Visualisation of the power of a Pearson correlation test
This fuction is useful when conducting power analyses for a Pearson correlation. It draws the sampling distribution of Pearson's r assuming a null hypothesis value of r and assuming a the hypothetical population value. The probability of making a Type 1 error is also illustrated.
showPearsonPower(n = 100, rho = 0.3, rNull = 0, distLabels = c("Null Hypothesis", "Population"), rhoColor = "green", rhoFill = "green", rhoAlpha = 0.1, rhoLineSize = 1, rNullColor = "blue", rNullFill = "blue", rNullAlpha = 0.1, rNullLineSize = 1, type2Color = "red", type2Fill = "red", type2Alpha = 0.1, type2LineSize = 0, theme = dlvTheme(), alpha = 0.05, digits = 3)
- The number of participants.
- The value of the correlation coefficient in the population.
- The value of the correlation coefficient according to the null hypothesis.
- Labels for the two distributions; the first one is the null hypothesis distribution, the second one the alternative distribution.
- rhoColor, rNullColor, type2Color
- The border colors of the distributions and the region used to illustrate the Type 2 error probability.
- rhoFill, rNullFill, type2Fill
- The fill colors of the distributions and the region used to illustrate the Type 2 error probability.
- rhoAlpha, rNullAlpha, type2Alpha
- The alpha (transparency) of the distributions and the region used to illustrate the Type 2 error probability.
- rhoLineSize, rNullLineSize, type2LineSize
- The line thicknesses of the distributions and the region used to illustrate the Type 2 error probability.
- The theme to use.
- The significance level (alpha) of the null hypothesis test.
- The number of digits to round to.
ggplotplot is returned.
## Not run: # showPearsonPower(); # ## End(Not run)