# Computation of degrees of freedom. This function computes the degrees of freedom. ```r df.compute(nE, nC, SigmaE = NULL, SigmaC = NULL, matrix.type = NULL, equalSigmas = NULL, m = NULL) ``` ## Arguments - `nE`: Sample size for the experimental (test) group. - `nC`: Sample size for the control group. - `SigmaE`: NULL or a matrix indicating the covariances between the primary endpoints in the experimental (test) group. See Details. - `SigmaC`: NULL or a matrix indicating the covariances between the primary endpoints in the control group. See Details. - `matrix.type`: NULL or an integer among 1, 2, 3, 4, giving the type of the matrices 'SigmaE' and 'SigmaC'. See Details. - `equalSigmas`: NULL or a logical indicating if 'SigmaC' and 'SigmaE' are equal. See Details. - `m`: NULL or the value for `m`. See Details. ## Details You should provide either both `SigmaE`, `SigmaC` or both `matrix.type`, `equalSigmas`. When you provide the former, the latter should be set to NULL. And vice versa. ## Returns - **df**: The degrees of freedom. ## References Delorme P., Lafaye de Micheaux P., Liquet B., Riou, J. (2015). Type-II Generalized Family-Wise Error Rate Formulas with Application to Sample Size Determination. **Statistics in Medicine.** Romano J. and Shaikh A. (2006) Stepup Procedures For Control of Generalizations of the Familywise Error Rate. **The Annals of Statistics**, 34(4), 1850--1873. ## Author(s) P. Lafaye de Micheaux, B. Liquet and J. Riou ## See Also `global.1m.analysis`, `indiv.1m.ssc`, `indiv.1m.analysis`, `global.1m.ssc` ## Examples ```r ## Not run: # standard deviation of the treatment effect var <- c(0.3520^2,0.6219^2,0.5427^2,0.6075^2,0.6277^2,0.5527^2,0.8066^2) # Correlation matrix cov <- matrix(1,ncol=7,nrow=7) cov[1,2:7] <- cov[2:7,1] <- c(0.1341692,0.1373891,0.07480123,0.1401267,0.1280336,0.1614103) cov[2,3:7] <- cov[3:7,2] <- c(0.2874531,0.18451960,0.3156895,0.2954996,0.3963837) cov[3,4:7] <- cov[4:7,3] <- c(0.19903400,0.2736123,0.2369907,0.3423579) cov[4,5:7] <- cov[5:7,4] <- c(0.1915028,0.1558958,0.2376056) cov[5,6:7] <- cov[6:7,5] <- c(0.2642217,0.3969920) cov[6,7] <- cov[7,6] <- c(0.3352029) # Covariance matrix diag(cov) <- var df.compute(SigmaE = cov, SigmaC = cov, nE = 20, nC = 30) ## End(Not run) ```

df.compute function