pif.confidence.approximate: Approximate Confidence Intervals for the Population...

Description Usage Arguments Author(s) Examples

View source: R/pif_confidence_approximate.R

Description

Function that calculates approximate confidence intervals to the population attributable fraction

Usage

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pif.confidence.approximate(Xmean, Xvar, thetahat, thetavar, rr,
  cft = function(Xmean) {     matrix(0, ncol = ncol(as.matrix(Xmean)), nrow =
  nrow(as.matrix(Xmean))) }, check_thetas = TRUE, check_cft = TRUE,
  check_xvar = TRUE, check_rr = TRUE, check_integrals = TRUE,
  check_exposure = TRUE, deriv.method.args = list(),
  deriv.method = c("Richardson", "complex"), nsim = 1000, confidence = 95,
  is_paf = FALSE)

Arguments

Xmean

Mean value of exposure levels.

Xvar

Variance of exposure levels.

thetahat

Estimator (vector or matrix) of theta for the Relative Risk function rr

thetavar

Estimator of variance of thetahat

rr

Function for Relative Risk which uses parameter theta. The order of the parameters shound be rr(X, theta).

**Optional**

cft

Function cft(X) for counterfactual. Leave empty for the Population Attributable Fraction paf where counterfactual is 0 exposure.

check_thetas

Checks that theta parameters are correctly inputed

check_cft

Check if counterfactual function cft reduces exposure.

check_xvar

Check if it is covariance matrix.

check_rr

Check that Relative Risk function rr equals 1 when evaluated at 0

check_integrals

Check that counterfactual and relative risk's expected values are well defined for this scenario.

check_exposure

Check that exposure X is positive and numeric

deriv.method.args

method.args for hessian.

deriv.method

method for hessian. Don't change this unless you know what you are doing.

nsim

Number of simulations for estimation of variance

confidence

Concidence level (0 to 100) default = 95 %

is_paf

Force evaluation of paf

Author(s)

Rodrigo Zepeda-Tello [email protected]

Dalia Camacho-Garc<c3><ad>a-Forment<c3><ad> [email protected]

Examples

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## Not run: 
#Example 1: Exponential Relative risk
#--------------------------------------------
set.seed(46987)
rr      <- function(X,theta){exp(X*theta)}
cft     <- function(X){0.5*X}
X       <- runif(1000)
Xmean   <- data.frame(mean(X))
Xvar    <- var(X)
theta   <-  0.2
thetavar <- 0.015
pif.confidence.approximate(Xmean, Xvar, theta, thetavar, rr)
pif.confidence.approximate(Xmean, Xvar, theta, thetavar, rr, cft) 

#Example 2: Multivariate example
#--------------------------------------------
X1       <- rnorm(1000,3,.5)
X2       <- rnorm(1000,4,1)
X        <- as.matrix(cbind(X1,X2))
Xmean    <- data.frame(t(colMeans(X)))
Xvar     <- cov(X)
theta    <- c(0.12, 0.17)
thetavar  <- matrix(c(0.001, 0.00001, 0.00001, 0.004), byrow = TRUE, nrow = 2)
rr       <- function(X, theta){exp(theta[1]*X[,1] + theta[2]*X[,2])}
pif.confidence.approximate(Xmean, Xvar, theta, thetavar, rr, 
cft = function(X){cbind(0.5*X[,1],0.4*X[,2])})

## End(Not run)

pifpaf documentation built on Sept. 29, 2017, 1:03 a.m.