# plot.sim.seqtest.cor: Plot sim.seqtest In miscor: Miscellaneous Functions for the Correlation Coefficient

## Description

This function plots the `sim.seqtest.cor` object

## Usage

 ```1 2 3``` ```## S3 method for class 'sim.seqtest.cor' plot(x, plot.lines = TRUE, plot.nom = TRUE, ylim = NULL, type = "b", pch = 19, lty = 1, lwd = 1, ...) ```

## Arguments

 `x` `sim.seqtest.cor` object. `plot.lines` plot lines connecting points withe the x- and y-axis. `plot.nom` plot line at the nominal alpha. `ylim` the y limits of the plot. `type` what type of plot should be drawn (`"p"` for points, `"l"` for lines and `"b"` for both). `pch` plotting character. `lty` line type. `lwd` line width. `...` further arguments passed to or from other methods.

## Author(s)

Takuya Yanagida takuya.yanagida@univie.ac.at

## References

Schneider, B., Rasch, D., Kubinger, K. D., & Yanagida, T. (2015). A Sequential triangular test of a correlation coefficient's null-hypothesis: 0 < ρ ≤ ρ0. Statistical Papers, 56, 689-699.

`sim.seqtest.cor`, `seqtest.cor`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## Not run: #--------------------------------------------- # Determine optimal k and nominal type-II-risk # H0: rho <= 0.3, H1: rho > 0.3 # alpha = 0.01, beta = 0.05, delta = 0.25 # Step 1: Determine the optimal size of subsamples (k) sim.obj.1 <- sim.seqtest.cor(rho.sim = 0.3, k = seq(4, 16, by = 1), rho = 0.3, alternative = "greater", delta = 0.25, alpha = 0.05, beta = 0.05, runs = 10000) plot(sim.obj.1) # Step 2: Determine the optimal nominal type-II-risk based on # the optimal size of subsamples (k) from step 1 sim.obj.2 <- sim.seqtest.cor(rho.sim = 0.55, k = 16, rho = 0.3, alternative = "greater", delta = 0.25, alpha = 0.05, beta = seq(0.05, 0.15, by = 0.01), runs = 10000) plot(sim.obj.2) ## End(Not run) ```