data(HouseVotes84, package = "mlbench")
house_votes84 = (as.matrix(HouseVotes84[, -1]) == "y") * 1.0
rownames(house_votes84) = HouseVotes84$Class
colnames(house_votes84) = c("handicapped-infants",
"water-project-cost-sharing",
"adoption-of-the-budget-resolution",
"physician-fee-freeze",
"el-salvador-aid",
"religious-groups-in-schools",
"anti-satellite-test-ban",
"aid-to-nicaraguan-contras",
"mx-missile",
"immigration",
"synfuels-corporation-cutback",
"education-spending",
"superfund-right-to-sue",
"crime",
"duty-free-exports",
"export-administration-act-south-africa")
save(house_votes84, file = "data/house_votes84.rdata", compress = "xz")
# another file data(House2001, package = "gnm")
# library(logisticPCA)
# cv = cv.lpca(votes84_mat, 2, Ms = 1:10)
# plot(cv)
# lpca = logisticPCA(votes84_mat, 2, M = 5)
#
# plot(lpca, "scores")
#
# library(ggplot2)
# dfLPCA = data.frame(PC = lpca$PCs, Class = HouseVotes84$Class)
# ggplot(dfLPCA, aes(PC.1, PC.2, colour = Class)) + geom_point()
#
#
# lsvd = logisticSVD(votes84_mat, 2)
#
# plot(lsvd, "scores")
#
# dfLSVD = data.frame(PC = lsvd$A, Class = HouseVotes84$Class)
# ggplot(dfLSVD, aes(PC.1, PC.2, colour = Class)) + geom_point()
#
# library(generalizedPCA)
# pca = generalizedPCA(votes84_mat, 2)
#
# dfPCA = data.frame(PC = pca$PCs, Class = HouseVotes84$Class)
# ggplot(dfPCA, aes(PC.1, PC.2, colour = Class)) + geom_point()
#
#
#
# # LDA ---------------------------------------------------------------------
# library(MASS)
#
# df_lda = data.frame(Class = HouseVotes84$Class,
# PC = pca$PCs,
# LPC = lpca$PCs,
# A = lsvd$A
# )
#
# lda_pca = lda(Class ~ PC.1 + PC.2, data = df_lda)
# lda_lpca = lda(Class ~ LPC.1 + LPC.2, data = df_lda)
# lda_lsvd = lda(Class ~ A.1 + A.2, data = df_lda)
#
# mean(predict(lda_pca)$class == df_lda$Class)
# mean(predict(lda_lpca)$class == df_lda$Class)
# mean(predict(lda_lsvd)$class == df_lda$Class)
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