Computes projection trimmed regression in 2 dimensions.

trimProjReg2d(x, y, alpha = 0.1)

## Arguments

x Independent variable Dependent variable Percentage of trimmed observations

## Examples


# EXAMPLE 1
data(pension)
plot(pension)abline(lm(Reserves ~ Income, data = pension), lty = 3, lwd = 2) # lmabline(trimProjReg2d(pension[, 1], pension[, 2]), lwd = 2) # trimprojreg2dlegend("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") # lmabline(trimProjReg2d(x, y), lwd = 2, col = "red") # trimmed reglegend("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')