In uriahf/rtichoke: rtichoke
```{=html}
```r
knitr::opts_chunk$set(
echo = FALSE,
message = FALSE,
warning = FALSE
)
library(rtichoke)
library(dplyr)
Performance Metrics Cheat Sheet
tibble::tribble(
~"type", ~"predicted_positive", ~"predicted_negative",
"Real Positive", "TP", "FN",
"Real Negative", "FP", "TN"
) %>%
reactable::reactable(sortable = FALSE,
fullWidth = FALSE,
borderless = FALSE,
defaultColDef = reactable::colDef(
align = "center",
headerStyle = list(fontWeight = 100)
),
columns = list(
type = reactable::colDef(
name = "",
align = "left",
style = list(fontWeight = 100),
minWidth = 110
),
predicted_positive = reactable::colDef(
name = "Predicted Positive",
style = function(value) {
color <- if (value == "TP") {
"lightgreen"
} else if (value == "FP") {
"pink"
}
list(fontWeight = 600, background = color)
}
),
predicted_negative = reactable::colDef(
name = "Predicted Negative",
style = function(value) {
color <- if (value == "TN") {
"lightgreen"
} else if (value == "FN") {
"pink"
}
list(fontWeight = 600, background = color)
}
)
)
)
$\begin{aligned} {\text{Prevalence}} = \frac{\text{TP + FN}}{\text{TP + FP + TN + FN}} \end{aligned}$
$\begin{aligned} {\text{PPCR (Predicted Positives Condition Rate)}} = \frac{\text{TP + FP}}{\text{TP + FP + TN + FN}} \end{aligned}$
$\begin{aligned} \text{Sensitivity (Recall, True Positive Rate)} = \frac{\text{TP}}{\text{TP + FN}} = \frac{\text{TP}}{\text{Real Positives}} = \text{Prob( Predicted Positive | Real Positive )} \end{aligned}$
$\begin{aligned} \text{Specificity (True Negative Rate)} = \frac{\text{TN}}{\text{TN + FP}} = \frac{\text{TN}}{\text{Real Negatives}} = \text{Prob( Predicted Negative | Real Negative )} \end{aligned}$
$\begin{aligned} \text{PPV (Precision)} = \frac{\text{TP}}{\text{TP + FP}} = \frac{\text{TP}}{\text{Predicted Positives}} = \text{Prob( Real Positive | Predicted Positive )} \end{aligned}$
$\begin{aligned} \text{NPV} = \frac{\text{TN}}{\text{TN + FN}} = \frac{\text{TN}}{\text{Predicted Negatives}} = \text{Prob( Real Negative | Predicted Negative )} \end{aligned}$
$\begin{aligned} \text{Lift} = \frac{\text{PPV}}{\text{Prevalence}} = \frac{\cfrac{\text{TP}}{\text{TP + FP}}}{\cfrac{\text{TP + FN}}{\text{TP + FP + TN + FN}}} \end{aligned}$
$\begin{aligned} \text{Net Benefit} = \frac{\text{TP}}{\text{TP + FP + TN + FN}} - \frac{\text{FP}}{\text{TP + FP + TN + FN}} * {\frac{{p_{t}}}{{1 - p_{t}}}} \end{aligned}$
performance_dat <- prepare_performance_data(probs = params$probs,
reals = params$reals)
create_table_for_prevalence(performance_dat)
Calibration {.tabset}
Smooth
create_calibration_curve(probs = params$probs,
reals = params$reals,
interactive = params$interactive,
type = "smooth", size = 550)
Discrete
create_calibration_curve(probs = params$probs,
reals = params$reals,
interactive = params$interactive,
size = 550)
Discrimination {.tabset}
create_table_for_auc(probs = params$probs,
reals = params$reals)
By Probability Threshold
Performance Metrics Curves {.tabset}
ROC
plot_roc_curve(performance_dat, interactive = params$interactive,
size = 500)
Lift
plot_lift_curve(performance_dat, interactive = params$interactive,
size = 500)
Precision Recall
plot_precision_recall_curve(performance_dat, interactive = params$interactive,
size = 500)
Gains
plot_gains_curve(performance_dat, interactive = params$interactive,
size = 500)
By Predicted Positives Condition Rate (PPCR)
Performance Metrics Curves {.tabset}
ROC
performance_dat_ppcr <- prepare_performance_data(probs = params$probs,
reals = params$reals,
stratified_by = "ppcr")
plot_roc_curve(performance_dat_ppcr, interactive = params$interactive,
size = 500)
Lift
plot_lift_curve(performance_dat_ppcr, interactive = params$interactive,
size = 500)
Precision Recall
plot_precision_recall_curve(performance_dat_ppcr, interactive = params$interactive,
size = 500)
Gains
plot_gains_curve(performance_dat_ppcr,
interactive = params$interactive,
size = 500)
Utility (Decision Curve)
plot_decision_curve(performance_dat,
interactive = params$interactive,
type = "combined",
size = 500)
Performance Table {.tabset}
By Probability Threshold
render_performance_table(performance_dat)
By Predicted Positives Condition Rate (PPCR)
render_performance_table(performance_dat_ppcr)
```{css echo=FALSE}
.form-control {
font-family: system-ui, sans-serif;
font-size: 2rem;
font-weight: bold;
line-height: 1.1;
display: grid;
grid-template-columns: 1em auto;
gap: 0.5em;
place-content: center;
}
.form-control + .form-control {
margin-top: 1em;
}
input[type="checkbox"] {
-webkit-appearance: none;
appearance: none;
background-color: #fff;
margin: 0;
font: inherit;
width: 1.15em;
height: 1.15em;
border: 0.15em solid currentColor;
border-radius: 0.15em;
transform: translateY(-0.075em);
display: grid;
place-content: center;
}
input[type="checkbox"][value="1"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #1b9e77;
background-color: CanvasText;
}
input[type="checkbox"][value="2"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #d95f02;
background-color: CanvasText;
}
input[type="checkbox"][value="3"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #7570b3;
background-color: CanvasText;
}
input[type="checkbox"][value="4"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #e7298a;
background-color: CanvasText;
}
input[type="checkbox"][value="5"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #07004D;
background-color: CanvasText;
}
input[type="checkbox"][value="6"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #E6AB02;
background-color: CanvasText;
}
input[type="checkbox"][value="7"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #FE5F55;
background-color: CanvasText;
}
input[type="checkbox"][value="8"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #54494B;
background-color: CanvasText;
}
input[type="checkbox"][value="9"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #006E90;
background-color: CanvasText;
}
input[type="checkbox"][value="10"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #BC96E6;
background-color: CanvasText;
}
input[type="checkbox"][value="11"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #52050A;
background-color: CanvasText;
}
input[type="checkbox"][value="12"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #1F271B;
background-color: CanvasText;
}
input[type="checkbox"][value="13"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #BE7C4D;
background-color: CanvasText;
}
input[type="checkbox"][value="14"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #63768D;
background-color: CanvasText;
}
input[type="checkbox"][value="15"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #08A045;
background-color: CanvasText;
}
input[type="checkbox"][value="16"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #320A28;
background-color: CanvasText;
}
input[type="checkbox"][value="17"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #82FF9E;
background-color: CanvasText;
}
input[type="checkbox"][value="18"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #2176FF;
background-color: CanvasText;
}
input[type="checkbox"][value="19"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #D1603D;
background-color: CanvasText;
}
input[type="checkbox"][value="20"]::before {
content: "";
width: 0.65em;
height: 0.65em;
transform: scale(0);
box-shadow: inset 1em 1em #585123;
background-color: CanvasText;
}
input[type="checkbox"]:checked::before {
transform: scale(1);
}
```
uriahf/rtichoke documentation built on Nov. 22, 2023, 1:30 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
```{=html}
```r
knitr::opts_chunk$set(
echo = FALSE,
message = FALSE,
warning = FALSE
)
library(rtichoke)
library(dplyr)
Performance Metrics Cheat Sheet
tibble::tribble( ~"type", ~"predicted_positive", ~"predicted_negative", "Real Positive", "TP", "FN", "Real Negative", "FP", "TN" ) %>% reactable::reactable(sortable = FALSE, fullWidth = FALSE, borderless = FALSE, defaultColDef = reactable::colDef( align = "center", headerStyle = list(fontWeight = 100) ), columns = list( type = reactable::colDef( name = "", align = "left", style = list(fontWeight = 100), minWidth = 110 ), predicted_positive = reactable::colDef( name = "Predicted Positive", style = function(value) { color <- if (value == "TP") { "lightgreen" } else if (value == "FP") { "pink" } list(fontWeight = 600, background = color) } ), predicted_negative = reactable::colDef( name = "Predicted Negative", style = function(value) { color <- if (value == "TN") { "lightgreen" } else if (value == "FN") { "pink" } list(fontWeight = 600, background = color) } ) ) )
$\begin{aligned} {\text{Prevalence}} = \frac{\text{TP + FN}}{\text{TP + FP + TN + FN}} \end{aligned}$
$\begin{aligned} {\text{PPCR (Predicted Positives Condition Rate)}} = \frac{\text{TP + FP}}{\text{TP + FP + TN + FN}} \end{aligned}$
$\begin{aligned} \text{Sensitivity (Recall, True Positive Rate)} = \frac{\text{TP}}{\text{TP + FN}} = \frac{\text{TP}}{\text{Real Positives}} = \text{Prob( Predicted Positive | Real Positive )} \end{aligned}$
$\begin{aligned} \text{Specificity (True Negative Rate)} = \frac{\text{TN}}{\text{TN + FP}} = \frac{\text{TN}}{\text{Real Negatives}} = \text{Prob( Predicted Negative | Real Negative )} \end{aligned}$
$\begin{aligned} \text{PPV (Precision)} = \frac{\text{TP}}{\text{TP + FP}} = \frac{\text{TP}}{\text{Predicted Positives}} = \text{Prob( Real Positive | Predicted Positive )} \end{aligned}$
$\begin{aligned} \text{NPV} = \frac{\text{TN}}{\text{TN + FN}} = \frac{\text{TN}}{\text{Predicted Negatives}} = \text{Prob( Real Negative | Predicted Negative )} \end{aligned}$
$\begin{aligned} \text{Lift} = \frac{\text{PPV}}{\text{Prevalence}} = \frac{\cfrac{\text{TP}}{\text{TP + FP}}}{\cfrac{\text{TP + FN}}{\text{TP + FP + TN + FN}}} \end{aligned}$
$\begin{aligned} \text{Net Benefit} = \frac{\text{TP}}{\text{TP + FP + TN + FN}} - \frac{\text{FP}}{\text{TP + FP + TN + FN}} * {\frac{{p_{t}}}{{1 - p_{t}}}} \end{aligned}$
performance_dat <- prepare_performance_data(probs = params$probs, reals = params$reals)
create_table_for_prevalence(performance_dat)
Calibration {.tabset}
Smooth
create_calibration_curve(probs = params$probs, reals = params$reals, interactive = params$interactive, type = "smooth", size = 550)
Discrete
create_calibration_curve(probs = params$probs, reals = params$reals, interactive = params$interactive, size = 550)
Discrimination {.tabset}
create_table_for_auc(probs = params$probs, reals = params$reals)
By Probability Threshold
Performance Metrics Curves {.tabset}
ROC
plot_roc_curve(performance_dat, interactive = params$interactive, size = 500)
Lift
plot_lift_curve(performance_dat, interactive = params$interactive, size = 500)
Precision Recall
plot_precision_recall_curve(performance_dat, interactive = params$interactive, size = 500)
Gains
plot_gains_curve(performance_dat, interactive = params$interactive, size = 500)
By Predicted Positives Condition Rate (PPCR)
Performance Metrics Curves {.tabset}
ROC
performance_dat_ppcr <- prepare_performance_data(probs = params$probs, reals = params$reals, stratified_by = "ppcr")
plot_roc_curve(performance_dat_ppcr, interactive = params$interactive, size = 500)
Lift
plot_lift_curve(performance_dat_ppcr, interactive = params$interactive, size = 500)
Precision Recall
plot_precision_recall_curve(performance_dat_ppcr, interactive = params$interactive, size = 500)
Gains
plot_gains_curve(performance_dat_ppcr, interactive = params$interactive, size = 500)
Utility (Decision Curve)
plot_decision_curve(performance_dat, interactive = params$interactive, type = "combined", size = 500)
Performance Table {.tabset}
By Probability Threshold
render_performance_table(performance_dat)
By Predicted Positives Condition Rate (PPCR)
render_performance_table(performance_dat_ppcr)
```{css echo=FALSE}
.form-control { font-family: system-ui, sans-serif; font-size: 2rem; font-weight: bold; line-height: 1.1; display: grid; grid-template-columns: 1em auto; gap: 0.5em; place-content: center; }
.form-control + .form-control { margin-top: 1em; }
input[type="checkbox"] { -webkit-appearance: none; appearance: none; background-color: #fff; margin: 0; font: inherit; width: 1.15em; height: 1.15em; border: 0.15em solid currentColor; border-radius: 0.15em; transform: translateY(-0.075em); display: grid; place-content: center; }
input[type="checkbox"][value="1"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #1b9e77; background-color: CanvasText; }
input[type="checkbox"][value="2"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #d95f02; background-color: CanvasText; }
input[type="checkbox"][value="3"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #7570b3; background-color: CanvasText; }
input[type="checkbox"][value="4"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #e7298a; background-color: CanvasText; }
input[type="checkbox"][value="5"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #07004D; background-color: CanvasText; }
input[type="checkbox"][value="6"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #E6AB02; background-color: CanvasText; }
input[type="checkbox"][value="7"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #FE5F55; background-color: CanvasText; }
input[type="checkbox"][value="8"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #54494B; background-color: CanvasText; }
input[type="checkbox"][value="9"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #006E90; background-color: CanvasText; }
input[type="checkbox"][value="10"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #BC96E6; background-color: CanvasText; }
input[type="checkbox"][value="11"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #52050A; background-color: CanvasText; }
input[type="checkbox"][value="12"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #1F271B; background-color: CanvasText; }
input[type="checkbox"][value="13"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #BE7C4D; background-color: CanvasText; }
input[type="checkbox"][value="14"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #63768D; background-color: CanvasText; }
input[type="checkbox"][value="15"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #08A045; background-color: CanvasText; }
input[type="checkbox"][value="16"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #320A28; background-color: CanvasText; }
input[type="checkbox"][value="17"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #82FF9E; background-color: CanvasText; }
input[type="checkbox"][value="18"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #2176FF; background-color: CanvasText; }
input[type="checkbox"][value="19"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #D1603D; background-color: CanvasText; }
input[type="checkbox"][value="20"]::before { content: ""; width: 0.65em; height: 0.65em; transform: scale(0); box-shadow: inset 1em 1em #585123; background-color: CanvasText; }
input[type="checkbox"]:checked::before { transform: scale(1); }
```
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.