ES.chisq.gof: Compute effect size of chi-squared test of goodness of fit

In powerAnalysis: Power Analysis in Experimental Design

Description

Compute effect size of chi-squared test of goodness of fit

Usage

ES.chisq.gof(p1 = NULL, p0 = rep(1/length(p1), length(p1)))

Arguments

p1	a vector of frequencies or probabilities (alternative hypothesis). Frequencies will be rescaled to probabilities automatically. An error is given if any entry of p1 is negative.
p0	a vector of frequencies or probabilities of the same length of p1 (null hypothesis). Frequencies will be rescaled to probabilities automatically. An error is given if any entry of p0 is negative. Default value of p0 is a vector of 1/n with length of n. n is the length of p1.

See Also

ES.chisq.assoc

Examples

ES.chisq.gof(p1=c(10,20,30,40))
ES.chisq.gof(p1=c(0.1,0.2,0.3,0.4))

ES.chisq.gof(p1=c(10,20,30,40),p0=c(0.2,0.3,0.3,0.2))
ES.chisq.gof(p1=c(10,20,30,40),p0=c(20,30,30,20))
ES.chisq.gof(p1=c(0.1,0.2,0.3,0.4),p0=c(0.2,0.3,0.3,0.2))
ES.chisq.gof(p1=c(0.1,0.2,0.3,0.4),p0=c(20,30,30,20))

Example output

     effect size of chi-squared test of goodness of fit 

              w = 0.4472136
             p1 = 0.1, 0.2, 0.3, 0.4
             p0 = 0.25, 0.25, 0.25, 0.25

NOTE: Probabilities were same as provided or were rescaled from provided frequencies
small effect size:  w = 0.1
medium effect size: w = 0.3
large effect size:  w = 0.5


     effect size of chi-squared test of goodness of fit 

              w = 0.4472136
             p1 = 0.1, 0.2, 0.3, 0.4
             p0 = 0.25, 0.25, 0.25, 0.25

NOTE: Probabilities were same as provided or were rescaled from provided frequencies
small effect size:  w = 0.1
medium effect size: w = 0.3
large effect size:  w = 0.5


     effect size of chi-squared test of goodness of fit 

              w = 0.5322906
             p1 = 0.1, 0.2, 0.3, 0.4
             p0 = 0.2, 0.3, 0.3, 0.2

NOTE: Probabilities were same as provided or were rescaled from provided frequencies
small effect size:  w = 0.1
medium effect size: w = 0.3
large effect size:  w = 0.5


     effect size of chi-squared test of goodness of fit 

              w = 0.5322906
             p1 = 0.1, 0.2, 0.3, 0.4
             p0 = 0.2, 0.3, 0.3, 0.2

NOTE: Probabilities were same as provided or were rescaled from provided frequencies
small effect size:  w = 0.1
medium effect size: w = 0.3
large effect size:  w = 0.5


     effect size of chi-squared test of goodness of fit 

              w = 0.5322906
             p1 = 0.1, 0.2, 0.3, 0.4
             p0 = 0.2, 0.3, 0.3, 0.2

NOTE: Probabilities were same as provided or were rescaled from provided frequencies
small effect size:  w = 0.1
medium effect size: w = 0.3
large effect size:  w = 0.5


     effect size of chi-squared test of goodness of fit 

              w = 0.5322906
             p1 = 0.1, 0.2, 0.3, 0.4
             p0 = 0.2, 0.3, 0.3, 0.2

NOTE: Probabilities were same as provided or were rescaled from provided frequencies
small effect size:  w = 0.1
medium effect size: w = 0.3
large effect size:  w = 0.5

powerAnalysis documentation built on May 2, 2019, 12:40 p.m.