plot.simple_screenr | R Documentation |
plot.simple_screenr
plots the k-fold cross-validated
receiver-operating characteristic, including confidence intervals on the
combinations of the local maxima of sensitivity and specificity.
Plot ROC curve with pointwise 95 intevals on sensitivity and specificity and (optionally) returns a dataframe containing numerical values.
## S3 method for class 'simple_screenr' plot( x, ..., plot_ci = TRUE, conf_level = 0.95, bootreps = 4000, print_auc = TRUE, partial_auc = c(0.8, 1), partial_auc_focus = c("sensitivity", "specificity"), partial_auc_correct = TRUE, type = c("l", "S") )
x |
an object of class |
... |
additional arguments for |
plot_ci |
logical indicator for plotting point-wise confidence
intervals at the locally maximum subset of coordinates for
on sensitivity and specificity. Default: |
conf_level |
confidence level in the interval (0,1). Default is 0.95
producing 95% confidence intervals. Default: |
bootreps |
numeric-valued number of bootstrap replication for estimation of 95% confidence intervals. Default: 4000. |
print_auc |
logical indicator for printing the area
under the ROC curve (AUC) on the plot. Default: |
partial_auc |
One of |
partial_auc_focus |
one of |
partial_auc_correct |
logical indictor for transformation of the pAUC
to fall within the range from 0.5 (random guess) to 1.0 (perfect
classification). Default: |
type |
type of plot; one of |
This function produces a plot as a side effect, and (optionally) returns a dataframe dataframe containing medians and bootstrap confidence limits of sensitivity and specificity.
Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters. 2006. 27(8):861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Linden A. Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. Journal of Evaluation in Clinical Practice. 2006; 12(2):132-139. https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1365-2753.2005.00598.x
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Muller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011; 12:77. https://www.biomedcentral.com/1471-2105/12/77
data(unicorns) too_simple <- simple_screenr(testresult ~ Q1 + Q2 + Q3 + Q4 + Q5 + Q6 + Q7, data = unicorns) plot(too_simple)
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