clt.ani {animation} | R Documentation |
First of all, a number of obs
observations are generated from a certain distribution for each variable X_j, j = 1, 2, ..., n, and n = 1, 2, ..., nmax, then the sample means are computed, and at last the density of these sample means is plotted as the sample size n increases, besides, the p-values from the normality test shapiro.test
are computed for each n and plotted at the same time.
clt.ani(obs = 300, FUN = rexp, col = c("bisque", "red", "black"), mat = matrix(1:2, 2), widths = rep(1, ncol(mat)), heights = rep(1, nrow(mat)), ...)
obs |
the number of sample points to be generated from the distribution |
FUN |
the function to generate n random numbers from a certain distribution |
col |
a vector of length 2 specifying the colors of the histogram and the density line |
mat, widths, heights |
arguments passed to layout to set the layout of the two graphs. |
... |
other arguments passed to hist |
As long as the conditions of the Central Limit Theorem (CLT) are satisfied, the distribution of the sample mean will be approximate to the Normal distribution when the sample size n
is large enough, no matter what is the original distribution. The largest sample size is defined by nmax
in ani.options
.
None.
Yihui Xie
E. L. Lehmann, Elements of Large-Sample Theory. Springer-Verlag, New York, 1999.
http://animation.yihui.name/prob:central_limit_theorem
oopt = ani.options(interval = 0.1, nmax = 150) op = par(mar = c(3, 3, 1, 0.5), mgp = c(1.5, 0.5, 0), tcl = -0.3) clt.ani(type = "s") par(op) ## Not run: # HTML animation page ani.options(ani.height = 500, ani.width = 600, outdir = getwd(), nmax = 100, interval = 0.1, title = "Demonstration of the Central Limit Theorem", description = "This animation shows the distribution of the sample mean as the sample size grows.") ani.start() par(mar = c(3, 3, 1, 0.5), mgp = c(1.5, 0.5, 0), tcl = -0.3) clt.ani(type = "h") ani.stop() ## End(Not run) ani.options(oopt) # other distributions: Chi-square with df = 5 f = function(n) rchisq(n, 5) clt.ani(FUN = f)