paf.plot: Plot of Population Attributable Fraction under different...

In INSP-RH/pif: Potential Impact Fraction and Population Attributable Fraction for Cross-Sectional Data

Description

Function that plots the paf under different values of a univariate parameter theta of the Relative Risk function rr which depends on the exposure X and a theta parameter (rr(X, theta))

Usage

paf.plot(X, thetalow, thetaup, rr, method = "empirical",
  confidence_method = "bootstrap", confidence = 95, nsim = 100,
  weights = rep(1/nrow(as.matrix(X)), nrow(as.matrix(X))), mpoints = 100,
  adjust = 1, n = 512, Xvar = var(X), deriv.method.args = list(),
  deriv.method = "Richardson", ktype = "gaussian", bw = "SJ",
  colors = c("deepskyblue", "gray25"), xlab = "Theta", ylab = "PAF",
  title = "Population Attributable Fraction (PAF) under different values of theta",
  check_exposure = TRUE, check_rr = TRUE, check_integrals = TRUE)

Arguments

X	Random sample (data.frame) which includes exposure and covariates or sample mean if "approximate" method is selected.
thetalow	Minimum of theta (parameter of relative risk rr) for plot.
thetaup	Maximum of theta (parameter of relative risk rr) for plot.
rr	function for Relative Risk which uses parameter theta. The order of the parameters should be rr(X, theta). **Optional**
method	Either "empirical" (default), "kernel" or "approximate". For details on estimation methods see pif.
confidence_method	Either bootstrap (default) inverse, one2one, linear, loglinear. See paf details for additional information.
confidence	Confidence level % (default 95).
nsim	Number of simulations to generate confidence intervals.
weights	Normalized survey weights for the sample X.
mpoints	Number of points in plot.
adjust	Adjust bandwith parameter (for "kernel" method) from density.
n	Number of equally spaced points at which the density (for "kernel" method) is to be estimated (see density).
Xvar	Variance of exposure levels (for "approximate" method).
deriv.method.args	method.args for hessian (for "approximate" method).
deriv.method	method for hessian. Don't change this unless you know what you are doing (for "approximate" method).
ktype	kernel type: "gaussian", "epanechnikov", "rectangular", "triangular", "biweight", "cosine", "optcosine" (for "kernel" method). Additional information on kernels in density.
bw	Smoothing bandwith parameter (for "kernel" method) from density. Default "SJ".
colors	vector Colors of plot.
xlab	string Label of x-axis in plot.
ylab	string Label of y-axis in plot.
title	string Title of plot.
check_exposure	boolean Check that exposure X is positive and numeric.
check_rr	boolean Check that Relative Risk function rr equals 1 when evaluated at 0.
check_integrals	boolean Check that counterfactual cft and relative risk's rr expected values are well defined for this scenario.

Value

paf.plot ggplot object with plot of Population Attributable Fraction as function of theta.

Author(s)

Rodrigo Zepeda-Tello [e-mail]

Dalia Camacho-García-Formentí [e-mail]

See Also

See paf for Population Attributable Fraction estimation with confidence intervals paf.confidence.

See pif.plot for same plot with Potential Impact Fraction pif.

Examples

## Not run: 
#Example 1: Exponential Relative Risk empirical method
#-----------------------------------------------------
set.seed(18427)
X <- data.frame(Exposure = rbeta(25, 4.2, 10))
paf.plot(X, thetalow = 0, thetaup = 2, function(X, theta){exp(theta*X)})


#Same example with kernel method
paf.plot(X, 0, 2, function(X, theta){exp(theta*X)}, method = "kernel",
title = "Kernel method example") 
 
#Same example for approximate method
Xmean <- data.frame(mean(X[,"Exposure"]))
Xvar  <- var(X)
paf.plot(Xmean, 0, 2, function(X, theta){exp(theta*X)}, 
method = "approximate", Xvar = Xvar, title = "Approximate method example")

## End(Not run)

INSP-RH/pif documentation built on May 7, 2019, 6:01 a.m.