plotnPV2 {bdpv} | R Documentation |

## Plot experimental design for different settings in a set of sub figure.

### Description

The function creates a plot from the results of the function `nPV`

.

### Usage

```
plotnPV2(x, NPVlty = 1, PPVlty = 3, ...)
```

### Arguments

`x` |
an object of class |

`NPVlty` |
single integer value, the linetype for NPV sample size, see |

`PPVlty` |
single integer value, the linetype for PPV sample size, see |

`...` |
further arguments to be passed to |

### Details

Required sample sizes for different experimental settings and prevalences, needed to achieve a prespecified power can be calculated in dependence of the proportion of true negative and true positive compounds in the validation set, using function `nPV`

. This function draws a plot with the proportion of true positives on x and the total sample size on y, combining all parameter settings in one plot.

Note that for huge numbers of setting this should not work.

### Value

A plot.

### Author(s)

Frank Schaarschmidt

### References

*Steinberg DM, Fine J, Chappell R (2009).* Sample size for positive and negaitive predictiove value in diagnostic research using case-control designs. Biostatoistics 10, 1, 94-105.

### See Also

`plotnPV`

, for sample sizes for several settings in one figure

### Examples

```
TEST<-nPV(se=c(0.9, 0.92, 0.94, 0.96, 0.98), sp=c(0.98, 0.96, 0.94, 0.92, 0.90),
pr=0.12, NPV0=0.98, PPV0=0.4, NPVpower = 0.8, PPVpower = 0.8,
rangeP = c(0.05, 0.95), nsteps = 20, alpha = 0.05)
plotnPV2(TEST, log="x")
```

*bdpv*version 1.3 Index]