126 lines
3.7 KiB
R
126 lines
3.7 KiB
R
#ZAD1
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head(USArrests)
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pairs(USArrests)
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#UrbanPop jest najsłabiej skorelowana z pozostałymi
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cor.test(USArrests$Murder,USArrests$UrbanPop, method="pearson")
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cor.test(USArrests$Rape,USArrests$UrbanPop, method="pearson")
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(pca_1 <- prcomp(~ Murder + Assault + Rape, data = USArrests, scale = TRUE))
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summary(pca_1)
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head(pca_1$x)
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cat("...")
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pca_1$rotation
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par(mfrow = c(1, 2))
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matplot(pca_1$rotation, type = 'l', lty = 1, lwd = 2,
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xlab = 'zmienne', ylab = 'ładunki', ylim = c(-0.9, 1.05),
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xaxt = "n")
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axis(1, at = 1:3, labels = rownames(pca_1$rotation))
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legend('topleft', legend = c('PC1', 'PC2', 'PC3'), ncol = 3, col = 1:3, lwd = 2)
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text(rep(1, 3), pca_1$rotation[1, ], round(pca_1$rotation[1, ], 2), pos = 4)
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text(rep(2, 3), pca_1$rotation[2, ], round(pca_1$rotation[2, ], 2), pos = 1)
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text(rep(3, 3), pca_1$rotation[3, ], round(pca_1$rotation[3, ], 2), pos = 2)
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matplot(abs(pca_1$rotation), type = 'l', lty = 1, lwd = 2,
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xlab = 'zmienne', ylab = '|ładunki|', ylim = c(0, 1.05),
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xaxt = "n")
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axis(1, at = 1:3, labels = rownames(pca_1$rotation))
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legend('topleft', legend = c('PC1', 'PC2', 'PC3'), ncol = 3, col = 1:3, lwd = 2)
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text(rep(1, 3), abs(pca_1$rotation)[1, ], abs(round(pca_1$rotation[1, ], 2)), pos = 4)
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text(rep(2, 3), abs(pca_1$rotation)[2, ], abs(round(pca_1$rotation[2, ], 2)), pos = 1)
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text(rep(3, 3), abs(pca_1$rotation)[3, ], abs(round(pca_1$rotation[3, ], 2)), pos = 2)
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par(mfrow = c(1, 1))
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plot(pca_1)
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# trzecie podejście
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# wartości własne = wariancje
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pca_1$sdev^2
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mean(pca_1$sdev^2)
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## 1, tak musi być przy skalowaniu
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# Pomijamy te składowe główne, których wartości własne są mniejsze od średniej.
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# Zatem wybieramy jedną.
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biplot(pca_1)
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install.packages("ape")
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library(ape)
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plot(mst(dist(scale(USArrests[, -3]))), x1 = pca_1$x[, 1], x2 = pca_1$x[, 2])
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#ZAD2
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mtcars_sel <- mtcars[, c(1, 3:7)]
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(pca_2 <- prcomp(mtcars_sel, scale = TRUE))
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summary(pca_2)
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head(pca_2$x)
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cat("...")
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pca_2$rotation
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par(mfrow = c(2, 1))
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matplot(pca_2$rotation, type = 'l', lty = 1, lwd = 2,
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xlab = 'zmienne', ylab = 'ładunki', ylim = c(-0.9, 1.05),
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xaxt = "n")
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axis(1, at = 1:6, labels = rownames(pca_2$rotation))
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legend('topleft', legend = c('PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'), ncol = 6, col = 1:6, lwd = 2)
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matplot(abs(pca_2$rotation), type = 'l', lty = 1, lwd = 2,
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xlab = 'zmienne', ylab = '|ładunki|', ylim = c(0, 1.05),
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xaxt = "n")
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axis(1, at = 1:6, labels = rownames(pca_2$rotation))
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legend('topleft', legend = c('PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'), ncol = 6, col = 1:6, lwd = 2)
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par(mfrow = c(1, 1))
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plot(pca_2)
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# trzecie podejście
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# wartości własne = wariancje
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pca_2$sdev^2
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mean(pca_2$sdev^2)
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## 1, tak musi być przy skalowaniu
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# Pomijamy te składowe główne, których wartości własne są mniejsze od średniej.
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# Zatem wybieramy dwie.
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biplot(pca_2)
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library(ape)
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plot(mst(dist(mtcars_sel)), x1 = pca_2$x[, 1], x2 = pca_2$x[, 2])
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(pca_3 <- prcomp(mtcars_sel, scale = FALSE, center = FALSE))
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summary(pca_3)
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head(pca_3$x)
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cat("...")
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pca_3$rotation
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par(mfrow = c(2, 1))
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matplot(pca_3$rotation, type = 'l', lty = 1, lwd = 2,
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xlab = 'zmienne', ylab = '<EFBFBD>adunki', ylim = c(-0.9, 1.15),
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xaxt = "n")
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axis(1, at = 1:6, labels = rownames(pca_3$rotation))
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legend('topleft', legend = c('PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'), ncol = 6, col = 1:6, lwd = 2)
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matplot(abs(pca_3$rotation), type = 'l', lty = 1, lwd = 2,
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xlab = 'zmienne', ylab = '|<7C>adunki|', ylim = c(0, 1.1),
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xaxt = "n")
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axis(1, at = 1:6, labels = rownames(pca_3$rotation))
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legend('topleft', legend = c('PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6'), ncol = 6, col = 1:6, lwd = 2)
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par(mfrow = c(1, 1))
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plot(pca_3)
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#1
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pca_3$sdev^2
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mean(pca_3$sdev^2)
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