Package 'movieROC'

Title:	Visualizing the Decision Rules Underlying Binary Classification
Description:	Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC() function); - transforming the marker by a proper function trying to improve the classification performance (hROC() function); - when dealing with multivariate markers, considering a proper transformation to univariate space trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function). The classification regions behind each point of the ROC curve are displayed in both static graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or videos (movieROC() function).
Authors:	Sonia Perez-Fernandez [aut, cre]
Maintainer:	Sonia Perez-Fernandez <[email protected]>
License:	GPL-3
Version:	0.1.1
Built:	2024-09-27 06:19:02 UTC
Source:	CRAN

Help Index

Build a ROC curve for a univariate marker
Build a binormal ROC curve for a univariate marker
Hepatocellular carcinoma data
Build a ROC curve for a transformation of a univariate marker
Create a video with the building procedure of the ROC curve
Create a video with the building procedure of the smooth ROC curve estimate
Build a ROC curve for a multivariate marker with dimension p
Plot an ROC curve
Plot the building procedure of the ROC curve
Plot density function estimates for controls and cases
Plot standard smooth ROC curve estimate
Plot the transformation function used for the marker
Plot the classification regions underlying a ROC curve
Predict the classification regions for a particular specificity
Print an ROC curve object

Build a ROC curve for a univariate marker

Description

This is one of the main functions of the movieROC package. It builds a univariate ROC curve (standard or general) and returns a ‘groc’ object, a list of class ‘groc’. This object can be printed, plotted, or predicted for a particular point. It may be also passed to other functions: plot_regions(), plot_buildROC(), movieROC(), plot_densities(), and plot_densityROC().

Usage

gROC(X, D, ...)
## Default S3 method:
gROC(X, D, side = c("right", "left", "both", "both2"),
    restric = FALSE, optim = TRUE, t0 = NULL, t0max = FALSE, verbose = FALSE, ...)
gROC(X, D, ...)
## Default S3 method:
gROC(X, D, side = c("right", "left", "both", "both2"),
    restric = FALSE, optim = TRUE, t0 = NULL, t0max = FALSE, verbose = FALSE, ...)

Arguments

`X`	Vector of marker values.
`D`	Vector of response values. Two levels; if more, the two first ones are used.
`side`	Type of ROC curve. One of `"right"` ( $\mathcal{R}_r(\cdot)$ ), `"left"` ( $\mathcal{R}_l(\cdot)$ ), `"both"` ( $\mathcal{R}_g(\cdot)$ ) or `"both2"` ( $\mathcal{R}_{g'}(\cdot)$ ). Default: `"right"`.
`restric`	If TRUE, the gROC curve with restriction (C) is computed. Default: FALSE.
`optim`	If TRUE (and `restric = TRUE`), the computation of the optimal gROC curve under restriction (C) is performed by using Floyd's algorithm (Floyd, 1962), implemented in the `allShortestPaths()` function in the e1071 package. Default: TRUE.
`t0`	An integer number between 1 and $m+1$ (where $m$ is the negative sample size). If `restric = TRUE`, the restricted gROC curve is computed departing from (`t0` $-1$ )/ $m$ . Default: the one reporting the Youden index.
`t0max`	If TRUE, the computation of the gROC curve under restriction (C) is performed departing from every possible `t0` and the one reporting the maximum AUC is selected.
`verbose`	If TRUE, a progress bar is displayed for computationally intensive methods. Default: FALSE.
`...`	Other parameters to be passed. Not used.

Details

This function's main job is to estimate an ROC curve for a univariate marker under one of these considerations: larger values of the marker are associated with a higher probability of being positive (resulting in the right-sided ROC curve, $\mathcal{R}_r (\cdot)$ ), the opposite (left-sided ROC curve, $\mathcal{R}_l (\cdot)$ ), when both smaller and larger values of the marker are associated with having more probability of being positive (gROC curve, $\mathcal{R}_g(\cdot)$ ), the opposite (opposite gROC curve, $\mathcal{R}_{g'}(\cdot)$ ).

Value

A list of class ‘groc’ with the following fields:

`controls`, `cases`	Marker values of negative and positive subjects, respectively.
`levels`	Levels of response values.
`side`	Type of ROC curve.
`t`	Vector of false-positive rates.
`roc`	Vector of values of the ROC curve for `t`.
`c`	Vector of marker thresholds resulting in (`t`, `roc`) if `side = "right" \| "left"`.
`xl`, `xu`	Vectors of marker thresholds resulting in (`t`, `roc`) if `side = "both" \| "both2"`.
`auc`	Area under the curve estimate.
`aucfree`	Area under the curve estimate without restrictions.
`aucs`	Area under the curve with restriction (C) departing from every false-positive rate, $FPR \in \{ 0, 1/m, \dots, 1 \}$ .

Dependencies

If side = "both" and optim = TRUE, the allShortesPaths() function in the e1071 package is used. Also the combinations() function in gtools and %[]% in intrval.

References

P. Martínez-Camblor, N. Corral, C. Rey, J. Pascual, and E. Cernuda-Morollón (2017) “Receiver operating characteristic curve generalization for non-monotone relationships”. Statistical Methods in Medical Research, 26(1):113–123. DOI: doi:10.1177/0962280214541095.

S. Pérez-Fernández, P. Martínez-Camblor, P. Filzmoser, and N. Corral (2021). “Visualizing the decision rules behind the ROC curves: understanding the classification process”. AStA Advances in Statistical Analysis, 105(1):135-161. DOI: doi:10.1007/s10182-020-00385-2.

R. W. Floyd (1962) “Algorithm 97: Shortest path”. Communications of the ACM, 5: 345–345. DOI: doi:10.1145/367766.368168.

Examples

data(HCC)

# ROC curve estimates for gene 03515901 and response tumor
gROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Standard right-sided ROC curve
gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided ROC curve
gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "both") # gROC curve without restrictions


### Warning: Next line of code is time consuming. gROC curve with restriction (C)
gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "both", restric = TRUE)

data(HCC)

# ROC curve estimates for gene 03515901 and response tumor
gROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Standard right-sided ROC curve
gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided ROC curve
gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "both") # gROC curve without restrictions


### Warning: Next line of code is time consuming. gROC curve with restriction (C)
gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "both", restric = TRUE)

Build a binormal ROC curve for a univariate marker

Description

This function builds a univariate ROC curve (standard or general) assuming the binormal scenario with parameters being the sample estimates. It returns a ‘groc’ object, a list of class ‘groc’.

Usage

gROC_param(X, D, side = c("right", "left", "both", "both2"), N = NULL, ...)
gROC_param(X, D, side = c("right", "left", "both", "both2"), N = NULL, ...)

Arguments

`X`	Vector of marker values.
`D`	Vector of response values. Two levels; if more, the two first ones are used.
`side`	Type of ROC curve. One of `"right"` ( $\mathcal{R}_r(\cdot)$ ), `"left"` ( $\mathcal{R}_l(\cdot)$ ), `"both"` ( $\mathcal{R}_g(\cdot)$ ) or `"both2"` ( $\mathcal{R}_{g'}(\cdot)$ ). Default: `"right"`.
`N`	Number indicating the length of the vector of FPR considered to build the ROC curve: $t \in \{ 0, 1/N, 2/N, \dots, 1 \}$ . Default: `1000`.
`...`	Other parameters to be passed. Not used.

Details

Value

A list of class ‘groc’ with the following fields:

`controls`, `cases`	Marker values of negative and positive subjects, respectively.
`levels`	Levels of response values.
`side`	Type of ROC curve.
`t`	Vector of false-positive rates.
`roc`	Vector of values of the ROC curve for `t`.
`c`	Vector of marker thresholds resulting in (`t`, `roc`) if `side = "right" \| "left"`.
`xl`, `xu`	Vectors of marker thresholds resulting in (`t`, `roc`) if `side = "both" \| "both2"`.
`auc`	Area under the curve estimate.
`a`, `b`	Estimates for parameters $a$ and $b$ considered for the ROC curve estimation: $\hat{a} = \left[ \overline{\xi_n} - \overline{\chi_m} \right]/\hat{s}_\xi$ and $\hat{b} = \hat{s}_\chi / \hat{s}_\xi$ .
`p0`	Estimate of the "central value", $\mu^*$ , about to which the thresholds $x^L$ and $x^U$ are symmetrical.

References

P. Martínez-Camblor and J. C. Pardo-Fernández (2019) “Parametric estimates for the receiver operating characteristic curve generalization for non-monotone relationships”. Statistical Methods in Medical Research, 28(7): 2032–2048. DOI: doi:10.1177/0962280217747009.

Examples

data(HCC)

# ROC curve estimates for gene 03515901 and response tumor assuming the binormal scenario
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor) # Standard right-sided ROC curve
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided ROC curve 
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor, side = "both") # gROC curve 
data(HCC)

# ROC curve estimates for gene 03515901 and response tumor assuming the binormal scenario
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor) # Standard right-sided ROC curve
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided ROC curve 
gROC_param(X = HCC[,"cg03515901"], D = HCC$tumor, side = "both") # gROC curve

Hepatocellular carcinoma data

Description

This dataset is derived from gene expression arrays of tumor and adjacent non-tumor tissues of 62 Taiwanese cases of hepatocellular carcinoma. The complete dataset was deposited in NCBI’s Gene Expression Omnibus (GEO) and it is available through series accession number GSE37988. This dataset contains 948 from the 27,578 autosomal CpG sites screened.

Usage

data("HCC")data("HCC")

Format

A data frame with 124 observations on 952 variables. First 4 variables are tissue (identification number for the tissue; from 1 to 62), sex (female or male), age (age in years of the patient), and tumor (status of the tissue; nontumor or tumor). The following 948 from cg03409548 to cg20240860 are numeric variables containing the relative gene expression intensities of the corresponding gene.

Source

NCBI’s Gene Expression Omnibus (GEO) - Series accession number GSE37988 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37988)

Examples

data(HCC)
str(HCC) # Data structure
table(HCC$tumor) # Number of non-tumor and tumor tissues

# Histograms of gene 18384097 expression intensities for non-tumor and tumor tissues
oldpar <- par(mfrow = c(2,1))
x <- subset(HCC, tumor == "nontumor")$cg18384097
y <- subset(HCC, tumor == "tumor")$cg18384097
hist(x, xlim = c(0,1), main = "Gene 18384097 in non-tumor tissues")
hist(y, xlim = c(0,1), main = "Gene 18384097 in tumor tissues")
par(oldpar)
data(HCC)
str(HCC) # Data structure
table(HCC$tumor) # Number of non-tumor and tumor tissues

# Histograms of gene 18384097 expression intensities for non-tumor and tumor tissues
oldpar <- par(mfrow = c(2,1))
x <- subset(HCC, tumor == "nontumor")$cg18384097
y <- subset(HCC, tumor == "tumor")$cg18384097
hist(x, xlim = c(0,1), main = "Gene 18384097 in non-tumor tissues")
hist(y, xlim = c(0,1), main = "Gene 18384097 in tumor tissues")
par(oldpar)

Build a ROC curve for a transformation of a univariate marker

Description

This is one of the main functions of the movieROC package. It builds a univariate ROC curve for a transformed marker $h(X)$ and returns a ‘hroc’ object, a list of class ‘hroc’. This object can be printed, plotted, or predicted for a particular point. It may be also passed to plot_funregions() and plot_regions() functions.

Usage

hROC(X, D, ...)
## Default S3 method:
hROC(X, D, type = c("lrm", "overfitting", "kernel", "h.fun"), 
    formula.lrm = "D ~ pol(X,3)", h.fun = function(x) {x},  kernel.h = 1,
    plot.h = FALSE, plot.roc = FALSE, new.window = FALSE, 
    main = NULL, xlab = "x", ylab = "h(x)", xaxis = TRUE, ...)
hROC(X, D, ...)
## Default S3 method:
hROC(X, D, type = c("lrm", "overfitting", "kernel", "h.fun"), 
    formula.lrm = "D ~ pol(X,3)", h.fun = function(x) {x},  kernel.h = 1,
    plot.h = FALSE, plot.roc = FALSE, new.window = FALSE, 
    main = NULL, xlab = "x", ylab = "h(x)", xaxis = TRUE, ...)

Arguments

`X`	Vector of marker values.
`D`	Vector of response values. Two levels; if more, the two first ones are used.
`type`	Type of transformation considered. One of `"lrm"` (a binary logistic regression is computed by using `lrm()` function in rms package), `"kernel"` (the transformation included in Martínez-Camblor et al. (2021) estimated by the kernel density approach), `"overfitting"` (the overfitting transformation, $\hat{h}_{of}(\cdot)$ is taken), or `"h.fun"` (the transformation indicated in the input parameter `h.fun` is considered). Default: `"lrm"`.
`formula.lrm`	If `type = "lrm"`, the transformation employed in the right-hand side of the logistic regression model (in terms of `X` and `D`). Default: `'D ~ pol(X, 3)'`.
`kernel.h`	If `type = "kernel"`, the bandwidth used for the kernel density estimation by using the `density()` function in stats package. Default: 1.
`h.fun`	If `type = "h.fun"`, the transformation employed (as a function in R). Default: `function(x){x}`.
`plot.h`	If TRUE, the transformation employed is illustrated.
`plot.roc`	If TRUE, the resulting ROC curve is illustrated.
`new.window`	If TRUE, two previous graphics are plotted separately in different windows.
`main`	A main title for the plot used if `plot.h = TRUE`.
`xlab`, `ylab`	A label for the x and y axis of the plot used if `plot.h = TRUE`.
`xaxis`	Graphical parameter used if `plot.h = TRUE`. If FALSE, plotting of the axis is supressed.
`...`	Other parameters to be passed. Not used.

Details

A theoretical and practical discussion about the type of transformation considered and its basis may be found in Martínez-Camblor et al. (2019) and Martínez-Camblor et al. (2021).

The overfitting function estimate is defined as follows:

$\hat{h}_{of}(x) = \sum_{i=1}^{n_1} I(x = y_i) + \sum_{i=1}^{n_2} \dfrac{\#(\xi = z_i)}{\#(\xi = z_i) + \#(\chi = z_i)} I(x = z_i)$

where $I(A)$ denotes the indicator function (which takes the value 1 if $A$ is true and 0 otherwise), $\#(B)$ is the cardinal of the subset $B$ , $\{y_1, \dots, y_{n_1}\} \subseteq \left\{ \xi_1, \dots, \xi_n \right\}$ are the positive sample values without ties and $\{z_1, \dots, z_{n_2}\} \subseteq \left\{ \xi_1, \dots, \xi_n \right\}$ are the positive sample values with ties with any negative sample value. Classification based on this transformation is the optimal one in the AUC sense, but the resulting decision rules cannot be extended to any other sample.

Value

A list of class ‘hroc’ with the following fields:

`levels`	Levels of response values.
`X`, `Y`	Original and transformed marker values, respectively.
`Sp`, `Se`	Vector of true-negtive and true-positive rates, respectively.
`auc`	Area under the curve estimate.
`model`	If `type = "lrm"`, the coefficients of the logistic regression model fitted by `formula`.

Dependencies

If type = "lrm", the lrm() function in the rms package is used. This library is also loaded to consider special transformation functions such as pol() and rcs().

References

P. Martínez-Camblor, S. Pérez-Fernández, and S. Díaz-Coto (2019) “Improving the biomarker diagnostic capacity via functional transformations”. Journal of Applied Statistics, 46(9): 1550–1566. DOI: doi:10.1080/02664763.2018.1554628.

P. Martínez-Camblor, S. Pérez-Fernández, and S. Díaz-Coto (2021) “Optimal classification scores based on multivariate marker transformations”. AStA Advances in Statistical Analysis, 105(4): 581–599. DOI: doi:10.1007/s10182-020-00388-z.

Examples

data(HCC)

# ROC curve for gene 18384097 to identify tumor by considering 4  different transformations:
X <- HCC$cg18384097; D <- HCC$tumor
## 1. Ordinary cubic polynomial formula for binary logistic regression
hROC(X, D)
## 2. Linear tail-restricted cubic splines for binary logistic regression
hROC(X, D, formula.lrm = "D ~ rcs(X,8)")
## 3. Overfitting transformation for this particular sample
hROC(X, D, type = "overfitting")
## 4. Optimal transformation in terms of likelihood ratio 
##    by kernel density estimation with bandwidth 3
hROC(X, D, type = "kernel", kernel.h = 3)
data(HCC)

# ROC curve for gene 18384097 to identify tumor by considering 4  different transformations:
X <- HCC$cg18384097; D <- HCC$tumor
## 1. Ordinary cubic polynomial formula for binary logistic regression
hROC(X, D)
## 2. Linear tail-restricted cubic splines for binary logistic regression
hROC(X, D, formula.lrm = "D ~ rcs(X,8)")
## 3. Overfitting transformation for this particular sample
hROC(X, D, type = "overfitting")
## 4. Optimal transformation in terms of likelihood ratio 
##    by kernel density estimation with bandwidth 3
hROC(X, D, type = "kernel", kernel.h = 3)

Create a video with the building procedure of the ROC curve

Description

Save a video as a GIF illustrating the construction of the ROC curve. Particularly, for each element in vector fpr (optional input parameter), the function executed is

- for ‘groc’ objects:

plot_buildROC(obj, FPR = fpr[i], build.process = TRUE,...);

- for ‘multiroc’ objects:

plot_buildROC(obj, FPR = fpr[i], build.process = TRUE, display.method, displayOV,...)

Usage

movieROC(x, ...)
## S3 method for class 'groc'
movieROC(x, fpr = NULL, h = c(1,1), histogram = FALSE, breaks = 15,
  reduce = TRUE, completeROC = FALSE, videobar = TRUE, file = "animation1.gif",
  save = TRUE, legends = FALSE, speedcorrection = FALSE, tpause = 1, interval = 0.2,
  ani.width, ani.height, xlab = "Marker", main.density = "Density functions",
  cex.lab = 2.5, cex.axis = 1.75, cex.main = 2.25 + as.numeric(reduce),
  xlim = NULL, ylim = NULL, cex.point = 1.5, lwd.curve = 2, mar = NULL,
  lim.density = 0.01, col.controlscases = c("#485C99", "#8F3D52"),
  col.curve = "black", col.threshold = "#FCBA04", verbose = FALSE, ...)
## S3 method for class 'multiroc'
movieROC(x, fpr = NULL, display.method = c("PCA", "OV"),
  displayOV = c(1,2), border = TRUE, completeROC = FALSE, videobar = TRUE,
  file = "animation1.gif", save = TRUE, legends = FALSE, tpause = 1,
  interval = 0.2, ani.width, ani.height, xlab = NULL, ylab = NULL,
  cex = 0.8, cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, alpha.points = 1,
  alpha.contour = 0.25, lwd.curve = 2, lty.curve = 1, lf = NULL,
  col.controlscases = c('#485C99','#8F3D52'), col.curve = 'black',
  col.threshold = '#FCBA04', verbose = FALSE, ...)
movieROC(x, ...)
## S3 method for class 'groc'
movieROC(x, fpr = NULL, h = c(1,1), histogram = FALSE, breaks = 15,
  reduce = TRUE, completeROC = FALSE, videobar = TRUE, file = "animation1.gif",
  save = TRUE, legends = FALSE, speedcorrection = FALSE, tpause = 1, interval = 0.2,
  ani.width, ani.height, xlab = "Marker", main.density = "Density functions",
  cex.lab = 2.5, cex.axis = 1.75, cex.main = 2.25 + as.numeric(reduce),
  xlim = NULL, ylim = NULL, cex.point = 1.5, lwd.curve = 2, mar = NULL,
  lim.density = 0.01, col.controlscases = c("#485C99", "#8F3D52"),
  col.curve = "black", col.threshold = "#FCBA04", verbose = FALSE, ...)
## S3 method for class 'multiroc'
movieROC(x, fpr = NULL, display.method = c("PCA", "OV"),
  displayOV = c(1,2), border = TRUE, completeROC = FALSE, videobar = TRUE,
  file = "animation1.gif", save = TRUE, legends = FALSE, tpause = 1,
  interval = 0.2, ani.width, ani.height, xlab = NULL, ylab = NULL,
  cex = 0.8, cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, alpha.points = 1,
  alpha.contour = 0.25, lwd.curve = 2, lty.curve = 1, lf = NULL,
  col.controlscases = c('#485C99','#8F3D52'), col.curve = 'black',
  col.threshold = '#FCBA04', verbose = FALSE, ...)

Arguments

`x`	An ROC curve object from the movieROC package. Possible classes are: ‘groc’ (output of `gROC()` function) and ‘multiroc’ (output of `multiROC()` function).
`fpr`	A vector of false-positive rates for which the building process of the ROC curve is displayed. Default: if length of `x$t` is lower than 150, such vector is taken as `fpr`; otherwise, an equally-space vector of length 100 covering the range of the marker is considered.
`h`	A vector of length 2 with the bandwidth used to compute kernel density estimation for controls and cases, respectively. See `adjust` parameter in `density()` function from stats package. Default: `h = c(1,1)`.
`histogram`	If TRUE, histograms are displayed on the left instead of kernel density estimates. Default: FALSE. Only available for a ‘groc’ object.
`breaks`	If `histogram = TRUE`, number of breaks used for the histograms. Default: 15.
`reduce`	If FALSE, two extra graphics are displayed at the bottom (see Details for more information). Default: TRUE. Only available for a ‘groc’ object.
`completeROC`	A logical value indicating if the whole ROC curve should be displayed in light gray or not. Default: FALSE.
`videobar`	If TRUE, a text progress bar is shown in the R console. Default: TRUE.
`verbose`	If TRUE, a progress bar is displayed for computationally intensive methods. Default: FALSE.
`file`	File name of the movie (with the extension). Default: `"animation1.gif"`.
`save`	If TRUE, video is saved as a GIF by using the `saveGIF` function in animation package. Default: TRUE.
`legends`	If TRUE, a legend with colors meaning is displayed on the left graphic and the AUC is shown on the right graphic. Default: FALSE.
`speedcorrection`	If TRUE, only some FPR in `fpr` vector are considered to make the video faster. Default: FALSE. Only available for a ‘groc’ object.
`tpause`	If `save = FALSE`, time interval to suspend execution for, in seconds. Default: 1.
`interval`	If `save = TRUE`, a positive number to set the time interval of the animation (unit in seconds) in animation package. Default: 0.2.
`ani.width`, `ani.height`	If `save = TRUE`, width and height of image frames (unit in px) in animation package.
`xlab`, `ylab`	Label for x- and y-axis on the left plot. `ylab` not available for object of class ‘groc’.
`main.density`	Title for the left plot. Only available for a ‘groc’ object.
`cex.lab`, `cex.axis`, `cex.main`	The magnification to be used for labels, axis annotation and main titles, respectively, relative to the current setting of `cex`.
`cex`	A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. Default: 0.8. Not available for object of class ‘groc’ (`cex=1` in this case).
`cex.point`	The magnification to be used for the particular point on the ROC curve and its text, relative to the current setting of `cex`. Default: `cex.point = 1.5`. Only available for a ‘groc’ object.
`xlim`, `ylim`	Range for x- and y-axis on the left plot. Only available for a ‘groc’ object.
`lim.density`	If `xlim` is `NULL`, lower limit for the density estimate to compute the range for the x-axis on the left plot. Default: 0.01. Only available for a ‘groc’ object.
`lty.curve`, `lwd.curve`, `col.curve`	The line type, width and color for ROC curve. Default: `lty.curve = 1` (solid), `lwd.curve = 2` and `col.curve = "black"`.
`mar`	A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. Only available for a ‘groc’ object.
`col.controlscases`	Vector of length 2 with the color used to control and case group, respectively. Default: `c('#485C99','#8F3D52')`.
`col.threshold`	Color for the chosen point. Default: `"FCBA04"`.
`border`	If TRUE, a border for the classification subsets is drawn. Default: TRUE. Not available for object of class ‘groc’.
`alpha.points`, `alpha.contour`	Number in [0,1] modifying the opacity alpha of the color for the points and classification region. The `adjustcolor` function in grDevices package is used. Default: `alpha.points = 1, alpha.contour = 0.25`. Not available for object of class ‘groc’.
`lf`	Epsilon value for steps. Not used.
`display.method`	Method to display the marker values from a ‘multiroc’ object on the left plot. Methods available: `"OV"` (projected over two selected components of the marker indicated in `displayOV`), or `"PCA"` (projected over the two principal components from a Principal Component Analysis). Default: `"PCA"`. Only available for object of class ‘multiroc’.
`displayOV`	If `display.method = "OV"`, the two components of the marker used to project the marker values on the left plot. Default: `c(1,2)` (two first components). Only available for object of class ‘multiroc’.
`...`	Other parameters to be passed to the `saveGIF()` function in animation package.

Value

A video with the building procedure of the ROC curve estimate with the selected graphical parameters

Dependencies

If save = TRUE (by default), the saveGIF() function in the animation package is used.

Examples

data(HCC)

# Standard ROC curve for gene 20202438
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor, side = "right")

### The video will be saved as a GIF with the name "video_cg20202438"
movieROC(roc_cg20202438, file = "video_cg20202438.gif", save = FALSE)

# Multivariate ROC curve for genes 0202438, 18384097, and 03515901
multiroc_PT <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901),
    D = HCC$tumor, method = "fixedLinear", methodLinear = "PepeThompson")

# Two first components from PCA:
### The video will be saved as a GIF with the name "video_multiroc_pca"
movieROC(multiroc_PT, file = "video_multiroc_pca.gif", save = FALSE)


### The video will be saved as a GIF with the name "video_multiroc_orig"
movieROC(multiroc_PT, display.method = "OV", displayOV = c(1,3),
    file = "video_multiroc_orig.gif", cex = 1.2, xlab = "Gene 20202438",
    ylab = "Gene 03515901", lwd.curve = 4, save = FALSE)
  
data(HCC)

# Standard ROC curve for gene 20202438
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor, side = "right")

### The video will be saved as a GIF with the name "video_cg20202438"
movieROC(roc_cg20202438, file = "video_cg20202438.gif", save = FALSE)

# Multivariate ROC curve for genes 0202438, 18384097, and 03515901
multiroc_PT <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901),
    D = HCC$tumor, method = "fixedLinear", methodLinear = "PepeThompson")

# Two first components from PCA:
### The video will be saved as a GIF with the name "video_multiroc_pca"
movieROC(multiroc_PT, file = "video_multiroc_pca.gif", save = FALSE)


### The video will be saved as a GIF with the name "video_multiroc_orig"
movieROC(multiroc_PT, display.method = "OV", displayOV = c(1,3),
    file = "video_multiroc_orig.gif", cex = 1.2, xlab = "Gene 20202438",
    ylab = "Gene 03515901", lwd.curve = 4, save = FALSE)

Create a video with the building procedure of the smooth ROC curve estimate

Description

This function tracks the construction of the standard ROC curve (right- or left-sided, depending on the side of the object of class ‘groc’ included) resulting from the kernel density function estimation for controls and cases. Four graphics are displayed: top-left, the kernel density estimates; top-right, the resulting ROC curve; bottom-left, boxplots and points for controls and cases and classification subset in gray color; bottom-right, classification subsets for every FPR until the current one. It makes use of the plot_densityROC() function for each screenshot.

Usage

movieROC2_densities(obj, h = c(1, 1), cut.off = NULL, completeROC = FALSE,
  legends = FALSE, videobar = TRUE, file = "animation1.gif", clean = FALSE,
  interval = 0.2, ani.width = 500, ani.height = 750, save = TRUE,
  tpause = 1, verbose = FALSE, ...)
movieROC2_densities(obj, h = c(1, 1), cut.off = NULL, completeROC = FALSE,
  legends = FALSE, videobar = TRUE, file = "animation1.gif", clean = FALSE,
  interval = 0.2, ani.width = 500, ani.height = 750, save = TRUE,
  tpause = 1, verbose = FALSE, ...)

Arguments

`obj`	An object of class ‘groc’ with `side = "right"` or `"left"`.
`h`	A vector of length 2 with the bandwidth used to compute kernel density estimation for controls and cases, respectively. See `adjust` parameter in `density()` function from stats package. Default: `h = c(1,1)`.
`cut.off`	Vector with marker cutoffs for which the graphics are displayed. Default: if number of unique marker values is lower than 150, these are considered; otherwise, a equally-spaced grid of length 102 in the range of the marker is used.
`save`	If TRUE, video is saved as a GIF by using the `saveGIF()` function in animation package. Default: TRUE.
`completeROC`	A logical value indicating if the whole ROC curve should be displayed in light gray or not. Default: FALSE.
`legends`	If TRUE, legends with the meaning of colors are displayed. Default: FALSE.
`videobar`	If TRUE, a text progress bar is shown in the R console. Default: TRUE.
`file`	File name of the movie (with the extension). Default: `"animation1.gif"`.
`clean`	Whether to delete the individual image frames in animation package. Default: FALSE.
`interval`	A positive number to set the time interval of the animation (unit in seconds) in animation package. Default: 0.2.
`ani.width`, `ani.height`	Width and height of image frames (unit in px) in animation package.
`tpause`	If `save = FALSE`, time interval to suspend execution for, in seconds. Default: 1.
`verbose`	If TRUE, a progress bar is displayed for computationally intensive methods. Default: FALSE.
`...`	Other parameters to be passed to the `saveGIF` function in animation package.

Value

A video with the building procedure of the smooth ROC curve estimate with the selected graphical parameters

Dependencies

If save = TRUE (by default), the saveGIF() function in the animation package is used.

Examples

data(HCC)

# Standard ROC curve for gene 20202438
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor)

### The video will be saved as a GIF with the name "video_cg20202438_smooth"
movieROC2_densities(roc_cg20202438, file = "video_cg20202438_smooth.gif", save = FALSE)
data(HCC)

# Standard ROC curve for gene 20202438
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor)

### The video will be saved as a GIF with the name "video_cg20202438_smooth"
movieROC2_densities(roc_cg20202438, file = "video_cg20202438_smooth.gif", save = FALSE)

Build a ROC curve for a multivariate marker with dimension $p$

Description

This is one of the main functions of the movieROC package. It builds a multivariate ROC curve by considering one of these methods: i) fitting a binary logistic regression model with a particular combination (fixed by the user) of the two components on the right-hand side, ii) linear combinations with fixed parameters, or iii) linear combinations with dynamic parameters, or iv) estimating optimal transformation based on kernel density estimation, or v) quadratic combinations with fixed parameters (if $p=2$ ). It returns a ‘multiroc’ object, a list of class ‘multiroc’. This object can be printed or plotted. It may be also passed to plot_buildROC() and movieROC() function.

Usage

multiROC(X, D, ...)
## Default S3 method:
multiROC(X, D, 
    method = c("lrm", "fixedLinear", "fixedQuadratic", "dynamicEmpirical", 
                "dynamicMeisner", "kernelOptimal"),
    formula.lrm = "D ~ X.1 + I(X.1^2) + X.2 + I(X.2^2) + I(X.1*X.2)",
    stepModel = TRUE, 
    methodLinear = c("coefLinear", "SuLiu", "PepeThompson", "logistic", "minmax"), 
    coefLinear = rep(1,ncol(X)), coefQuadratic = c(1,1,0,1,1), 
    K = 201, alpha = 0.5, approxh = 0.5, multiplier = 2, 
    kernelOptimal.H = c("Hbcv", "Hscv", "Hpi", "Hns", "Hlscv", "Hbcv.diag", 
                        "Hscv.diag", "Hpi.diag", "Hlscv.diag"), 
    eps = sqrt(.Machine$double.eps), verbose = FALSE, ...)
multiROC(X, D, ...)
## Default S3 method:
multiROC(X, D, 
    method = c("lrm", "fixedLinear", "fixedQuadratic", "dynamicEmpirical", 
                "dynamicMeisner", "kernelOptimal"),
    formula.lrm = "D ~ X.1 + I(X.1^2) + X.2 + I(X.2^2) + I(X.1*X.2)",
    stepModel = TRUE, 
    methodLinear = c("coefLinear", "SuLiu", "PepeThompson", "logistic", "minmax"), 
    coefLinear = rep(1,ncol(X)), coefQuadratic = c(1,1,0,1,1), 
    K = 201, alpha = 0.5, approxh = 0.5, multiplier = 2, 
    kernelOptimal.H = c("Hbcv", "Hscv", "Hpi", "Hns", "Hlscv", "Hbcv.diag", 
                        "Hscv.diag", "Hpi.diag", "Hlscv.diag"), 
    eps = sqrt(.Machine$double.eps), verbose = FALSE, ...)

Arguments

`X`	Matrix (dimension $n \times p$ ) of marker values where $n$ is the sample size and $p$ is the dimension of the multivariate marker.
`D`	Vector of response values. Two levels; if more, the two first ones are used.
`method`	Method used to build the classification regions. One of `"lrm"` (fitting a binary logistic regression model by the input parameter `formula`), `"fixedLinear"` (linear frontiers with fixed parameters given in `coefLinear` or estimated by the method in `methodLinear`), `"fixedQuadratic"` (quadratic frontiers with fixed parameters given in `coefQuadratic`, only available for $p=2$ ), `"dynamicMeisner"` (linear frontiers with dynamic parameters reported by Meisner et al. (2021) method), `"dynamicEmpirical"` (linear frontiers with dynamic parameters reported by the empirical method, only available for $p=2$ ), or `"kernelOptimal"` (estimating optimal transformation based on bivariate kernel density estimation by Martínez-Camblor et al. (2021) using the `kde()` function in the ks package). Default: `"lrm"`.
`formula.lrm`	If `method = "lrm"`, the transformation employed in the right-hand side of the logistic regression model (in terms of `X.1`, `X.2` `dots`, `X.p`, and `D`). Default: quadratic formula for the two first components `X.1` and `X.2`.
`stepModel`	If TRUE and `method = "lrm"`, a model selection is performed based on the AIC (Akaike information criterion) in a stepwise algorithm (see `step()` function in stats package for more information). Default: TRUE.
`methodLinear`	If `method = "fixedLinear"`, method used to build the classification regions. One of `"coefLinear"` (particular fixed coefficients in `coefLinear`), `"SuLiu"` (Su and Liu, 1993), `"PepeThompson"` (Pepe and Thompson, 2000), `"logistic"` (logistic regression model), `"minmax"` (Liu et al., 2011). Default: `"coefLinear"`.
`coefLinear`	If `method = "fixedLinear"` and `methodLinear = "coefLinear"`, a vector of length $p$ with the coefficients $\beta_i$ ( $i \in \{1, \dots, p\}$ ) used to $\mathcal{L}_{\boldsymbol{\beta}}(\boldsymbol{X}) = \beta_1 X_1 + \dots + \beta_p X_p$ . Default: $(1,\dots,1)$ .
`coefQuadratic`	If `method = "fixedQuadratic"`, a vector of length 5 with coefficients $\beta_1, \dots, \beta_5$ used to $\mathcal{Q}_{\boldsymbol{\beta}}(\boldsymbol{X}) = \beta_1 X_1 + \beta_2 X_2 + \beta_3 X_1 X_2 + \beta_4 X_1^2 + \beta_5 X_2^2$ . Default: $(1,1,0,1,1)$ .
`alpha`, `approxh`, `multiplier`	If `method = "dynamicMeisner"`, input parameters used in the `maxTPR()` function of the maxTPR package (internally integrated because the library is no longer available in CRAN). Default: `alpha = 0.5`, `approxh = 0.5` and `multiplier = 2`.
`K`	If `method = "dynamicEmpirical"`, the number of equally spaced $\alpha \in (-1,1)$ studied. Default: 201.
`kernelOptimal.H`	If `method = "kernelOptimal"`, the bandwidth matrix `H` used in the `kde()` function of the ks package. Default: `"Hbcv"` (biased cross-validation (BCV) bandwidth matrix selector for bivariate data) if $p = 2$ , `"Hpi"` (plug-in bandwidth selector) if $p > 2$ .
`eps`	Epsilon value to consider. Default: `sqrt(.Machine$double.eps)`.
`verbose`	If TRUE, a progress bar is displayed for computationally intensive methods. Default: FALSE.
`...`	Other parameters to be passed. Not used.

Value

A list of class ‘multiroc’ with the following fields:

`controls`, `cases`	Marker values of negative and positive subjects, respectively.
`levels`	Levels of response values.
`t`	Vector of false-positive rates.
`roc`	Vector of values of the ROC curve for `t`.
`auc`	Area under the curve estimate.
`Z`	If `method` $\neq$ `"dynamicMeisner"` and `method` $\neq$ `"dynamicEmpirical"`, resulting univariate marker values.
`c`	If `method` $\neq$ `"dynamicMeisner"` and `method` $\neq$ `"dynamicEmpirical"`, vector of final marker thresholds resulting in (`t`, `roc`).
`CoefTable`	If `method = "dynamicMeisner"` or `"dynamicEmpirical"`, a list of length equal to length of vector `t`. Each element of the list keeps the linear coefficients (`coef`), threshold for such linear combination (`c`), the corresponding point in the ROC curve (`t`, `roc`), the resulting univariate marker values (`Z`) and a matrix of dimension 100 $\times$ 100 with the marker values over a grid of ( $X_1$ , $X_2$ ) bivariate values (`f`).

Dependencies

If method = "lrm", the glm() function in the stats package is used.

If method = "kernelOptimal", the kde() function in the ks package is used.

References

J. Q. Su and J. S. Liu. (1993) “Linear combinations of multiple diagnostic markers”. Journal of the American Statistical Association, 88(424): 1350–1355. DOI: doi:10.1080/01621459.1993.10476417.

M. S. Pepe and M. L. Thompson (2000) “Combining diagnostic test results to increase accuracy”. Biostatistics, 1 (2):123–140. DOI: doi:10.1093/biostatistics/1.2.123.

C. Liu, A. Liu, and S. Halabi (2011) “A min–max combination of biomarkers to improve diagnostic accuracy”. Statistics in Medicine, 30(16): 2005–2014. DOI: doi:10.1002/sim.4238.

A. Meisner, M. Carone, M. S. Pepe, and K. F. Kerr (2021) “Combining biomarkers by maximizing the true positive rate for a fixed false positive rate”. Biometrical Journal, 63(6): 1223–1240. DOI: doi:10.1002/bimj.202000210.

Examples

data(HCC)

# ROC curve for genes 20202438 and 18384097 (p=2) to identify tumor by 4 different methods:
X <- cbind(HCC$cg20202438, HCC$cg18384097); D <- HCC$tumor
## 1. Linear combinations with fixed parameters by Pepe and Thompson (2000)
multiROC(X, D, method = "fixedLinear", methodLinear = "PepeThompson")
## 2.Linear combinations with dynamic parameters by Meisner et al. (2021)
### Time consuming
multiROC(X, D, method = "dynamicMeisner")
## 3. Logistic regression model with quadratic formula by default
multiROC(X, D)
## 4. Optimal transformation with multivariate KDE by Martínez-Camblor et al. (2021)
multiROC(X, D, method = "kernelOptimal")

# ROC curve for genes 20202438, 18384097, and 03515901 (p=3) to identify tumor
# by 4 different methods:
X <- cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901); D <- HCC$tumor
## 1. Linear combinations with fixed parameters by Pepe and Thompson (2000)
multiROC(X, D, method = "fixedLinear", methodLinear = "PepeThompson")
## 2.Linear combinations with dynamic parameters by Meisner et al. (2021)
### Time consuming
multiROC(X, D, method = "dynamicMeisner")
## 3. Logistic regression model with quadratic formula by default
multiROC(X, D)
## 4. Optimal transformation with multivariate KDE by Martínez-Camblor et al. (2021)
multiROC(X, D, method = "kernelOptimal")
data(HCC)

# ROC curve for genes 20202438 and 18384097 (p=2) to identify tumor by 4 different methods:
X <- cbind(HCC$cg20202438, HCC$cg18384097); D <- HCC$tumor
## 1. Linear combinations with fixed parameters by Pepe and Thompson (2000)
multiROC(X, D, method = "fixedLinear", methodLinear = "PepeThompson")
## 2.Linear combinations with dynamic parameters by Meisner et al. (2021)
### Time consuming
multiROC(X, D, method = "dynamicMeisner")
## 3. Logistic regression model with quadratic formula by default
multiROC(X, D)
## 4. Optimal transformation with multivariate KDE by Martínez-Camblor et al. (2021)
multiROC(X, D, method = "kernelOptimal")

# ROC curve for genes 20202438, 18384097, and 03515901 (p=3) to identify tumor
# by 4 different methods:
X <- cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901); D <- HCC$tumor
## 1. Linear combinations with fixed parameters by Pepe and Thompson (2000)
multiROC(X, D, method = "fixedLinear", methodLinear = "PepeThompson")
## 2.Linear combinations with dynamic parameters by Meisner et al. (2021)
### Time consuming
multiROC(X, D, method = "dynamicMeisner")
## 3. Logistic regression model with quadratic formula by default
multiROC(X, D)
## 4. Optimal transformation with multivariate KDE by Martínez-Camblor et al. (2021)
multiROC(X, D, method = "kernelOptimal")

Plot an ROC curve

Description

This is one of the core functions of the movieROC package. It displays the empirical ROC curve estimate from an object of class ‘groc’, ‘hroc’, or ‘multiroc’.

Usage

## S3 method for class 'groc'
plot(x, xlim = c(0, 1), ylim = c(0, 1), lwd = 3, 
  xlab = "False-Positive Rate", ylab = "True-Positive Rate", main = "ROC curve", 
  cex.lab = 1.25, cex.main = 1.5, type = NULL, new = TRUE, ...)
## S3 method for class 'hroc'
plot(x, type = 'S', xlim = c(0,1), ylim = c(0,1), 
  lwd = 3, xlab = "False-Positive Rate", ylab = "True-Positive Rate", 
  main = "ROC Curve", cex.lab = 1.25, cex.main = 1.5, new = TRUE, ...)
## S3 method for class 'multiroc'
plot(x, ...)
## S3 method for class 'groc'
plot(x, xlim = c(0, 1), ylim = c(0, 1), lwd = 3, 
  xlab = "False-Positive Rate", ylab = "True-Positive Rate", main = "ROC curve", 
  cex.lab = 1.25, cex.main = 1.5, type = NULL, new = TRUE, ...)
## S3 method for class 'hroc'
plot(x, type = 'S', xlim = c(0,1), ylim = c(0,1), 
  lwd = 3, xlab = "False-Positive Rate", ylab = "True-Positive Rate", 
  main = "ROC Curve", cex.lab = 1.25, cex.main = 1.5, new = TRUE, ...)
## S3 method for class 'multiroc'
plot(x, ...)

Arguments

`x`	An ROC curve object from movieROC package. Possible classes are: ‘groc’ (output of `gROC()` function), ‘hroc’ (output of `hROC()` function), and ‘multiroc’ (output of `multiROC()` function).
`xlim`, `ylim`	Range for x- and y-axis. Default: unit interval.
`lwd`	Line width of the ROC curve. Default: 3.
`xlab`, `ylab`	Label for x- and y-axis.
`main`	Title for the plot.
`cex.lab`, `cex.main`	The magnification to be used for labels and main title, respectively, relative to the current setting of `cex`. Default: `cex.lab = 1.25, cex.main = 1.5`.
`type`	What type of plot should be drawn (see help from `plot` function in base package). Default: `type = "s"` (stair steps), except for `x` object from `gROC_param` function, in which case `type = "l"` (lines).
`new`	If TRUE, a new plot is displayed; otherwise, the ROC curve is plotted over the existing graphic. Default: TRUE.
`...`	Other graphical parameters to be passed.

Value

A plot of the ROC curve with the selected graphical parameters

Examples

data(HCC)
# ROC curve estimates for gene 03515901 and response tumor
rroc <- gROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Right-sided
lroc <- gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided
hroc <- hROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Transformed by a cubic polinomial

plot(rroc, lty = 2, frame = FALSE)
plot(lroc, new = FALSE)
plot(hroc, new = FALSE, col = "blue")
legend("topleft", legend = c("Right-sided", "Left-sided", "Transformed marker"),
    col = c("black", "black", "blue"), lty = c(1,2,1), lwd = 2, bty = "n")

# ROC curve estimate for genes 20202438 and 18384097 to simultaneously identify tumor
# by a logistic regression model with quadratic formula
biroc <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097), D = HCC$tumor)
plot(biroc)
legend("bottomright", paste("AUC = ", format(biroc$auc, digits = 3)))

# ROC curve estimate for genes 20202438, 18384097 and 03515901 to simultaneously 
# identify tumor by a linear combinations with fixed parameters by Pepe and Thompson (2000)
multiroc <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901), 
    D = HCC$tumor, method = "fixedLinear", methodLinear = "PepeThompson")
plot(multiroc)
legend("bottomright", paste("AUC = ", format(multiroc$auc, digits = 3)))
data(HCC)
# ROC curve estimates for gene 03515901 and response tumor
rroc <- gROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Right-sided
lroc <- gROC(X = HCC[,"cg03515901"], D = HCC$tumor, side = "left") # Left-sided
hroc <- hROC(X = HCC[,"cg03515901"], D = HCC$tumor) # Transformed by a cubic polinomial

plot(rroc, lty = 2, frame = FALSE)
plot(lroc, new = FALSE)
plot(hroc, new = FALSE, col = "blue")
legend("topleft", legend = c("Right-sided", "Left-sided", "Transformed marker"),
    col = c("black", "black", "blue"), lty = c(1,2,1), lwd = 2, bty = "n")

# ROC curve estimate for genes 20202438 and 18384097 to simultaneously identify tumor
# by a logistic regression model with quadratic formula
biroc <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097), D = HCC$tumor)
plot(biroc)
legend("bottomright", paste("AUC = ", format(biroc$auc, digits = 3)))

# ROC curve estimate for genes 20202438, 18384097 and 03515901 to simultaneously 
# identify tumor by a linear combinations with fixed parameters by Pepe and Thompson (2000)
multiroc <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901), 
    D = HCC$tumor, method = "fixedLinear", methodLinear = "PepeThompson")
plot(multiroc)
legend("bottomright", paste("AUC = ", format(multiroc$auc, digits = 3)))

Plot the building procedure of the ROC curve

Description

This function tracks the ROC curve. It plots two graphics in the same figure: right, the empirical ROC curve estimate; left, classification subset for a particular FPR or threshold(s) for the marker (threshold only for object of class ‘groc’).

Usage

## S3 method for class 'groc'
plot_buildROC(x, FPR = NULL, C = NULL, XL = NULL, XU = NULL, h = c(1,1), 
    histogram = FALSE, breaks = 15, reduce = TRUE, build.process = FALSE, 
    completeROC = FALSE, new.window = FALSE, legends = FALSE, type = 's', 
    cex.point = 1.5, lwd.curve = 2, mar = NULL, lim.density = 0.01, xlim = NULL, 
    ylim = NULL, cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, xlab = "Marker", 
    main.density = "Density functions", col.controlscases = c('#485C99','#8F3D52'), 
    col.threshold = '#FCBA04', col.curve = 'black', eps = sqrt(.Machine$double.eps), 
    ...)
## S3 method for class 'multiroc'
plot_buildROC(x, FPR = 0.15, display.method = c("PCA", "OV"), 
    displayOV = c(1,2), build.process = FALSE, completeROC = TRUE, 
    new = FALSE, new.window = FALSE, border = FALSE, cutoff = TRUE, legends = FALSE, 
    type = 's', col.controlscases = c('#485C99','#8F3D52'), 
    col.threshold = '#FCBA04', col.curve = 'black', cex.point = 1.5, 
    alpha.points = .75, alpha.contour = 0.25, lwd.curve = 2, lty.curve = 1, 
    cex = 0.8, cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, xlab = NULL, ylab = NULL, 
    lf = NULL, eps = sqrt(.Machine$double.eps), ...)
## S3 method for class 'groc'
plot_buildROC(x, FPR = NULL, C = NULL, XL = NULL, XU = NULL, h = c(1,1), 
    histogram = FALSE, breaks = 15, reduce = TRUE, build.process = FALSE, 
    completeROC = FALSE, new.window = FALSE, legends = FALSE, type = 's', 
    cex.point = 1.5, lwd.curve = 2, mar = NULL, lim.density = 0.01, xlim = NULL, 
    ylim = NULL, cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, xlab = "Marker", 
    main.density = "Density functions", col.controlscases = c('#485C99','#8F3D52'), 
    col.threshold = '#FCBA04', col.curve = 'black', eps = sqrt(.Machine$double.eps), 
    ...)
## S3 method for class 'multiroc'
plot_buildROC(x, FPR = 0.15, display.method = c("PCA", "OV"), 
    displayOV = c(1,2), build.process = FALSE, completeROC = TRUE, 
    new = FALSE, new.window = FALSE, border = FALSE, cutoff = TRUE, legends = FALSE, 
    type = 's', col.controlscases = c('#485C99','#8F3D52'), 
    col.threshold = '#FCBA04', col.curve = 'black', cex.point = 1.5, 
    alpha.points = .75, alpha.contour = 0.25, lwd.curve = 2, lty.curve = 1, 
    cex = 0.8, cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, xlab = NULL, ylab = NULL, 
    lf = NULL, eps = sqrt(.Machine$double.eps), ...)

Arguments

`x`	An ROC curve object from movieROC package. Possible classes are: ‘groc’ (output of `gROC()` function) and ‘multiroc’ (output of `multiROC()` function).
`FPR`	False-positive rate for which the left plot is computed. Default: 0.15.
`C`	Marker cutoff for which the left plot is computed. Only available for a ‘groc’ object.
`XL`, `XU`	Marker cutoffs for which the left plot is computed. Only available for object of class ‘groc’ with `side = "both"` or `"both2"`.
`h`	A vector of length 2 with the bandwidth used to compute kernel density estimation for controls and cases, respectively. See `adjust` parameter in `density()` function from stats package. Default: `h = c(1,1)`.
`histogram`	If TRUE, histograms are displayed on the left instead of kernel density estimates. Default: FALSE. Only available for a ‘groc’ object.
`breaks`	If `histogram = TRUE`, number of breaks used for the histograms. Default: 15.
`reduce`	If FALSE, two extra graphics are displayed at the bottom (see Details for more information). Default: TRUE. Only available for a ‘groc’ object.
`build.process`	If FALSE, the whole ROC curve is displayed; otherwise, if `completeROC = TRUE`, the portion of the ROC curve until the fixed FPR is highlighted in black and the rest is shown in gray. Default: FALSE.
`completeROC`	If `build.process = TRUE`, a logical value indicating if the whole ROC curve should be displayed in light gray or not. Default: FALSE for ‘groc’ object; TRUE for ‘multiroc’ object.
`new.window`, `new`	If TRUE, graphics are displayed in a new window. Default: FALSE. `new` not available for object of class ‘groc’.
`legends`	If TRUE, a legend with colors meaning is displayed on the left graphic and the AUC is shown on the right graphic. Default: FALSE.
`type`, `lty.curve`, `lwd.curve`, `col.curve`	The line type, width and color for ROC curve. Default: `type = "s"` (stair steps), `lty.curve = 1` (solid), `lwd.curve = 2` and `col.curve = "black"`.
`cex.lab`, `cex.axis`, `cex.main`	The magnification to be used for labels, axis annotation and main titles, respectively, relative to the current setting of `cex`. Default: `cex.lab = 1.5, cex.axis = 1.5, cex.main = 2`.
`cex`	A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. Default: 0.8. Not available for object of class ‘groc’ (`cex=1` in this case).
`cex.point`	The magnification to be used for the particular point on the ROC curve and its text, relative to the current setting of `cex`. Default: `cex.point = 1.5`.
`xlim`, `ylim`	Range for x- and y-axis on the left plot. Only available for a ‘groc’ object.
`lim.density`	If `xlim` is `NULL`, lower limit for the density estimate to compute the range for the x-axis on the left plot. Default: 0.01. Only available for a ‘groc’ object.
`xlab`, `ylab`	Label for x- and y-axis on the left plot. `ylab` not available for object of class ‘groc’.
`main.density`	Title for the left plot. Only available for a ‘groc’ object.
`col.controlscases`	Vector of length 2 with the color used to control and case group, respectively. Default: `c('#485C99','#8F3D52')`.
`col.threshold`	Color for the chosen point. Default: `"FCBA04"`.
`mar`	A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. Only available for a ‘groc’ object.
`eps`	Epsilon value to consider. Default: `sqrt(.Machine$double.eps)`.
`border`	If TRUE, a border for the classification subsets is drawn. Default: FALSE for ‘multiroc’ object (TRUE if $p=2$ ). Not available for object of class ‘groc’.
`cutoff`	If TRUE, the cutoff value for the resulting univariate marker is displayed on the right plot (ROC curve). Default: TRUE. Not available for object of class ‘groc’.
`alpha.points`, `alpha.contour`	Number in [0,1] modifying the opacity alpha of the color for the points and classification region. The `adjustcolor()` function in grDevices package is used. Default: `alpha.points = 0.75, alpha.contour = 0.25`. Not available for object of class ‘groc’.
`lf`	Epsilon value for steps. Not used. Not available for object of class ‘groc’.
`display.method`	Method to display the marker values from a ‘multiroc’ object on the left plot. Methods available: `"OV"` (projected over two selected components of the marker indicated in `displayOV`), or `"PCA"` (projected over the two principal components from a Principal Component Analysis). Default: `"PCA"`. Only available for object of class ‘multiroc’.
`displayOV`	If `display.method = "OV"`, the two components of the marker used to project the marker values on the left plot. Default: `c(1,2)` (two first components). Only available for object of class ‘multiroc’.
`...`	Other parameters to be passed. Not used.

Details

- For univariate ROC curves:

It plots two graphics in the same figure: left, density function estimates for the marker in both populations with the areas corresponding to FPR and TPR colored (blue and red, respectively) for a particular FPR or threshold(s) for the marker; right, the empirical ROC curve estimate.

For an object of class ‘groc’, if the input parameter reduce = FALSE, two more graphics are displayed at the bottom: left, boxplots for the marker distribution in both populations; right, classification subsets for every false-positive rate (grey color by default).

- For multivariate ROC curves:

Plot two graphics in the same figure: right, the ROC curve highlighting the point and the threshold for the resulting univariate marker; left, scatterplot with the marker values for controls (blue color) and cases (red color), and the classification subset (in gold color) reporting the false-positive rate selected by the user (if FPR is not NULL).

If build.process = FALSE, the whole ROC curve is displayed; otherwise, if completeROC = TRUE, the portion of the ROC curve until the fixed FPR is highlighted in black and the rest is shown in gray, while if completeROC = FALSE, only the first portion of the curve is illustrated.

Value

A plot of the building procedure of the ROC curve with the selected graphical parameters

Dependencies

If method = "kernelOptimal" in a ‘multiroc’ object, the na.locf() function in the zoo package is used.

Examples

data(HCC)

# Standard ROC curve for gene 20202438
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor, side = "right")
plot_buildROC(roc_cg20202438)
plot_buildROC(roc_cg20202438, C = .77, build.process = TRUE, reduce = FALSE)

# Multivariate ROC curve for genes 0202438, 18384097, and 03515901
multiroc_PT <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901), 
    D = HCC$tumor, method = "fixedLinear", methodLinear = "PepeThompson")
plot_buildROC(multiroc_PT, cex = 1.2, lwd.curve = 4) # Two first components from PCA
plot_buildROC(multiroc_PT, display.method = "OV", displayOV = c(1,3), 
    cex = 1.2, xlab = "Gene 20202438", ylab = "Gene 03515901", lwd.curve = 4)
data(HCC)

# Standard ROC curve for gene 20202438
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor, side = "right")
plot_buildROC(roc_cg20202438)
plot_buildROC(roc_cg20202438, C = .77, build.process = TRUE, reduce = FALSE)

# Multivariate ROC curve for genes 0202438, 18384097, and 03515901
multiroc_PT <- multiROC(X = cbind(HCC$cg20202438, HCC$cg18384097, HCC$cg03515901), 
    D = HCC$tumor, method = "fixedLinear", methodLinear = "PepeThompson")
plot_buildROC(multiroc_PT, cex = 1.2, lwd.curve = 4) # Two first components from PCA
plot_buildROC(multiroc_PT, display.method = "OV", displayOV = c(1,3), 
    cex = 1.2, xlab = "Gene 20202438", ylab = "Gene 03515901", lwd.curve = 4)

Plot density function estimates for controls and cases

Description

This function plots the kernel density function estimates for controls and cases from an object of class ‘groc’. It offers the possibility of plotting the histograms instead of the smooth estimates.

Usage

plot_densities(obj, h = c(1, 1), histogram = FALSE, breaks = 15, 
  col = c("#485C99", "#8F3D52"), lwd = 2, xlim = NULL, ylim = NULL, 
  xaxs = "i", yaxs = "i", xlab = "Marker", ylab = "f(x)", 
  main = "Density functions", legend = FALSE, pos.legend = "topright", 
  cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, cex.legend = 1, 
  eps = sqrt(.Machine$double.eps), new = TRUE, ...)
plot_densities(obj, h = c(1, 1), histogram = FALSE, breaks = 15, 
  col = c("#485C99", "#8F3D52"), lwd = 2, xlim = NULL, ylim = NULL, 
  xaxs = "i", yaxs = "i", xlab = "Marker", ylab = "f(x)", 
  main = "Density functions", legend = FALSE, pos.legend = "topright", 
  cex.lab = 1.5, cex.axis = 1.5, cex.main = 2, cex.legend = 1, 
  eps = sqrt(.Machine$double.eps), new = TRUE, ...)

Arguments

`obj`	An object of class ‘groc’.
`h`	A vector of length 2 with the bandwidth used to compute kernel density estimation for controls and cases, respectively. See `adjust` parameter in `density()` function from stats package. Default: `h = c(1,1)`.
`histogram`	If TRUE, histograms are displayed instead of kernel density estimates. Default: FALSE.
`breaks`	If `histogram = TRUE`, number of breaks used for the histograms. Default: 15.
`col`	A vector of length 2 with color used for controls and cases, respectively. Default: `c("#485C99", "#8F3D52")`.
`lwd`	Line width for the density function or histogram. Default: 2.
`xlim`, `ylim`	Range for x- and y-axis.
`xaxs`, `yaxs`	The style of axis interval calculation to be used for the x- and the y-axis, respectively. Default: `"i"` (internal; just finds an axis with pretty labels that fits within the original data range). For more information, see help for `par`.
`xlab`, `ylab`	Label for x- and y-axis.
`main`	Title for the plot.
`cex.lab`, `cex.axis`, `cex.main`	The magnification to be used for labels, axis annotation and main titles, respectively, relative to the current setting of `cex`. Default: `cex.lab = 1.5, cex.axis = 1.5, cex.main = 2`.
`legend`	If TRUE, a legend with the meaning of colors is displayed on the left plot. Default: FALSE.
`pos.legend`, `cex.legend`	The position and magnification to be used for legend, relative to the current setting of `cex`. Default: `pos.legend = "topright", cex.legend = 1`.
`eps`	Epsilon value to consider. Default: `sqrt(.Machine$double.eps)`.
`new`	If TRUE, a new plot is displayed; otherwise, density estimates are displayed over the current plot. Default: TRUE.
`...`	Other parameters to be passed. Not used.

Value

A plot of the kernel density function estimates for both populations with the selected graphical parameters

Examples

data(HCC)
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor)
plot_densities(roc_cg20202438, main = "Density functions and histogram")
plot_densities(roc_cg20202438, histogram = TRUE, new = FALSE)
data(HCC)
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor)
plot_densities(roc_cg20202438, main = "Density functions and histogram")
plot_densities(roc_cg20202438, histogram = TRUE, new = FALSE)

Plot standard smooth ROC curve estimate

Description

This function estimates the standard ROC curve (right- or left-sided, depending on the side of the object of class ‘groc’ included) resulting from the kernel density function estimation for controls and cases. Two graphics are displayed: left, the kernel density estimates; right, the resulting ROC curve. Two extra graphics may be shown at the bottom if the used introduces a value for the input parameter C: left, boxplots and points for controls and cases and corresponding classification subset in gray color; right, classification subsets for every FPR until the one corresponding to the chosen C.

Usage

plot_densityROC(obj, h = c(1, 1), C = NULL, build.process = FALSE, 
  completeROC = TRUE, legends = FALSE, rel.tol = 0.001, 
  par.specify = FALSE, cex.lab = 1.5, cex.axis = 1.25, cex.main = 1.75, 
  lwd = 2, col = c("#485C99", "#8F3D52"), col.roc = "blue", ...)
plot_densityROC(obj, h = c(1, 1), C = NULL, build.process = FALSE, 
  completeROC = TRUE, legends = FALSE, rel.tol = 0.001, 
  par.specify = FALSE, cex.lab = 1.5, cex.axis = 1.25, cex.main = 1.75, 
  lwd = 2, col = c("#485C99", "#8F3D52"), col.roc = "blue", ...)

Arguments

`obj`	An object of class ‘groc’ with `side = "right"` or `"left"`.
`h`	A vector of length 2 with the bandwidth used to compute kernel density estimation for controls and cases, respectively. See `adjust` parameter in `density()` function from stats package. Default: `h = c(1,1)`.
`C`	Marker cutoff for which the graphics are displayed. Default: none.
`build.process`	If FALSE, the whole ROC curve is displayed; otherwise, if `completeROC = TRUE`, the portion of the ROC curve until the fixed FPR (resulting from the chosen `C`) is highlighted in black and the rest is shown in gray. Default: FALSE.
`completeROC`	If `build.process = TRUE`, a logical value indicating if the whole ROC curve should be displayed in light gray or not. Default: TRUE.
`legends`	If TRUE, legends with the meaning of colors are displayed. Default: FALSE.
`rel.tol`	Relative accuracy requested for the `integrate()` function from stats package. Default: 0.001.
`par.specify`	If FALSE, graphics are organized in one row and two columns. Default: FALSE.
`cex.lab`, `cex.axis`, `cex.main`	The magnification to be used for labels, axis annotation and main titles, respectively, relative to the current setting of `cex`. Default: `cex.lab = 1.5, cex.axis = 1.25, cex.main = 1.75`.
`lwd`, `col.roc`	Line width and color for the ROC curve. Default: `lwd = 2, col.roc = "blue"`.
`col`	A vector of length 2 with color used for controls and cases, respectively. Default: `c("#485C99", "#8F3D52")`.
`...`	Other parameters to be passed. Not used.

Value

A plot of the standard smooth ROC curve estimate with the selected graphical parameters

Examples

data(HCC)
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor)
plot_densityROC(roc_cg20202438)
plot_densityROC(roc_cg20202438, h = c(2,2))
data(HCC)
roc_cg20202438 <- gROC(X = HCC$cg20202438, D = HCC$tumor)
plot_densityROC(roc_cg20202438)
plot_densityROC(roc_cg20202438, h = c(2,2))

Plot the transformation function used for the marker

Description

This function plots the transformation function used for the marker for an object of class ‘hroc’ or class ‘groc’ (this one is only allowed for objects with self-contained classification subsets). Over this graphic, the classification region for a particular FPR chosen by the user is displayed in blue color. If the user specifies a second FPR (FPR2), the classification region is displayed on the same graphic in red color.

Usage

## S3 method for class 'hroc'
plot_funregions(x, FPR = 0.15, FPR2 = NULL, 
  plot.subsets = TRUE, new.window = FALSE, main = NULL, ylim = NULL, ...)
## S3 method for class 'groc'
plot_funregions(x, FPR = 0.15, FPR2 = NULL, 
  plot.subsets = TRUE, new.window = FALSE, main = NULL, ylim = NULL, ...)
## S3 method for class 'hroc'
plot_funregions(x, FPR = 0.15, FPR2 = NULL, 
  plot.subsets = TRUE, new.window = FALSE, main = NULL, ylim = NULL, ...)
## S3 method for class 'groc'
plot_funregions(x, FPR = 0.15, FPR2 = NULL, 
  plot.subsets = TRUE, new.window = FALSE, main = NULL, ylim = NULL, ...)

Arguments

`x`	An object of class ‘hroc’ or ‘groc’.
`FPR`	False-positive rate used to display the classification region in blue. Default: 0.15.
`FPR2`	Other false-positive rate used to display the classification region in red. Default: none.
`plot.subsets`	If TRUE, the classification subsets are displayed. Otherwise, only the transformation function. Default: TRUE.
`new.window`	If TRUE, graphics are displayed in a new window. Default: FALSE.
`main`	Title for the plot.
`ylim`	Range for the y-axis.
`...`	Other parameters to be passed. Not used.

Value

A plot of the transformation function used for the marker with the selected graphical parameters

Examples

data(HCC)

hroc_cg18384097 <- hROC(X = HCC$cg18384097, D = HCC$tumor, 
    formula.lrm = "D ~ rcs(X,8)") 
plot_funregions(hroc_cg18384097)
plot_funregions(hroc_cg18384097, FPR = .1, FPR2 = .5)
data(HCC)

hroc_cg18384097 <- hROC(X = HCC$cg18384097, D = HCC$tumor, 
    formula.lrm = "D ~ rcs(X,8)") 
plot_funregions(hroc_cg18384097)
plot_funregions(hroc_cg18384097, FPR = .1, FPR2 = .5)

Plot the classification regions underlying a ROC curve

Description

This function plots the classification regions for univariate markers. It works for objects of class ‘groc’ and ‘hroc’. Two graphics are displayed in the same figure: left, classification subsets for every false-positive rate (grey color by default); right, 90º rotated ROC curve.

Usage

## S3 method for class 'groc'
plot_regions(x, FPR = 0.15, plot.roc = TRUE, plot.auc = FALSE, 
  col = c("white", "grey"), col.FPR = "blue", lwd = 2, new.window = TRUE, 
  type.plotroc = "s", xlim = NULL, mar = c(5, 6, 4, 0.25), 
  cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.75, main = NULL, 
  xlab = "", ylab = "False-Positive Rate", main.plotroc = "ROC curve", 
  legend = TRUE, cex.legend = 1, ...)
## S3 method for class 'hroc'
plot_regions(x, FPR = 0.15, plot.roc = TRUE, plot.auc = FALSE, 
  col = c('white','grey'), col.FPR = 'blue', lwd = 2, new.window = TRUE, 
  type.plotroc = 's', xlim = NULL, mar = c(5,6,4,0.25), 
  cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.75, main = NULL, 
  xlab = "", ylab = "False-Positive Rate", main.plotroc = "ROC curve", 
  legend = TRUE, cex.legend = 1, verbose = FALSE, ...)
## S3 method for class 'groc'
plot_regions(x, FPR = 0.15, plot.roc = TRUE, plot.auc = FALSE, 
  col = c("white", "grey"), col.FPR = "blue", lwd = 2, new.window = TRUE, 
  type.plotroc = "s", xlim = NULL, mar = c(5, 6, 4, 0.25), 
  cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.75, main = NULL, 
  xlab = "", ylab = "False-Positive Rate", main.plotroc = "ROC curve", 
  legend = TRUE, cex.legend = 1, ...)
## S3 method for class 'hroc'
plot_regions(x, FPR = 0.15, plot.roc = TRUE, plot.auc = FALSE, 
  col = c('white','grey'), col.FPR = 'blue', lwd = 2, new.window = TRUE, 
  type.plotroc = 's', xlim = NULL, mar = c(5,6,4,0.25), 
  cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.75, main = NULL, 
  xlab = "", ylab = "False-Positive Rate", main.plotroc = "ROC curve", 
  legend = TRUE, cex.legend = 1, verbose = FALSE, ...)

Arguments

`x`	An ROC curve object from a univariate marker. Possible classes are: ‘groc’ (output of `gROC` function) and ‘hroc’ (output of `hROC` function),
`FPR`	False-positive rate to be highlighted, both on the ROC curve (right plot) and classification subset (left plot). Default: 0.15.
`plot.roc`	If TRUE, the ROC curve is displayed. Default: TRUE.
`plot.auc`	If TRUE, the AUC is displayed on the plot of the bottomleft corner of the ROC curve plot. Default: FALSE.
`col`	Vector of length 2 with colors used for outside and inside of classification subsets, respectively. Default: `c("white", "grey")`.
`col.FPR`	Color used to highlight the `FPR` chose. Default: `"blue"`.
`type.plotroc`, `lwd`	Line type and width for the ROC curve. Default: `type.plotroc = "s", lwd = 2`.
`new.window`	If TRUE, graphics are displayed in a new window. Default: TRUE.
`xlim`	Range for x-axis on the left plot. Default: range of the marker values.
`mar`	A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot.
`cex.lab`, `cex.axis`, `cex.main`	The magnification to be used for labels, axis annotation and main titles, respectively, relative to the current setting of `cex`. Default: `cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.75`.
`xlab`, `ylab`	Label for x- and y-axis on the left plot.
`main`, `main.plotroc`	Title for the left and the right plot, respectively.
`legend`	If TRUE, a legend with the meaning of colors is displayed on the left plot. Default: TRUE.
`cex.legend`	The magnification to be used for legend, relative to the current setting of `cex`. Default: 1.
`verbose`	If TRUE, a progress bar is displayed. Default: FALSE.
`...`	Other parameters to be passed. Not used.

Value

A plot of the classification regions underlying a ROC curve with the selected graphical parameters

Examples

data(HCC)
# 1. Standard ROC curve
roc_cg18384097 <- gROC(X = HCC$cg18384097, D = HCC$tumor)
plot_regions(roc_cg18384097, plot.auc = TRUE)
# 2. gROC curve
groc_cg18384097 <- gROC(X = HCC$cg18384097, D = HCC$tumor, side = "both")
plot_regions(groc_cg18384097, plot.auc = TRUE)
# 3. hROC curve with a restricted cubic splines transformation
hroc_cg18384097 <- hROC(X = HCC$cg18384097, D = HCC$tumor, 
    formula.lrm = "D ~ rcs(X,8)") 
plot_regions(hroc_cg18384097, plot.auc = TRUE)
data(HCC)
# 1. Standard ROC curve
roc_cg18384097 <- gROC(X = HCC$cg18384097, D = HCC$tumor)
plot_regions(roc_cg18384097, plot.auc = TRUE)
# 2. gROC curve
groc_cg18384097 <- gROC(X = HCC$cg18384097, D = HCC$tumor, side = "both")
plot_regions(groc_cg18384097, plot.auc = TRUE)
# 3. hROC curve with a restricted cubic splines transformation
hroc_cg18384097 <- hROC(X = HCC$cg18384097, D = HCC$tumor, 
    formula.lrm = "D ~ rcs(X,8)") 
plot_regions(hroc_cg18384097, plot.auc = TRUE)

Predict the classification regions for a particular specificity

Description

This function prints the classification subsets corresponding to a particular false-positive rate FPR or to cutoff value(s) C or XL, XU introduced by the user.

Usage

## S3 method for class 'groc'
predict(object, FPR = NULL, C = NULL, XL = NULL, XU = NULL, ...)
## S3 method for class 'hroc'
predict(object, FPR = 0.15, ...)
## S3 method for class 'groc'
predict(object, FPR = NULL, C = NULL, XL = NULL, XU = NULL, ...)
## S3 method for class 'hroc'
predict(object, FPR = 0.15, ...)

Arguments

`object`	An object of class ‘groc’ or ‘hroc’.
`FPR`	False-positive rate used to predict the classification region. Default: 0.15 if no cutoff value is provided by the next input parameters.
`C`	Cutoff value used to predict the classification region for ‘groc’ object with `side = "right"` or `"left"`. If `FPR` is provided, `C` is not used. Default: none.
`XL`, `XU`	Cutoff values used to predict the classification region for ‘groc’ object with `side = "both"` or `"both2"`. If `FPR` is provided, `C` is not used. Default: none.
`...`	Other parameters to be passed. Not used.

Value

A list of length 3 with the following fields:

`ClassSubsets`	A matrix with the classification region. Number of rows indicate the number of intervals whose union defines the classification region.
`Specificity`	Resulting specificity value.
`Sensitivity`	Resulting sensitivity value.

Examples

data(HCC)

roc <- gROC(X = HCC$cg18384097, D = HCC$tumor) # Right-sided ROC curve
predict(roc, FPR = 0.5)
groc <- gROC(X = HCC$cg18384097, D = HCC$tumor, side = "both") # gROC curve
predict(groc, FPR = 0.5)
hroc_cg18384097 <- hROC(X = HCC$cg18384097, D = HCC$tumor, 
    formula.lrm = "D ~ rcs(X,8)") 
predict(hroc_cg18384097, FPR = 0.5)
data(HCC)

roc <- gROC(X = HCC$cg18384097, D = HCC$tumor) # Right-sided ROC curve
predict(roc, FPR = 0.5)
groc <- gROC(X = HCC$cg18384097, D = HCC$tumor, side = "both") # gROC curve
predict(groc, FPR = 0.5)
hroc_cg18384097 <- hROC(X = HCC$cg18384097, D = HCC$tumor, 
    formula.lrm = "D ~ rcs(X,8)") 
predict(hroc_cg18384097, FPR = 0.5)

Print an ROC curve object

Description

This function prints information about an ROC curve.

Usage

## S3 method for class 'groc'
print(x, ...)
## S3 method for class 'hroc'
print(x, ...)
## S3 method for class 'multiroc'
print(x, ...)
## S3 method for class 'groc'
print(x, ...)
## S3 method for class 'hroc'
print(x, ...)
## S3 method for class 'multiroc'
print(x, ...)

Arguments

`x`	An ROC curve object from the movieROC package. Possible classes are: ‘groc’ (output of `gROC()` function), ‘hroc’ (output of `hROC()` function), and ‘multiroc’ (output of `multiROC()` function).
`...`	Other parameters to be passed. Not used.

Value

A character vector with the details of an ROC curve object.

Examples

## See examples for gROC(), hROC(), and multiROC() function
## See examples for gROC(), hROC(), and multiROC() function

Package 'movieROC'

Help Index

Build a ROC curve for a univariate marker

Description

Usage

Arguments

Details

Value

Dependencies

References

Examples

Build a binormal ROC curve for a univariate marker

Description

Usage

Arguments

Details

Value

References

Examples

Hepatocellular carcinoma data

Description

Usage

Format

Source

Examples

Build a ROC curve for a transformation of a univariate marker

Description

Usage

Arguments

Details

Value

Dependencies

References

Examples

Create a video with the building procedure of the ROC curve

Description

Usage

Arguments

Value

Dependencies

Examples

Create a video with the building procedure of the smooth ROC curve estimate

Description

Usage

Arguments

Value

Dependencies

Examples

Build a ROC curve for a multivariate marker with dimension ppp

Description

Usage

Arguments

Value

Dependencies

References

Examples

Plot an ROC curve

Description

Usage

Arguments

Value

Examples

Plot the building procedure of the ROC curve

Description

Usage

Arguments

Details

Value

Dependencies

Examples

Plot density function estimates for controls and cases

Description

Usage

Arguments

Value

Examples

Plot standard smooth ROC curve estimate

Description

Usage

Arguments

Build a ROC curve for a multivariate marker with dimension $p$