81 lines
2.8 KiB
R
81 lines
2.8 KiB
R
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library(ISLR) # for Hitters dataset
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library(MAVE)
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library(CVE)
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# Set global parameters.
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seed <- 21
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max.dim <- 5L
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attempts <- 25L
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max.iter <- 100L
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# momentum <- 0.0
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# Prepair data for analysis.
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cols <- c("Salary", "AtBat", "Hits", "HmRun", "Runs", "RBI",
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"Walks", "Years", "CAtBat","CHits", "CHmRun","CRuns",
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"CRBI", "CWalks", "PutOuts", "Assists", "Errors")
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outliers <- c(92, # Gary Pettis
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120, # John Moses
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173, # Milt Thompson
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189, # Rick Burleson
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220, # Scott Fletcher
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230, # Tom Foley
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241) # Terry Puhl
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# Subselect as a matrix without outliers as well as reordered
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# and filtered columns.
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ds <- na.omit(Hitters[, cols])[-outliers, ]
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ds$Salary <- log(ds$Salary)
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ds <- scale(ds, center = TRUE, scale = TRUE)
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# Split into data and responce.
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X <- as.matrix(ds[, colnames(ds) != "Salary"])
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Y <- as.matrix(ds[, "Salary"])
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pdf("results/hitters.pdf", width = 15, height = 5)
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layout(matrix(c(1, 2), nrow = 1))
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cat("Seed: ", seed, "\n\n")
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################################################################################
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### meanMAVE ###
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################################################################################
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set.seed(seed)
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dr <- mave(Y ~ X, method = "meanMAVE", max.dim = max.dim)
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dim <- which.min(mave.dim(dr)$cv)
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B <- coef(dr, 2L)
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# train linear model on reduced data
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data <- as.data.frame(cbind(Y, X %*% B))
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colnames(data) <- c("Y", "dir1", "dir2")
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model <- lm(Y ~ dir1 + I(dir1^2) + dir2, data = data)
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# Print and log.
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cat("### meanMAVE\n\nest.dim: ", dim, '\n\n')
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summary(model)
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plot(x = data[, "dir1"], y = data[, "Y"],
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main = "meanMave", xlab = "dir1", ylab = "Y")
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plot(x = data[, "dir2"], y = data[, "Y"],
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main = "meanMave", xlab = "dir2", ylab = "Y")
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################################################################################
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### CVE ###
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################################################################################
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set.seed(seed)
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dr <- cve(Y ~ X, max.dim = max.dim, max.iter = max.iter, attempts = attempts)
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dim <- predict_dim(dr, method = "cv")$k
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B <- coef(dr, 2L)
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# Determine 1st direction (cause CVE does not care for column order).
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if (which.max(abs(t(coef(dr, 1L)) %*% B)) == 2) {
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B <- B[, c(2, 1)] # switch directions
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}
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# train linear model on reduced data
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data <- as.data.frame(cbind(Y, X %*% B))
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colnames(data) <- c("Y", "dir1", "dir2")
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model <- lm(Y ~ dir1 + I(dir1^2) + dir2, data = data)
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# Print and log.
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cat("### CVE\n\nest.dim: ", dim, '\n\n')
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summary(model)
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plot(x = data[, "dir1"], y = data[, "Y"],
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main = "CVE", xlab = "dir1", ylab = "Y")
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plot(x = data[, "dir2"], y = data[, "Y"],
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main = "CVE", xlab = "dir2", ylab = "Y")
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dev.off()
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