# Estimation of relative risk

### 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 |

`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)
``` |

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