gof.functions: Functions to explore and determine GOF

In seeg: Statistics for Environmental Sciences, Engineering, and Geography

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)

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

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

Note

Input files are in 'datafiles.zip' in directory 'datafiles' and organized by chapters of Acevedo (2013).

Author(s)

Miguel F. Acevedo [e-mail]

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)

seeg documentation built on May 30, 2017, 7:09 a.m.