I have a data frame x1
, which was generated with the following piece of code,
x <- c(1:10)
y <- x^3
z <- y-20
s <- z/3
t <- s*6
q <- s*y
x1 <- cbind(x,y,z,s,t,q)
x1 <- data.frame(x1)
I would like to extract the y-axis intercept and the slope of the linear regression fit for the data,
x y z s t q
1 1 1 -19 -6.333333 -38 -6.333333
2 2 8 -12 -4.000000 -24 -32.000000
3 3 27 7 2.333333 14 63.000000
4 4 64 44 14.666667 88 938.666667
5 5 125 105 35.000000 210 4375.000000
6 6 216 196 65.333333 392 14112.000000
7 7 343 323 107.666667 646 36929.666667
8 8 512 492 164.000000 984 83968.000000
9 9 729 709 236.333333 1418 172287.000000
10 10 1000 980 326.666667 1960 326666.666667
I use the following codes to melt and plot three columns of data,
xm <- melt(x1, id=names(x1)[1], measure=names(x1)[c(2, 4, 5)], variable = "cols")
plt <- ggplot(xm) +
geom_point(aes(x=x,y= value, color=cols), size=3) +
labs(x = "x", y = "y")
Now what I require is to get a linear least squares fit for all the data separately and store the resulting intercept and slope in a new data frame.
I use plt + geom_abline()
but I don't get the desired result. Could someone let me know how to resolve this.