plotCV {ramwas} | R Documentation

## Plotting Functions used in Cross Validation Analysis (Methylation Risk Score).

### Description

The function `plotPrediction` plots cross validation predictions of a phenotype against true values of the phenotype with multiple summary stats in the title.

The function `plotCVcors` plots the predictive power (correlations) across predictions using various numbers of markers.

The function `plotROC` plots an ROC (Receiver operating characteristic) curve for predictions of a binary outcome.

### Usage

    plotPrediction(
            param,
            outcome,
            forecast,
            cpgs2use,
            main,
            dfFull = NULL)

    plotCVcors(cl, param)

    plotROC(outcome, forecast)

### Arguments

`param` | List of parameters as described in the "RW6_param.Rmd" vignette. Try: `vignette("RW6_param","ramwas")`. Only `modeloutcome`, `cvnfolds` and `mmalpha` elements are used.
`outcome` | Values of a phenotype. Must be binary for `plotROC`.
`forecast` | Predictions for the phenotype.
`cpgs2use` | Number of variables used for prediction (for the legend).
`main` | Part of the title (summary stats are added beneath).
`dfFull` | Number of degrees of freedom for the significance testing. Default is: `length(forecast) - 2`
`cl` | List with three elements: `x` - vector with the number of variables used for prediction `corp` - Pearson correlations between the predictions and the true value of the phenotype. `cors` - Spearman correlations between the predictions and the true value of the phenotype.

### Details

The `plotROC` and plot has no title.
To add a title use `title`.

### Value

The `plotROC` returns the area under the curve (AUC) for the ROC.
The `plotPrediction` function returns the list of calculated statistics printed in the title.
The `plotCVcors` returns nothing (`NULL`).

### Author(s)

Andrey A Shabalin andrey.shabalin@gmail.com

### See Also

See vignettes: `browseVignettes("ramwas")`.

### Examples

    # Sample data
    n = 300
    param = list(modeloutcome = "Age", mmalpha = 0, cvnfolds = 5)
    outcome = rnorm(n, mean = 50, sd = 20)
    forecast = outcome + rnorm(n, mean = 0, sd = 20)
    cpgs2use = 1000
    main = "Prediction success (simulated data)"

    # Plot phenotype-prediction plot
    plotPrediction(
            param,
            outcome,
            forecast,
            cpgs2use,
            main)

    # Artificial data for plotCVcors()
    cl = list(
        x =    c(50,  100, 200, 500,  1000),
        corp = c(0.1, 0.6, 0.7, 0.85, 0.8),
        cors = c(0.1, 0.6, 0.7, 0.85, 0.8) + rnorm(5)*0.1)

    # Plot prediction performance by the number of markers
    plotCVcors(cl, param)

    # Make the outcome binary for ROC plot
    outcome = (outcome > 50)

    # Plot ROC curve and calculate the AUC
    plotROC(outcome, forecast)

[Package ramwas version 1.10.5 Index]