: setosa versicolor virginica
#' # Self-consistency, do not use for assessing classifier performances!
confusion(iris_lvq)
#> 99 items classified with 94 true positives (error rate = 5.1%)
#>                Predicted
#> Actual          01 02 03 (sum) (FNR%)
#>   01 setosa     33  0  0    33      0
#>   02 versicolor  0 33  0    33      0
#>   03 virginica   0  5 28    33     15
#>   (sum)         33 38 28    99      5
# Use an independent test set instead
confusion(predict(iris_lvq, newdata = iris_test), iris_test$Species)
#> 50 items classified with 46 true positives (error rate = 8%)
#>                Predicted
#> Actual          01 02 03 04 (sum) (FNR%)
#>   01 versicolor 15  1  0  1    17     12
#>   02 virginica   2 15  0  0    17     12
#>   03 setosa      0  0 16  0    16      0
#>   04 NA          0  0  0  0     0       
#>   (sum)         17 16 16  1    50      8

Supervised classification using learning vector quantization

Arguments

Value

See also

Examples