library(zFactor) sum_tpr <- as.tibble(z.stats("HY")) sum_tpr$Tpr <- as.numeric(sum_tpr$Tpr) sum_tpr$Ppr <- as.numeric(sum_tpr$Ppr) library(rsm) swiss2.lm <- lm(RMSE ~ poly(Ppr, Tpr, degree=2), data=sum_tpr) par(mfrow=c(1,3)) image(swiss2.lm, Tpr ~ Ppr) contour(swiss2.lm, Tpr ~ Ppr) # persp(swiss2.lm, Tpr ~ Ppr, zlab = "RMSE")
# Ppr <- as.numeric(sum_tpr$Ppr) # Tpr <- as.numeric(sum_tpr$Tpr) # RMSE <- sum_tpr$RMSE sum_tpr <- as.tibble(z.stats("N10")) sum_tpr$Tpr <- as.numeric(sum_tpr$Tpr) sum_tpr$Ppr <- as.numeric(sum_tpr$Ppr) data.loess <- loess(RMSE ~ Ppr * Tpr, data = sum_tpr) xgrid <- seq(min(sum_tpr$Ppr), max(sum_tpr$Ppr), 0.1) ygrid <- seq(min(sum_tpr$Tpr), max(sum_tpr$Tpr), 0.2) data.fit <- expand.grid(Ppr = xgrid, Tpr = ygrid) mtrx3d <- predict(data.loess, newdata = data.fit) contour(x = xgrid, y = ygrid, z = mtrx3d, nlev = 10, method = "edge")
contour(x = xgrid, y = ygrid, z = mtrx3d)
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