| Title: | Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers |
|---|---|
| Description: | The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC. |
| Authors: | Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut] |
| Maintainer: | Juan-Pedro Garcia <[email protected]> |
| License: | GPL-3 |
| Version: | 1.19.0 |
| Built: | 2024-11-22 06:24:10 UTC |
| Source: | https://github.com/bioc/ROCpAI |
Calculate the area under the ROC curve following McClish methodologic from a dataset and a sample from that dataset.
mcpAUC( dataset, low.value = NULL, up.value = NULL, plot = FALSE, selection = NULL, variable = NULL )mcpAUC( dataset, low.value = NULL, up.value = NULL, plot = FALSE, selection = NULL, variable = NULL )
dataset |
Dataframe of the complete information of the samples |
low.value |
lower false positive rate value that the function will use to calculate the pAUC |
up.value |
upper false positive rate value that the function will use to calculate the pAUC |
plot |
ROC plot |
selection |
vector that will only be used if the parameter "dataset" is a RangedSummarizedExperiment object. This parameter is used to select the variables that will be analysed |
variable |
in case that dataset is a SummarizedExperiment, indicate the Gold Standard |
RangedSummarizedExperiment object with the pAUC and the mcpAUC scores,and the TPR and FPR values for each ROC curve generated
library(fission) data("fission") resultsMC <- mcpAUC(fission, low.value = 0, up.value = 0.25, plot = TRUE, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")library(fission) data("fission") resultsMC <- mcpAUC(fission, low.value = 0, up.value = 0.25, plot = TRUE, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")
Calculates the confidence interval using a boot analysis
mcpAUCboot( dataset, low.value = NULL, up.value = NULL, r = 50, level = 0.95, type.interval = "perc", selection = NULL, variable = NULL )mcpAUCboot( dataset, low.value = NULL, up.value = NULL, r = 50, level = 0.95, type.interval = "perc", selection = NULL, variable = NULL )
dataset |
dataframe or RangedSummarizedExperiment objetc |
low.value |
lower false positive rate value that the function will use to calculate the pAUC |
up.value |
upper false positive rate value that the function will use to calculate the pAUC |
r |
number of iterations. |
level |
confidence level |
type.interval |
String that represent the type of intervals required. The value should be any subset of the values c("norm","basic", "stud", "perc", "bca") or simply "all" which will compute all five types of intervals. |
selection |
vector that will only be used if the parameter "dataset" is a RangedSummarizedExperiment object. This parameter is used to select the variables that will be analysed |
variable |
in case that dataset is a SummarizedExperiment, indicate the Gold Standard |
SummarizedExperiment object with the mcpAUC, the standard desviation, and the lower and upper limits of the confidence interval.
library(fission) data("fission") resultsMCboot <- mcpAUCboot(fission,low.value = 0, up.value = 0.25, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")library(fission) data("fission") resultsMCboot <- mcpAUCboot(fission,low.value = 0, up.value = 0.25, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")
It calculates the coordinates (fpr, sen) of the ROC curve. This function sorts the scores of a model test and generates the points which will be used to plot its the ROC curve
pointsCurve(x, y)pointsCurve(x, y)
x |
It is the vector of the status (gold standar) |
y |
It is the vector with the values of a predictor variable or clasificator |
return a matrix with the points of 1-specificity and sensibility that will be used to generate a ROC curve
library(fission) data("fission") strain <- fission@colData@listData$strain pointsCurve<- pointsCurve(strain, t(assay(fission))[,"SPNCRNA.1080"])library(fission) data("fission") strain <- fission@colData@listData$strain pointsCurve<- pointsCurve(strain, t(assay(fission))[,"SPNCRNA.1080"])
It standarizes the partial area under the ROC curve by the tigher index
tpAUC( dataset, low.value = NULL, up.value = NULL, plot = FALSE, selection = NULL, variable = NULL )tpAUC( dataset, low.value = NULL, up.value = NULL, plot = FALSE, selection = NULL, variable = NULL )
dataset |
Dataframe of the complete information of the samples |
low.value |
lower false positive rate value that the function will use to calculate the pAUC |
up.value |
upper false positive rate value that the function will use to calculate the pAUC |
plot |
ROC plot |
selection |
vector that will only be used if the parameter "dataset" is a RangedSummarizedExperiment object. This parameter is used to select the variables that will be analysed |
variable |
in case that dataset is a SummarizedExperiment, indicate the Gold Standard |
RangedSummarizedExperiment object with the pAUC and the tpAUC scores, and the TPR and FPR values for each ROC curve generated
library(fission) data("fission") resultsT <- tpAUC(fission, low.value = 0, up.value = 0.25, plot = TRUE, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")library(fission) data("fission") resultsT <- tpAUC(fission, low.value = 0, up.value = 0.25, plot = TRUE, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")
Calculates the confidence interval using a boot analysis
tpAUCboot( dataset, low.value = NULL, up.value = NULL, r = 50, level = 0.95, type.interval = "perc", selection = NULL, variable = NULL )tpAUCboot( dataset, low.value = NULL, up.value = NULL, r = 50, level = 0.95, type.interval = "perc", selection = NULL, variable = NULL )
dataset |
dataframe or RangedSummarizedExperiment objetc |
low.value |
lower false positive rate value that the function will use to calculate the pAUC |
up.value |
upper false positive rate value that the function will use to calculate the pAUC |
r |
number of iterations. |
level |
confidence level |
type.interval |
String that represent the type of intervals required. The value should be any subset of the values c("norm","basic", "stud", "perc", "bca") or simply "all" which will compute all five types of intervals. |
selection |
vector that will only be used if the parameter "dataset" is a RangedSummarizedExperiment object. This parameter is used to select the variables that will be analysed |
variable |
in case that dataset is a SummarizedExperiment, indicate the Gold Standard |
SummarizedExperiment object with the Tp_AUC, the standard desviation, and the lower and upper limits of the confidence interval
library(fission) data("fission") resultstboot<- tpAUCboot(fission,low.value = 0, up.value = 0.25, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")library(fission) data("fission") resultstboot<- tpAUCboot(fission,low.value = 0, up.value = 0.25, selection = c("SPNCRNA.1080","SPAC186.08c"), variable="strain")