PP_Index {MVar.pt} | R Documentation |
Funcao usada para encontrar os indices da projection pursuit (PP).
PP_Index(data, class = NA, vector.proj = NA, findex = "HOLES", dimproj = 2, weight = TRUE, lambda = 0.1, r = 1, ck = NA)
data |
Conjunto de dados numericos sem a informacao das classes. |
class |
Vetor com os nomes das classes dos dados. |
vector.proj |
Vetor projecao. |
findex |
Funcao indice de projecao a ser usada: |
dimproj |
Dimensao da projecao dos dados (default = 2). |
weight |
Usado nos indice LDA, PDA e Lr, para ponderar os calculos pelo numero de elementos em cada classe (default = TRUE). |
lambda |
Usado no indice PDA (default = 0.1). |
r |
Usado no indice Lr (default = 1). |
ck |
Uso interno da funcao indice CHI. |
num.class |
Numero de classes. |
class.names |
Nomes das classes. |
findex |
Funcao indice de projecao usada. |
vector.proj |
Vetores de projecao encontrados. |
index |
Indice de projecao encontrado no processo. |
Paulo Cesar Ossani
Marcelo Angelo Cirillo
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PP_Optimizer
and Plot.PP
data(iris) # conjunto de dados data <- iris[,1:4] # Exemplo 1 - Sem as classes nos dados Ind <- PP_Index(data = data, class = NA, vector.proj = NA, findex = "moment", dimproj = 2, weight = TRUE, lambda = 0.1, r = 1) print("Numero de classes:"); Ind$num.class print("Nomes das classes:"); Ind$class.names print("Funcao indice de projecao:"); Ind$findex print("Vetores de projecao:"); Ind$vector.proj print("Indice de projecao:"); Ind$index # Exemplo 2 - Com as classes nos dados class <- iris[,5] # classe dos dados Fcindex <- "pda" # funcao indice sphere <- TRUE # Dados esfericos Res <- PP_Optimizer(data = data, class = class, findex = Fcindex, optmethod = "SA", dimproj = 2, sphere = sphere, weight = TRUE, lambda = 0.1, r = 1, cooling = 0.9, eps = 1e-3, maxiter = 1000, half = 30) # Comparando o resultado obtido if (match(toupper(Fcindex),c("LDA", "PDA", "LR"), nomatch = 0) > 0) { if (sphere) { data <- apply(predict(prcomp(data)), 2, scale) # dados esfericos } } else data <- as.matrix(Res$proj.data[,1:Dim]) Ind <- PP_Index(data = data, class = class, vector.proj = Res$vector.opt, findex = Fcindex, dimproj = 2, weight = TRUE, lambda = 0.1, r = 1) print("Numero de classes:"); Ind$num.class print("Nomes das classes:"); Ind$class.names print("Funcao indice de projecao:"); Ind$findex print("Vetores de projecao:"); Ind$vector.proj print("Indice de projecao:"); Ind$index print("Indice de projecao otimizado:"); Res$index[length(Res$index)]