# rrcalc: Estimation of relative risk In HEAT: Health Effects of Air Pollution and Temperature (HEAT)

## Description

`rrcalc` caculates relative risks below and above a threshold. Relative risks and the 95% C.I.s of lower unit and upper unit based on the threshold are estimated.

## Usage

 `1` ```rrcalc(object, rrunit = 1) ```

## Arguments

 `object` a fitted `threshpt` object produced by `threshpt()`. `rrunit` Unit of relative risk.

## Details

In GLM with log link, the coefficients of the exposure are equal to log values of RR. `rrcalc` gives relative risks in log link GLM, particularly, Poisson regression model.

## Value

The results of "<Threshold" mean that the relative risk and 95% confidence interval when the exposure increases by `rrunit` below threshold. The results of ">=Threshold" mean those when the exposure increases by `rrunit` above threshold.
RR = exp(beta*rrunit) and 95% C.I = exp((beta-1.96*s.e(beta), beta+1.96*s.e(beta))*rrunit)

## Author(s)

Youn-Hee Lim, Il-Sang Ohn, and Ho Kim

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# read the Seoul data set and create lag variables data(mort) seoul = read6city(mort, 11) seoul_lag = lagdata(seoul, c("meantemp", "mintemp", "meanpm10", "meanhumi"), 5) # find a optimal threshold and conduct piecewise linear regression mythresh = threshpt(nonacc ~ meantemp_m3 + meanpm10_m2 + meanhumi + ns(sn, 4*10) + factor(dow), expvar = "meantemp_m3", family = "poisson", data = seoul_lag, startrng = 23, endrng = 33, searchunit = 0.2) # calculate relative risks rrcalc(mythresh) ```

HEAT documentation built on May 29, 2017, 10:52 a.m.