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# https://designdatadecisions.wordpress.com/2016/05/05/manipulated-regression/
# Jyothi Subramanian, Ph. D.
library(manipulate)
## First define a custom function that fits a linear regression line
## to (x,y) points and overlays the regression line in a scatterplot.
## The plot is then 'manipulated' to change as y values change.
linregIllustrate <- function(x, y, e, h.max, h.med){
max.x <- max(x)
med.x <- median(x)
max.xind <- which(x == max.x)
med.xind <- which(x == med.x)
y1 <- y ## Modified y
y1[max.xind] <- y1[max.xind]+h.max ## at the end
y1[med.xind] <- y1[med.xind]+h.med ## at the center
plot(x, y1, xlim=c(min(x),max(x)+5), ylim=c(min(y1),max(y1)), pch=16,
xlab="X", ylab="Y")
text(x[max.xind], y1[max.xind],"I'm movable!", pos=3, offset = 0.3, cex=0.7, font=2, col="red")
text(x[med.xind], y1[med.xind],"I'm movable too!", pos=3, offset = 0.3, cex=0.7, font=2, col="red")
m <- lm(y ~ x) ## Regression with original set of points, the black line
abline(m, lwd=2)
m1 <- lm(y1 ~ x) ## Regression with modified y, the dashed red line
abline(m1, col="red", lwd=2, lty=2)
}
## Now generate some x and y data
x <- rnorm(35,10,5)
e <- rnorm(35,0,5)
y <- 3*x+5+e
## Plot and manipulate the plot!
manipulate(linregIllustrate(x, y, e, h.max, h.med),
h.max=slider(-100, 100, initial=0, step=10, label="Move y at the end"),
h.med=slider(-100, 100, initial=0, step=10, label="Move y at the center"))
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