DA {MVar} | R Documentation |
Perform linear and quadratic discriminant analysis.
DA(data, class = NA, type = "lda", validation = "Learning", method = "moment", prior = NA, testing = NA)
data |
Data to be classified. |
class |
Vector with data classes names. |
type |
"lda": linear discriminant analysis (default), or |
validation |
Type of validation: |
method |
Classification method: |
prior |
Probabilities of occurrence of classes. If not specified, it will take the proportions of the classes. If specified, probabilities must follow the order of factor levels. |
testing |
Vector with indices that will be used in data as test. For validation = "Learning", one has testing = NA. |
confusion |
Confusion table. |
error.rate |
Overall error ratio. |
prior |
Probability of classes. |
type |
Type of discriminant analysis. |
validation |
Type of validation. |
num.class |
Number of classes. |
class.names |
Class names. |
method |
Classification method. |
num.correct |
Number of correct observations. |
results |
Matrix with comparative classification results. |
Paulo Cesar Ossani
Marcelo Angelo Cirillo
FERREIRA, D. F. Estatistica Multivariada. 2a ed. revisada e ampliada. Lavras: Editora UFLA, 2011. 676 p.
MINGOTI, S. A. Analise de dados atraves de metodos de estatistica multivariada: uma abordagem aplicada. Belo Horizonte: UFMG, 2005. 297 p.
RENCHER, A. C. Methods of multivariate analysis. 2th. ed. New York: J.Wiley, 2002. 708 p.
RIPLEY, B. D. Pattern Recognition and Neural Networks. Cambridge University Press, 1996.
VENABLESs, W. N. and RIPLEY, B. D. Modern Applied Statistics with S. Fourth edition. Springer, 2002.
data(iris) # data set data = iris[,1:4] # data to be classified class = iris[,5] # data class prior = c(1,1,1)/3 # a priori probability of the classs Res <- DA(data, class, type = "lda", validation = "Learning", method = "mle", prior = prior, testing = NA) print("confusion table:"); Res$confusion print("Overall hit ratio:"); 1 - Res$error.rate print("Probability of classes:"); Res$prior print("classification method:"); Res$method print("type of discriminant analysis:"); Res$type print("class names:"); Res$class.names print("Number of classess:"); Res$num.class print("type of validation:"); Res$validation print("Number of correct observations:"); Res$num.correct print("Matrix with comparative classification results:"); Res$results ### cross-validation ### amostra = sample(2, nrow(data), replace = TRUE, prob = c(0.7,0.3)) datatrain = data[amostra == 1,] # training data datatest = data[amostra == 2,] # test data dim(datatrain) # training data dimension dim(datatest) # test data dimension testing = as.integer(rownames(datatest)) # test data index Res <- DA(data, class, type = "qda", validation = "testing", method = "moment", prior = NA, testing = testing) print("confusion table:"); Res$confusion print("Overall hit ratio:"); 1 - Res$error.rate print("Number of correct observations:"); Res$num.correct print("Matrix with comparative classification results:"); Res$results