# A little Regression
x <- rnorm(100)           # 100 random numbers from a normal(0,1) distribution
y <- exp(x) + rnorm(100)  # an exponential function with error
result <- lsfit(x,y)      # regress x on y and store the results
ls.print(result)          # print the regression results
plot(x,y)                 # pretty obvious what this does
abline(result)            # add the regression line to the plot
lines(lowess(x,y), col=2) # add a nonparametric regression line (a smoother)
hist(result$residuals)    # histogram of the residuals from the regression

# Boxplots
n <- 10
g <- gl(n, 100, n * 100)
x <- rnorm(n * 100) + sqrt(as.integer(g))
boxplot(split(x, g), col = "lavender", notch = TRUE)

# Scatter plot matrix
data("iris")
pairs(iris[1:4], main = "Edgar Anderson's Iris Data", font.main = 4, 
        pch = 19)
pairs(iris[1:4], main = "Edgar Anderson's Iris Data", pch = 21, 
        bg = c("red", "green3", "blue")[codes(iris$Species)])

# Coplots
data(quakes)
coplot(long ~ lat | depth, data = quakes, pch = 21, bg = "green3")

# cited from Eiji Nakama's Rweb-jp, originally written by Hunao.
x <- seq(-10, 10, length= 30)
y <- x
f <- function(x,y) { r <- sqrt(x^2+y^2); 10 * sin(r)/r }
z <- outer(x, y, f)
z[is.na(z)] <- 1
op <- par(bg = "white")
persp(x, y, z, theta = 30, phi = 30, expand = 0.5, col = "lightblue")

# Image and contour plots (These are Owww-Ahhh plots)
opar <- par(ask = interactive() && .Device == "X11")
data(volcano)
x <- 10 * (1:nrow(volcano))
x.at <- seq(100, 800, by = 100)
y <- 10 * (1:ncol(volcano))
y.at <- seq(100, 600, by = 100)
image(x, y, volcano, col = terrain.colors(100), axes = FALSE)

rx <- range(x <- 10*1:nrow(volcano))
ry <- range(y <- 10*1:ncol(volcano))
ry <- ry + c(-1,1) * (diff(rx) - diff(ry))/2
tcol <- terrain.colors(12)
par(opar); par(mfrow=c(1,1)); opar <- par(pty = "s", bg = "lightcyan")
plot(x = 0, y = 0,type = "n", xlim = rx, ylim = ry, xlab = "", ylab = "")
u <- par("usr")
rect(u[1], u[3], u[2], u[4], col = tcol[8], border = "red")
contour(x, y, volcano, col = tcol[2], lty = "solid", add = TRUE)
title("A Topographic Map of Maunga Whau", font = 4)
abline(h = 200*0:4, v = 200*0:4, col = "lightgray", lty = 2, lwd = 0.1)
par(opar)

short example, that also illustrates reading in data from a URL.

plot(x, y)
result <- lm(y ~ x)
abline(result)
summary(result)

Use this along with the "URL"
http://www.stat.umn.edu/geyer/somedata.txt

a sample code using sem package, originally posted by Mitsuo Igarashi [fpr 2411]

library(sem)

# lower triangle of covariance matrix
data.cov <- matrix(c(
  0.862,  0,      0,      0,      0,      0,
  0.489,  1.089,  0,      0,      0,      0,
 -0.056, -0.189,  0.606,  0,      0,      0,
 -0.122, -0.222,  0.406,  0.606,  0,      0,
  0.016, -0.044, -0.089, -0.022,  0.262,  0,
  0.067,  0.133, -0.200, -0.167,  0.200,  0.533
), ncol=6, byrow=T)

rownames(data.cov) <- colnames(data.cov) <- c('V1', 'V2', 'V3', 'V4', 'V5', 'V6')

# symbolic 'ram'
data.model <- matrix(c(
 'F1  -> V1', 'a1',  NA,
 'F1  -> V2', 'a1',  NA,
 'F2  -> V3', 'a2',  NA,  
 'F2  -> V4', 'a2',  NA,
 'F3  -> V5', 'a3',  NA,
 'F3  -> V6', 'a3',  NA,
 'V1 <-> V1', 'e1',  NA,
 'V2 <-> V2', 'e1',  NA,
 'V3 <-> V3', 'e2',  NA, 
 'V4 <-> V4', 'e2',  NA,
 'V5 <-> V5', 'e3',  NA,
 'V6 <-> V6', 'e3',  NA,
 'F1 <-> F1',  NA,    1,
 'F2 <-> F2',  NA,    1,
 'F3 <-> F3',  NA,    1,
 'F1 <-> F2', 'f12', NA, 
 'F2 <-> F3', 'f23', NA, 
 'F1 <-> F3', 'f13', NA  
), ncol=3, byrow=T)

data.sem <- sem(data.model, data.cov, 30)

summary(data.sem)