# NOT RUN {
Cor.Mat <- rbind(
c(1.00, 0.53, 0.58, 0.60, 0.46, 0.66),
c(0.53, 1.00, 0.35, 0.07, 0.14, 0.43),
c(0.58, 0.35, 1.00, 0.18, 0.29, 0.50),
c(0.60, 0.07, 0.18, 1.00, 0.30, 0.26),
c(0.46, 0.14, 0.29, 0.30, 1.00, 0.30),
c(0.66, 0.43, 0.50, 0.26, 0.30, 1.00))
RHO.XX <- Cor.Mat[2:6,2:6]
Rho.YX <- Cor.Mat[1,2:6]
# Method 1
# ss.power.reg.coef(Rho2.Y_X=0.7826786, Rho2.Y_X.without.j=0.7363697, p=5,
# alpha.level=.05, Directional=FALSE, desired.power=.80)
# Method 2
# ss.power.reg.coef(alpha.level=.05, RHO.XX=RHO.XX, Rho.YX=Rho.YX,
# which.predictor=5,
# Directional=FALSE, desired.power=.80)
# Method 3
# Here, beta.j is the standardized regression coefficient. Had beta.j
# been the unstandardized regression coefficient, sigma.X and sigma.Y
# would have been the standard deviation for the
# X variable of interest and Y, respectively.
# ss.power.reg.coef(Rho2.Y_X=0.7826786, Rho2.j_X.without.j=0.3652136,
# beta.j=0.2700964,
# p=5, alpha.level=.05, sigma.X=1, sigma.Y=1, Directional=FALSE,
# desired.power=.80)
# Method 4
# ss.power.reg.coef(alpha.level=.05, Cohen.f2=0.2130898, p=5,
# Directional=FALSE,
# desired.power=.80)
# Power given a specified N and squared multiple correlation coefficients.
# ss.power.reg.coef(Rho2.Y_X=0.7826786, Rho2.Y_X.without.j=0.7363697,
# Specified.N=25,
# p=5, alpha.level=.05, Directional=FALSE)
# Power given a specified N and effect size.
# ss.power.reg.coef(alpha.level=.05, Cohen.f2=0.2130898, p=5, Specified.N=25,
# Directional=FALSE)
# Reproducing Maxwell's (2000, p. 445) Example
Cor.Mat.Maxwell <- rbind(
c(1.00, 0.35, 0.20, 0.20, 0.20, 0.20),
c(0.35, 1.00, 0.40, 0.40, 0.40, 0.40),
c(0.20, 0.40, 1.00, 0.45, 0.45, 0.45),
c(0.20, 0.40, 0.45, 1.00, 0.45, 0.45),
c(0.20, 0.40, 0.45, 0.45, 1.00, 0.45),
c(0.20, 0.40, 0.45, 0.45, 0.45, 1.00))
RHO.XX.Maxwell <- Cor.Mat.Maxwell[2:6,2:6]
Rho.YX.Maxwell <- Cor.Mat.Maxwell[1,2:6]
R2.Maxwell <- Rho.YX.Maxwell
# }
# NOT RUN {
<!-- %*%solve(RHO.XX.Maxwell)%*%Rho.YX.Maxwell -->
# }
# NOT RUN {
RHO.XX.Maxwell.no.1 <- Cor.Mat.Maxwell[3:6,3:6]
Rho.YX.Maxwell.no.1 <- Cor.Mat.Maxwell[1,3:6]
R2.Maxwell.no.1 <-
Rho.YX.Maxwell.no.1
# }
# NOT RUN {
<!-- %*%solve(RHO.XX.Maxwell.no.1)%*%Rho.YX.Maxwell.no.1 -->
# }
# NOT RUN {
# Note that Maxwell arrives at N=113, whereas this procedure arrives at 111.
# This seems to be the case becuase of rounding error in calculations
# in Cohen (1988)'s tables. The present procedure is correct and contains no
# rounding error
# in the application of the method.
# ss.power.reg.coef(Rho2.Y_X=R2.Maxwell,
# Rho2.Y_X.without.j=R2.Maxwell.no.1, p=5,
# alpha.level=.05, Directional=FALSE, desired.power=.80)
# }
Run the code above in your browser using DataCamp Workspace