R: Build a ROC curve

roc {BDgraph}

R Documentation

Build a ROC curve

Description

This function builds a ROC curve specifically for graph structure learning and returns a “roc” object, a list of class “roc”. This object can be printed, plotted, or passed to the functions pROC::roc(), pROC::ci(), pROC::smooth.roc() and pROC::coords(). Additionally, two roc objects can be compared with pROC::roc.test(). This function is based on the roc function of R package pROC.

Usage

 
roc( pred, actual, auc = TRUE, smooth = FALSE, plot = FALSE, ... )

Arguments

`pred`	An adjacency matrix corresponding to an estimated graph. It can be an object with `S3` class `"bdgraph"` from function `bdgraph`. It can be an object of `S3` class `"ssgraph"`, from the function `ssgraph::ssgraph()` of `R` package `ssgraph::ssgraph()`. It can be a `numeric` or `ordered` vector of the same length than `actual`, containing the predicted value of each observation.
`actual`	An adjacency matrix corresponding to the actual graph structure in which a_{ij}=1 if there is a link between notes i and j, otherwise a_{ij}=0. It can be an object with `S3` class `"sim"` from function `bdgraph.sim`. It can be an object with `S3` class `"graph"` from function `graph.sim`. It can be a factor, numeric or character vector of responses (true class), typically encoded with 0 (controls) and 1 (cases). Only two classes can be used in a ROC curve.
`smooth`	if TRUE, the ROC curve is passed to `smooth` to be smoothed.
`auc`	compute the area under the curve (AUC)? If `TRUE` (default), additional arguments can be passed to `pROC::auc()`.
`plot`	plot the ROC curve? If `TRUE`, additional arguments can be passed to `pROC::plot.roc()`.
`...`	further arguments to be passed to `pROC::roc()`.

Value

If the data contained any NA value and na.rm=FALSE, NA is returned. Otherwise, if smooth=FALSE, a list of class “roc” with the following fields:

`auc`	if called with `auc=TRUE`, a numeric of class “auc” as defined in `pROC::auc()`.
`ci`	if called with `ci=TRUE`, a numeric of class “ci” as defined in `pROC::ci()`.
`response`	the response vector. Patients whose response is not `%in%` `levels` are discarded. If `NA` values were removed, a `na.action` attribute similar to `na.omit` stores the row numbers.
`predictor`	the predictor vector converted to numeric as used to build the ROC curve. Patients whose response is not `%in%` `levels` are discarded. If `NA` values were removed, a `na.action` attribute similar to `na.omit` stores the row numbers.
`original.predictor, original.response`	the response and predictor vectors as passed in argument.
`levels`	the levels of the response as defined in argument.
`controls`	the predictor values for the control observations.
`cases`	the predictor values for the cases.
`percent`	if the sensitivities, specificities and AUC are reported in percent, as defined in argument.
`direction`	the direction of the comparison, as defined in argument.
`fun.sesp`	the function used to compute sensitivities and specificities. Will be re-used in bootstrap operations.
`sensitivities`	the sensitivities defining the ROC curve.
`specificities`	the specificities defining the ROC curve.
`thresholds`	the thresholds at which the sensitivities and specificities were computed. See below for details.
`call`	how the function was called. See `match.call` for more details.

If smooth=TRUE a list of class “smooth.roc” as returned by pROC::smooth(), with or without additional elements auc and ci (according to the call).

Author(s)

Reza Mohammadi a.mohammadi@uva.nl

References

Tom Fawcett (2006) “An introduction to ROC analysis”. Pattern Recognition Letters 27, 861–874. DOI: doi: 10.1016/j.patrec.2005.10.010.

Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an open-source package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: doi: 10.1186/1471-2105-12-77.

Examples

## Not run: 
set.seed( 100 )

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )

# Running sampling algorithm based on GGMs 
sample.ggm <- bdgraph( data = data.sim, method = "ggm", iter = 10000 )

# ROC curve for GGM method
roc.ggm <- roc( pred = sample.ggm, actual = data.sim, plot = TRUE )

# Running sampling algorithm based on GCGMs
sample.gcgm <- bdgraph( data = data.sim, method = "gcgm", iter = 10000 )

# ROC curve for GGM and GCGM methods
roc.gcgm <- roc( pred = sample.gcgm, actual = data.sim, plot = TRUE )

ggroc( list( roc.ggm = roc.ggm, roc.gcgm = roc.gcgm ) )

## End(Not run)

[Package BDgraph version 2.64 Index]