seeg (version 1.0)

GOF: Functions to explore and determine GOF

Description

Functions to explore and determine GOF.

Usage

cdf.plot(x)
cdf.plot.gof(x,dist="normal",mu=0,sd=1,rate=1,min=0,max=1,shape=1,scale=1)
chisq.gof.norm(x, nclass, param.est)

Arguments

x

data for one variable

dist

distribution

mu

mean for normal dist

sd

standard deviation for normal dist

rate

rate parameter for exponential dist

min

minimum for uniform dist

max

maximum for uniform dist

shape

shape parameter for Weibull dist

scale

scale parameter for Weibull dist

nclass

number of bins for chisquare test

param.est

number of estimated parameter (to calculate df)

Value

• Fx.Emp
  CDF Empirical
• Fx.Theo
  CDF Theoretical
• Fx.Diff
  CDF Empirical minus CDF Theoretical
• X2
  Chisquare
• df
  Degrees of freedom
• p.value
  p-value
• observed
  Observations in each class

Details

cdf.plot: Compare CDF empirical and theoretical for normal cdf.plot.gof: Compare CDF empirical and theoretical for Normal, Uniform, Exponential, and Weibull. chisq.gof.norm: Determines GOF to a normal by Chisq

References

Acevedo M.F. 2013. "Data Analysis and Statistics for Geography, Environmental Science, and Engineering", CRC Press.

See Also

EDA functions eda6, ECDF stats ecdf, GOF tests ks.test, Shapiro test shapiro.test

Examples

x <- rnorm(100)
cdf.plot(x)

x <- runif(100)
cdf.plot(x)

xn <- rnorm(20)
cdf.plot.gof(xn,dist="normal")
mtext(side=3,line=2,"Sample Normal, Hyp Normal",cex=0.7)

xu <- runif(20,0,1)
cdf.plot.gof(xu,dist="unif")
mtext(side=3,line=2,"Sample Unif, Hyp Unif",cex=0.7)

z <- rnorm(100)
chisq.gof.norm(z,4,0)