Run Code
|
API
|
Code Wall
|
Misc
|
Feedback
|
Login
|
Theme
|
Privacy
|
Patreon
LDA
library(MASS) data(iris) head(iris, 5) r <- lda(formula = Species ~ ., data = iris, prior = c(1,1,1)/3) r$prior r$counts r$means r$scaling r$svd prop = r$svd^2/sum(r$svd^2) prop r2 <- lda(formula = Species ~ ., data = iris, prior = c(1,1,1)/3, CV = TRUE) head(r2$class) head(r2$posterior, 3) train <- sample(1:150, 75) r3 <- lda(Species ~ ., # training model iris, prior = c(1,1,1)/3, subset = train) plda = predict(object = r, # predictions newdata = iris[-train, ]) head(plda$class) # classification result head(plda$posterior, 3) # posterior prob head(plda$x, 3) # LD projections require(MASS) require(ggplot2) require(scales) require(gridExtra) pca <- prcomp(iris[,-5], center = TRUE, scale. = TRUE) prop.pca = pca$sdev^2/sum(pca$sdev^2) lda <- lda(Species ~ ., iris, prior = c(1,1,1)/3) prop.lda = r$svd^2/sum(r$svd^2) plda <- predict(object = lda, newdata = iris) dataset = data.frame(species = iris[,"Species"], pca = pca$x, lda = plda$x) p1 <- ggplot(dataset) + geom_point(aes(lda.LD1, lda.LD2, colour = species, shape = species), size = 2.5) + labs(x = paste("LD1 (", percent(prop.lda[1]), ")", sep=""), y = paste("LD2 (", percent(prop.lda[2]), ")", sep="")) p2 <- ggplot(dataset) + geom_point(aes(pca.PC1, pca.PC2, colour = species, shape = species), size = 2.5) + labs(x = paste("PC1 (", percent(prop.pca[1]), ")", sep=""), y = paste("PC2 (", percent(prop.pca[2]), ")", sep="")) grid.arrange(p1, p2)
run
|
edit
|
history
|
help
0
AjusteLinearizado
lab1
Dbinom
Prediction
Data Transformation
EulerEx2
31-08-2020-Exemplo AjusteNaoL
Teste
Sampling Distribution for the Wald-Wolfowitz 2-Sample Runs Test
asdf