nomogrammer {petersenlab} | R Documentation |
Create Nomogram.
Description
Create nomogram plot.
Usage
nomogrammer(
TP = NULL,
TN = NULL,
FP = NULL,
FN = NULL,
pretestProb = NULL,
selectionRate = NULL,
SN = NULL,
SP = NULL,
FPR = NULL,
PLR = NULL,
NLR = NULL,
Detail = FALSE,
NullLine = FALSE,
LabelSize = (14/5),
Verbose = FALSE
)
Arguments
TP |
Number of true positive cases. |
TN |
Number of true negative cases. |
FP |
Number of false positive cases. |
FN |
Number of false negative cases. |
pretestProb |
Pretest probability (prevalence/base rate/prior probability) of characteristic, as a number between 0 and 1. |
selectionRate |
Selection rate (marginal probability of positive test), as a number between 0 and 1. |
SN |
Sensitivity of the test at a given cut point, as a number between 0 and 1. |
SP |
Specificity of the test at a given cut point, as a number between 0 and 1. |
FPR |
False positive rate of the test at a given cut point, as a number between 0 and 1. |
PLR |
Positive likelihood ratio of the test at a given cut point. |
NLR |
Positive likelihood ratio of the test at a given cut point. |
Detail |
If |
NullLine |
If |
LabelSize |
Label size. |
Verbose |
Print out relevant metrics in the console. |
Details
Create nomogram plot from the following at a given cut point:
1) true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN)
2) pretest probability (pretestProb), sensitivity (SN), and specificity (SP), OR
3) pretest probability (pretestProb), sensitivity (SN), and false positive rate (FPR), OR
4) pretest probability (pretestProb), sensitivity (SN), and selection rate (selectionRate), OR
5) pretest probability (pretestProb), positive likelihood ratio (PLR), and negative likelihood ratio (NLR)
Value
ggplot object of nomogram plot.
See Also
https://github.com/achekroud/nomogrammer
Other accuracy:
accuracyAtCutoff()
,
accuracyAtEachCutoff()
,
accuracyOverall()
,
optimalCutoff()
,
posttestOdds()
Examples
nomogrammer(
TP = 253,
TN = 386,
FP = 14,
FN = 347)
nomogrammer(
pretestProb = .60,
SN = 0.421,
SP = 0.965)
nomogrammer(
pretestProb = .60,
SN = 0.421,
FPR = 0.035)
nomogrammer(
pretestProb = .60,
SN = 0.421,
selectionRate = 0.267)
nomogrammer(
pretestProb = .60,
PLR = 12,
NLR = 0.6)