Discriminant Analysis (FDA and MDA) plots in R

Question

I am trying to plot the results of Flexible discriminant analysis(FDA) and Mixture discriminant analysis(MDA) using the mda and ggplot2 package. I did it for Linear discriminant analysis(LDA) but I do not know to continue. Any help or ideas how to code these graphs using ggplot2?

Code:

require(MASS)
require(ggplot2)
require(mda)
require(scales)
  
irislda <- lda(Species ~ ., iris)

prop.lda = irislda$svd^2/sum(irislda$svd^2)
plda <- predict(irislda,   iris)

dataset = data.frame(species = iris[,"Species"], irislda = plda$x)
p1 <- ggplot(dataset) + geom_point(aes(irislda.LD1, irislda.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=""))
p1 

irisfda <- fda(Species ~ ., data = iris, method = mars)
 
irismda <- mda(Species ~ ., data = iris)

Answer 1

I believe this gets to what you are after. The fda model only has two dimensions, so it is explaining 100%. The mda model had 5 dimensions, so I am only showing the two that explain the most.

library(dplyr)
irisfda <- fda(Species ~ ., data = iris, method = mars)

irisfda$fit$fitted.values %>% 
  as_tibble() %>% 
  bind_cols(species = iris[,"Species"]) %>%
  ggplot() +
  geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) + 
  labs(x = paste("FDA1 (", percent(irisfda$percent.explained[1]/100), ")", sep=""),
       y = paste("FDA2 (", percent(irisfda$percent.explained[2]/100 - irisfda$percent.explained[1]/100), ")", sep=""))

irismda <- mda(Species ~ ., data = iris)

irismda$fit$fitted.values %>% 
  as_tibble() %>% 
  bind_cols(species = iris[,"Species"]) %>% 
  ggplot() +
  geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) + 
  labs(x = paste("MDA1 (", percent(irismda$percent.explained[1]/100), ")", sep=""),
       y = paste("MDA2 (", percent(irismda$percent.explained[2]/100 - irismda$percent.explained[1]/100), ")", sep=""))

EDIT:

To get rid of the warning that you are seeing, we can name the columns of the matrix before passing it to as_tibble. This edit does not use the %>% operator.

colnames(irisfda$fit$fitted.values) <- c("V1", "V2")
df1 <- bind_cols(as_tibble(irisfda$fit$fitted.values),
                 species = iris[,"Species"])
ggplot(df1) +
  geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) + 
  labs(x = paste("FDA1 (", percent(irisfda$percent.explained[1]/100), ")", sep=""),
       y = paste("FDA2 (", percent(irisfda$percent.explained[2]/100 - irisfda$percent.explained[1]/100), ")", sep=""))


colnames(irismda$fit$fitted.values) <- c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8") 
df2 <- bind_cols(as_tibble(irismda$fit$fitted.values), 
                 species = iris[,"Species"])
ggplot(df2) +
  geom_point(aes(V1, V2, color = species, shape = species), size = 2.5) + 
  labs(x = paste("MDA1 (", percent(irismda$percent.explained[1]/100), ")", sep=""),
       y = paste("MDA2 (", percent(irismda$percent.explained[2]/100 - irismda$percent.explained[1]/100), ")", sep=""))

EDIT 2: It seems that you don't want to use dplyr so I am including base R functions here with the ggplot plot.

library(dplyr)
require(MASS)
require(ggplot2)
require(mda)
require(scales)
irisfda <- fda(Species ~ ., data = iris, method = mars)

irismda <- mda(Species ~ ., data = iris)

df1 <- cbind(data.frame(irisfda$fit$fitted.values),
                 species = iris[,"Species"])
ggplot(df1) +
  geom_point(aes(X1, X2, color = species, shape = species), size = 2.5) + 
  labs(x = paste("FDA1 (", percent(irisfda$percent.explained[1]/100), ")", sep=""),
       y = paste("FDA2 (", percent(irisfda$percent.explained[2]/100 - irisfda$percent.explained[1]/100), ")", sep=""))


df2 <- cbind(data.frame(irismda$fit$fitted.values), 
                 species = iris[,"Species"])
ggplot(df2) +
  geom_point(aes(X1, X2, color = species, shape = species), size = 2.5) + 
  labs(x = paste("MDA1 (", percent(irismda$percent.explained[1]/100), ")", sep=""),
       y = paste("MDA2 (", percent(irismda$percent.explained[2]/100 - irismda$percent.explained[1]/100), ")", sep=""))