Package 'predtools'

Calculates the absolute surface between the empirical and expected ROCs

Description

Calculates the absolute surface between the empirical and expected ROCs

Usage

calc_mROC_stats(y, p, ordered = FALSE, fast = TRUE)

Arguments

y	y vector of binary responses
p	p vector of predicted probabilities (same length as y)
ordered	defaults to false
fast	defaults to true

Value

Returns a list with the A (mean calibration statistic) and B (mROC/ROC equality statistic) as well as the direction of poential miscalibration (sign of the difference between the ctual and predicted mean risk)

---

Calculates the first two moments of the bivariate distribution of NB_model and NB_all

Description

Calculates the first two moments of the bivariate distribution of NB_model and NB_all

Usage

calc_NB_moments(Y, pi, z, weights = NULL)

Arguments

Y	Vector of the binary response variable
pi	Vector of predicted risks
z	Decision threshold at which the NBs are calculated
weights	Optinal - observation weights

Value

Two means, two SDs, and one correlation coefficient. First element is for the model and second is for treating all

---

Title Create calibration plot based on observed and predicted outcomes.

Description

Title Create calibration plot based on observed and predicted outcomes.

Usage

calibration_plot(
  data,
  obs,
  follow_up = NULL,
  pred,
  group = NULL,
  nTiles = 10,
  legendPosition = "right",
  title = NULL,
  x_lim = NULL,
  y_lim = NULL,
  xlab = "Prediction",
  ylab = "Observation",
  points_col_list = NULL,
  data_summary = FALSE
)

Arguments

data	Data include observed and predicted outcomes.
obs	Name of observed outcome in the input data.
follow_up	Name of follow-up time (if applicable) in the input data.
pred	Name of first predicted outcome in the input data.
group	Name of grouping column (if applicable) in the input data.
nTiles	Number of tiles (e.g., 10 for deciles) in the calibration plot.
legendPosition	Legend position on the calibration plot.
title	Title on the calibration plot.
x_lim	Limits of x-axis on the calibration plot.
y_lim	Limits of y-axis on the calibration plot.
xlab	Label of x-axis on the calibration plot.
ylab	Label of y-axis on the calibration plot.
points_col_list	Points' color on the calibration plot.
data_summary	Logical indicates whether a summary of the predicted and observed outcomes. needs to be included in the output.

Value

Returns calibration plot (a ggplot object) and a dataset including summary statistics of the predicted and observed outcomes (if data_summary set to be TRUE).

Examples

library(predtools)
library(dplyr)
x <- rnorm(100, 10, 2)
y <- x + rnorm(100,0, 1)
data <- data.frame(x, y)
calibration_plot(data, obs = "x", pred = "y")

---

model development data

Description

A dataset containing sample model development data

Format

A data frame with 500 rows and 5 variables:

• ageage

• severitywhether or not the disease was severe

• sexbinary sex variable, 1 for female and 0 for male

• comorbiditywhether or not comorbidities are present

• yresponse variable

Source

Simulated

---

EVPI (Expected Value of Perfect Information) for validation Takes a vector of mean and a 2X2 covariance matrix

Description

EVPI (Expected Value of Perfect Information) for validation Takes a vector of mean and a 2X2 covariance matrix

Usage

evpi_val(
  Y,
  pi,
  method = c("bootstrap", "bayesian_bootstrap", "asymptotic"),
  n_sim = 1000,
  zs = (0:99)/100,
  weights = NULL
)

Arguments

Y	Binary response variable
pi	Mean of the second distribution
method	EVPI calculation method
n_sim	Number of Monte Carlo simulations (for bootstrap-based methods)
zs	vector of risk thresholds at which EVPI is to be calculated
weights	(optional) observation weights

Value

Returns a data frame containing thresholds, EVPIs, and some auxilary output.

---

Anonymized data from the gusto trial

Description

A dataset containing anonymized data from the gusto trial

Format

A data frame with 40830 rows and 29 variables:

• day30whether death happened by day 30 after intervention

• showhether cardiac shock was present

• higwhether the patient hat high blood pressure

• diawhether the patient had diabetes

• hrtwhether the patient was on hormone replacement therapies

Source

Internet

---

Takes in a mROC object and calculates the area under the curve

Description

Takes in a mROC object and calculates the area under the curve

Usage

mAUC(mROC_obj)

Arguments

mROC_obj

An object of class mROC

Value

Returns the area under the mROC curve

---

Calculates mROC from the vector of predicted risks Takes in a vector of probabilities and returns mROC values (True positives, False Positives in an object of class mROC)

Description

Calculates mROC from the vector of predicted risks Takes in a vector of probabilities and returns mROC values (True positives, False Positives in an object of class mROC)

Usage

mROC(p, ordered = FALSE)

Arguments

p	A numeric vector of probabilities.
ordered	Optional, if the vector p is ordered from small to large (if not the function will do it; TRUE is to facilitate fast computations).

Value

This function returns an object of class mROC. It has three vectors: thresholds on predicted risks (which is the ordered vector of input probabilities), false positive rates (FPs), and true positive rates (TPs). You can directly call the plot function on this object to draw the mROC

---

Main eROC analysis that plots ROC and eROC

Description

Main eROC analysis that plots ROC and eROC

Usage

mROC_analysis(y, p, inference = 0, n_sim, fast = TRUE)

Arguments

y	y vector of observed responses.
p	p vector of predicted probabilities (the same length as observed responses)
inference	0 for no inference, 1 for p-value only, and 2 for p-value and 95 percent CI.
n_sim	number of simulations
fast	defaults to true

Value

returns a list containing the results of mROC analysis.

---

Statistical inference for comparing empirical and expected ROCs. If CI=TRUE then also returns pointwise CIs

Description

Statistical inference for comparing empirical and expected ROCs. If CI=TRUE then also returns pointwise CIs

Usage

mROC_inference(y, p, n_sim = 1e+05, CI = FALSE, aux = FALSE, fast = TRUE)

Arguments

y	vector of binary response values
p	vector of probabilities
n_sim	number of Monte Carlo simulations to calculate p-value
CI	optional. Whether confidence interval should be calculated for each point of mROC. Default is FALSE.
aux	aux optional. whether additional results (component-wise p-values etc) should be written in the package's aux variable. Default is FALSE.
fast	fast optional. Whether the fast code (C++) or slow code (R) should be called. Default is TRUE (R code will be slow unless the dataset is small)

Value

Returns an object of type mROC_inference containing the results of statistical inference for the mROC curve

---

Calculates the expected value of the maximum of two random variables with zero-truncated bivariate normal distribution Takes a vector of mean and a 2X2 covariance matrix

Description

Calculates the expected value of the maximum of two random variables with zero-truncated bivariate normal distribution Takes a vector of mean and a 2X2 covariance matrix

Usage

mu_max_trunc_bvn(
  mu1,
  mu2,
  sigma1,
  sigma2,
  rho,
  precision = .Machine$double.eps
)

Arguments

mu1	Mean of the first distribution
mu2	Mean of the second distribution
sigma1	SD of the first distribution
sigma2	SD of the second distribution
rho	Correlation coefficient of the two random variables
precision	Numerical precision value

Value

A scalar value for the expected value

---

Title Update a prediction model for a binary outcome by multiplying a fixed odd-ratio to the predicted odds.

Description

Title Update a prediction model for a binary outcome by multiplying a fixed odd-ratio to the predicted odds.

Usage

odds_adjust(p0, p1, v)

Arguments

p0	Mean of observed risk or predicted risk in development sample.
p1	Mean of observed risk in target population.
v	Variance of predicted risk in development sample.

Value

Returns a correction factor that can be applied to the predicted odds in order to update the predictions for a new target population.

---

Title Estimate mean and variance of prediction based on model calibration output.

Description

Title Estimate mean and variance of prediction based on model calibration output.

Usage

pred_summary_stat(calibVector)

Arguments

calibVector

Vector of predicted probability of risk per decile or percentile (e.g., from a calibration plot).

Value

Returns mean and variance of predictions based on the predicted probabilities.

---

model validation data

Description

A dataset containing sample model validation data

Format

A data frame with 400 rows and 5 variables:

• ageage of the patient

• severitywhether or not the disease was severe

• sexbinary sex variable, 1 for female and 0 for male

• comorbiditywhether or not comorbidities are present

• yresponse variable

Source

Simulated

---