Description Usage Arguments Author(s) Examples
View source: R/pif_confidence_loglinear.R
Confidence intervals for the Potencial Impact Fraction for relative risk inyective functions, the pif is inyective, and intervals can be calculated for log(pif), and then transformed to pif CI.
1 2 3 4 |
X |
Random sample (can be vector or matrix) which includes exposure and covariates. |
thetahat |
Estimative of |
thetavar |
Estimator of variance of thetahat |
rr |
Function for Relative Risk which uses parameter
**Optional** |
cft |
Function |
weights |
Survey |
nsim |
Number of simulations for estimation of variance. |
confidence |
Confidence level % (default 95) |
check_thetas |
Check that theta parameters are correctly inputed |
check_exposure |
Check that exposure |
check_cft |
Check if counterfactual function |
is_paf |
Force evaluation of paf |
Rodrigo Zepeda-Tello rzepeda17@gmail.com
Dalia Camacho-Garc<c3><ad>a-Forment<c3><ad> daliaf172@gmail.com
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 <- as.data.frame(rnorm(100,5,1))
thetahat <- 0.4
thetavar <- 0.1
cft <- function(X){sqrt(X)}
pif.confidence.loglinear(X, thetahat, thetavar, rr = function(X, theta){exp(theta*X)}, cft)
#Same example with linear counterfactual
a <- 0.5
cft <- function(X){a*X}
pif.confidence.loglinear(X, thetahat, thetavar, function(X, theta){exp(theta*X)})
#' #Example 2: Multivariate Relative Risk
#--------------------------------------------
set.seed(18427)
X1 <- rnorm(100, 4,0.01)
X2 <- runif(100,0.4,2)
X <- as.data.frame(cbind(X1,X2))
thetahat <- c(0.12, 0.03)
thetavar <- matrix(c(0.01, 0, 0, 0.04), byrow = TRUE, nrow = 2)
rr <- function(X, theta){
.X <- as.matrix(X, ncol = 2)
exp(theta[1]*.X[,1] + theta[2]*.X[,2])
}
pif.confidence.loglinear(X, thetahat, thetavar, rr)
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