pif.conditional.variance.linear: Conditional Variance for the Potential Impact Fraction

Description Usage Arguments Author(s) See Also Examples

View source: R/pif_conditional_variance_linear.R

Description

Function that calculates the conditional variance of the potential impact fraction pif (linearization).

Usage

1
2
3
pif.conditional.variance.linear(X, thetahat, rr, cft = NA,
  weights = rep(1/nrow(as.matrix(X)), nrow(as.matrix(X))), check_cft = TRUE,
  is_paf = FALSE)

Arguments

X

Random sample (data.frame) which includes exposure and covariates.

thetahat

Estimator (vector) of theta for the Relative Risk function.

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.

weights

Survey weights for the random sample X.

check_cft

Check if counterfactual function cft reduces exposure.

is_paf

Force evaluation of paf

Author(s)

Rodrigo Zepeda-Tello [email protected]

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

See Also

pif.variance.linear for pif variance without conditioning on theta and pif.variance.loglinear for variance of log(pif)

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
#Example 1: Exponential Relative risk
#--------------------------------------------
set.seed(18427)
X        <- data.frame(rnorm(100,3,.5))
thetahat <- 0.12
rr       <- function(X, theta){exp(theta*X)}

#When no counterfactual is specified calculates PAF
pif.conditional.variance.linear(X, thetahat,  rr)

#Example with linear counterfactual
cft      <- function(X){0.3*X}
pif.conditional.variance.linear(X, thetahat,  rr = function(X, theta){exp(theta*X)}, cft)

#Example 2: Multivariate case
#--------------------------------------------
set.seed(18427)
X1 <- rnorm(100, 3,.5)
X2 <- runif(100, 1, 1.5)
X  <- data.frame(cbind(X1,X2))
thetahat  <- c(0.1, 0.03)
rr        <- function(X, theta){
           .X <- as.matrix(X, ncol = 2)
           exp(theta[1]*.X[,1] + theta[2]*.X[,2])
           }
cft <- function(X){0.5*X}
pif.conditional.variance.linear(X, thetahat, rr, cft) 

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