Description Usage Arguments Author(s) Examples
View source: R/trimprojreg2d.R
Computes projection trimmed regression in 2 dimensions.
1 | trimProjReg2d(x, y, alpha = 0.1)
|
x |
Independent variable |
y |
Dependent variable |
alpha |
Percentage of trimmed observations |
Zygmunt Zawadzki from Cracow University of Economics.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # EXAMPLE 1
data(pension)
plot(pension)
abline(lm(Reserves ~ Income, data = pension), lty = 3, lwd = 2) # lm
abline(trimProjReg2d(pension[, 1], pension[, 2]), lwd = 2) # trimprojreg2d
legend("bottomright", c("OLS", "TrimLS"), lty = 1:2)
# EXAMPLE 2
data(under5.mort)
data(inf.mort)
data(maesles.imm)
data2011 <- na.omit(cbind(under5.mort[, 22], inf.mort[, 22],
maesles.imm[, 22]))
x <- data2011[, 3]
y <- data2011[, 2]
plot(x, y, cex = 1.2, ylab = "infant mortality rate per 1000 live birth",
xlab = "against masles immunized percentage",
main = "Projection Depth Trimmed vs. LS regressions")
abline(lm(x ~ y), lwd = 2, col = "black") # lm
abline(trimProjReg2d(x, y), lwd = 2, col = "red") # trimmed reg
legend("bottomleft", c("LS", "TrimReg"), fill = c("black", "red"), cex = 1.4,
bty = "n")
##### Comparsion of a few regression methods #####
library(DepthProc)
library(MASS)
data("france")
plot(UR ~ MW, pch = 19, data = france)
# linear regression
lm.fit <- lm(UR ~ MW, data = france)
abline(lm.fit, lwd=2, cex=3, col='red')
# M-estimator
rlm.fit <- rlm(UR ~ MW, data = france)
abline(rlm.fit, lwd = 2,col = "blue")
# LMS
lqs.lms <- lqs(UR ~ MW, method = "lms", data = france) #least median of squares#
lqs.lts <- lqs(UR ~ MW, method = "lts", data = france) #least trimmed squares#
abline(lqs.lms, lwd = 2, col="green")
abline(lqs.lts, lwd = 2, col="pink")
# Lowess
lines(lowess(france$MW, france$UR, f = 0.5, iter = 0), lwd = 2) # loess
# Depth trimmed regression
trim.reg <- trimProjReg2d(france$MW, france$UR) #trimprojreg2d
abline(trim.reg, lwd = 4, col = 'orange')
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