least.squares {animation} | R Documentation |
This is a simple demonstration of the meaning of least squares in univariate linear regression. With either the intercept or the slope changing, the lines will be moving in the graph and corresponding residuals will be plotted. We can finally see the best estimate of the intercept and the slope from the residual plot.
least.squares(x, y, n = 15, ani.type = c("slope", "intercept"), a, b, a.range, b.range, ab.col = c("gray", "black"), est.pch = 19, v.col = "red", v.lty = 2, rss.pch = 19, rss.type = "o", mfrow = c(1, 2), ...)
x |
a numeric vector: the independent variable |
y |
a numeric vector: the dependent variable |
n |
the sample size: when x and y are missing, we use simulated values of y (x = 1:n and y = a + b * x + rnorm(n) ) |
ani.type |
"slope" : the slope is changing with the intercept fixed; "intercept" : intercept changing and slope fixed |
a, b |
the fixed intercept and slope; depending on ani.type , we only need to specify one of them; e.g. when ani.type == "slope" , we need to specify the value of a |
a.range, b.range |
a vector of length 2 to define the range of the intercept and the slope; only one of them need to be specified; see above |
ab.col |
the colors of two lines: the real regression line and the moving line with either intercept or slope changing |
est.pch |
the point character of the "estimated" values given x |
v.col, v.lty |
the color and line type of the vetical lines which demonstrate the residuals |
rss.pch, rss.type |
the point character and plot type of the residual plot |
mfrow |
defines the layout of the graph; see par |
... |
other parameters passed to plot to define the appearance of the scatterplot |
The value returned depends on the animation type.
If it is a slope animation, the value will be a list containing
lmfit |
the estimates of the intercept and slope with lm |
anifit |
the estimate of the slope in the animation |
If it is an intercept animation, the second component of the above list will be the estimate of the intercept.
Note the estimate will not be precise generally.
Yihui Xie
http://animation.yihui.name/lm:least_squares
opar = par(mar = c(5, 4, 0.5, 0.1)) oopt = ani.options(interval = 0.3, nmax = 50) # default animation: with slope changing least.squares() # intercept changing least.squares(ani.type = "i") par(opar) ## Not run: # save the animation in HTML pages ani.options(ani.height = 450, ani.width = 600, outdir = getwd(), title = "Demonstration of Least Squares", description = "We want to find an estimate for the slope in 50 candidate slopes, so we just compute the RSS one by one. ") ani.start() par(mar = c(4, 4, 0.5, 0.1), mgp = c(2, 0.5, 0), tcl = -0.3) least.squares() ani.stop() ## End(Not run) ani.options(oopt)