Power.F | R Documentation |
This function calculates the power of an F-test, given the value of non-centrality parameter
Power.F(df1, df2, ncp, alpha, Figure = TRUE)
df1 |
the first degrees of freedom for the F-distribution |
df2 |
the second degrees of freedom for the F-distribution |
ncp |
a value of of the non-centality paramter |
alpha |
the level of significance |
Figure |
show graph if TRUE (default); No graph if FALSE |
See Kim and Choi (2020)
Power |
Power of the test |
Crit.val |
Critical value at alpha level of signifcance |
See Application Section and Appendix of Kim and Choi (2020)
Jae H. Kim
Kim and Choi, 2020, Choosing the Level of Significance: A Decision-theoretic Approach, Abacus, Wiley. <https://doi.org/10.1111/abac.12172>
Leamer, E. 1978, Specification Searches: Ad Hoc Inference with Nonexperimental Data, Wiley, New York.
Kim, JH and Ji, P. 2015, Significance Testing in Empirical Finance: A Critical Review and Assessment, Journal of Empirical Finance 34, 1-14. <DOI:http://dx.doi.org/10.1016/j.jempfin.2015.08.006>
Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician. <https://doi.org/10.1080/00031305.2020.1750484.>
data(data1) # Define Y and X y=data1$lnoutput; x=cbind(data1$lncapital,data1$lnlabor) # Restriction matrices to test for constant returns to scale Rmat=matrix(c(0,1,1),nrow=1); rvec=matrix(0.94,nrow=1) # Model Estimation and F-test M=R.OLS(y,x,Rmat,rvec) # Degrees of Freedom and estimate of non-centrality parameter K=ncol(x)+1; T=length(y) df1=nrow(Rmat);df2=T-K; NCP=M$ncp Power.F(df1,df2,ncp=NCP,alpha=0.20747,Figure=TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.