ns {ACSWR}

Simulated Sample from Normal Distribution

Description

The data set is used to understand the sampling variation of the score function. The simulated data is available in Pawitan (2001).

Usage

data(ns)

Format

A data frame with 10 observations on the following 20 variables.

Sample.1

a numeric vector

Sample.2

a numeric vector

Sample.3

a numeric vector

Sample.4

a numeric vector

Sample.5

a numeric vector

Sample.6

a numeric vector

Sample.7

a numeric vector

Sample.8

a numeric vector

Sample.9

a numeric vector

Sample.10

a numeric vector

Sample.11

a numeric vector

Sample.12

a numeric vector

Sample.13

a numeric vector

Sample.14

a numeric vector

Sample.15

a numeric vector

Sample.16

a numeric vector

Sample.17

a numeric vector

Sample.18

a numeric vector

Sample.19

a numeric vector

Sample.20

a numeric vector

Source

Pawitan, Y. (2001). In All Likelihood. Oxford Science Publications.

References

Pawitan, Y. (2001). In All Likelihood. Oxford Science Publications.

Examples

library(stats4)
data(ns)
x <- ns[,1]
nlogl <- function(mean,sd) { -sum(dnorm(x,mean=mean,sd=sd,log=TRUE)) }
norm_mle <- mle(nlogl,start=list(mean=median(x),sd=IQR(x)),nobs=length(x))
summary(norm_mle)