> I.1d <- function(x) {

+   sin(4*x) *

+     x * ((x * ( x * (x*x-4) + 1) - 1))

+ }

> 

> adaptIntegrate(I.1d, -2, 2, tol=1e-7)

$integral

[1] 1.635644

$error

[1] 4.024021e-09

$functionEvaluations

[1] 105

$returnCode

[1] 0

> adaptIntegrate(I.2d, rep(-1, 2), rep(1, 2), maxEval=10000)

$integral

[1] -0.01797993

$error

[1] 7.845607e-07

$functionEvaluations

[1] 10013

$returnCode

[1] 0

> dmvnorm <- function (x, mean, sigma, log = FALSE) {
+     if (is.vector(x)) {
+         x <- matrix(x, ncol = length(x))
+     }
+     if (missing(mean)) {
+         mean <- rep(0, length = ncol(x))
+     }
+     if (missing(sigma)) {
+         sigma <- diag(ncol(x))
+     }
+     if (NCOL(x) != NCOL(sigma)) {
+         stop("x and sigma have non-conforming size")
+     }
+     if (!isSymmetric(sigma, tol = sqrt(.Machine$double.eps),
+         check.attributes = FALSE)) {
+         stop("sigma must be a symmetric matrix")
+     }
+     if (length(mean) != NROW(sigma)) {
+         stop("mean and sigma have non-conforming size")
+     }
+     distval <- mahalanobis(x, center = mean, cov = sigma)
+     logdet <- sum(log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values))
+     logretval <- -(ncol(x) * log(2 * pi) + logdet + distval)/2
+     if (log)
+         return(logretval)
+     exp(logretval)
+ }
> 
> m <- 3
> sigma <- diag(3)
> sigma[2,1] <- sigma[1, 2] <- 3/5 ; sigma[3,1] <- sigma[1, 3] <- 1/3
> sigma[3,2] <- sigma[2, 3] <- 11/15
> adaptIntegrate(dmvnorm, lower=rep(-0.5, m), upper=c(1,4,2),
+                         mean=rep(0, m), sigma=sigma, log=FALSE,
+                maxEval=10000)
$integral
[1] 0.3341125

$error
[1] 4.185435e-06

$functionEvaluations
[1] 10065

$returnCode
[1] 0

MC.simple.est <- function(g, a, b, n=1e4) {

  xi <- runif(n,a,b)      # step 1

  g.mean <- mean(g(xi))   # step 2

  (b-a)*g.mean            # step 3

}

runif(n,a,b)»n random sampling from uniform(a,b) distribution 

g <- function(x) exp(-x)

MC.simple.est(g, 2, 4)

 [1] 0.1161926

> MC.simple.est <- function(g, a, b, n=1e4) {
+   xi <- runif(n,a,b)      # step 1
+   g.mean <- mean(g(xi))   # step 2
+   (b-a)*g.mean            # step 3
+ }
> g <- function(x) 1/log(x)
> 
> MC.simple.est(g, 2, 4)
[1] 1.922819
> integrate(g,2,4)
1.922421 with absolute error < 7.2e-14
>

peg.tab

peg.df

expand.gird()

> pag.tab <- matrix(c(762, 484, 327, 239, 468, 477), nrow=2)

> dimnames(pag.tab) <-list(Gender=c("Female","Male"),Party=c("Democrat","Independent","Republican"))

> pag.tab <- as.table(pag.tab)

> pag.tab

        Party

Gender   Democrat Independent Republican

  Female      762         327        468

  Male        484         239        477

> # Or

> pag.df <-expand.grid(Gender=c("Female","Male"),Party=c("Democrat","Independent","Republican"))

> pag.df

  Gender       Party

1 Female    Democrat

2   Male    Democrat

3 Female Independent

4   Male Independent

5 Female  Republican

6   Male  Republican

>

data(iris)

chernoff face

setosa

install.packages("aplpack")
library(aplpack)
data = iris
faces(data[1:25, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")], 
    face.type = 1, scale = TRUE, labels = data$Species, plot.faces = TRUE, nrow.plot = 5, 
    ncol.plot = 5)

rm(list=ls())

mydata<-read.csv(file.choose())

mydata

fix(mydata)

install.packages("aplpack")

library(aplpack)

data =mydata

faces(data[1:30, c("Jamiyat", "M.R.S.J", "S.J.K", "M.SH","Masahat")], 

face.type = 1, scale = TRUE, 

labels = data$Species, 

plot.faces = TRUE, nrow.plot =6, 

ncol.plot = 5)