KeidingMonotonicity: Estimating Force of Infection, According to Keiding

In TeaKov/serostat: Modeling Infectious Disease Parameters Based on Serological and Social Contact Data R Package

Description

Keiding estimates the force of infection using the PAVA algorithm (see help("pavit")).

Usage

KeidingMonotonicity(p)

Arguments

p	This is a list containing the following parameters: pos describes the amount of successes out of “tot” trials, tot is the total amount of trials, grid gives back the rounded (and unique) age groups, and bw that describes the bandwidth. Note that both “pos” and “tot” should be ordered by age.

Examples

# Using Belgian B19 data
data("VZV_B19_BE_0103")
subset<-(VZV_B19_BE_0103$age>0.5)&(VZV_B19_BE_0103$age<76)&
  (!is.na(VZV_B19_BE_0103$age))&!is.na(VZV_B19_BE_0103$parvores)
VZV_B19_BE_0103<-VZV_B19_BE_0103[subset,]
y<-VZV_B19_BE_0103$parvores[order(VZV_B19_BE_0103$age)]
a<-VZV_B19_BE_0103$age[order(VZV_B19_BE_0103$age)]

grid<-sort(unique(round(a)))
neg<-table(y,round(a))[1,]
pos<-table(y,round(a))[2,]
tot<-neg+pos

params <- list(pos=pos, tot=tot, grid=grid, bw=10)

KeidingMonotonicity(params)

# Using Bulgarian HAV data
data("HAV_BUL_64_BUL_64")
a<-c(rep(HAV_BUL_64$Age,HAV_BUL_64$Pos),rep(HAV_BUL_64$Age,HAV_BUL_64$Tot-HAV_BUL_64$Pos))
y<-c(rep(rep(1,length(HAV_BUL_64$Age)),HAV_BUL_64$Pos),
  rep(rep(0,length(HAV_BUL_64$Age)),HAV_BUL_64$Tot-HAV_BUL_64$Pos))
y<-y[order(a)]
a<-a[order(a)]

grid<-sort(unique(round(a)))
neg<-HAV_BUL_64$Tot-HAV_BUL_64$Pos
pos<-HAV_BUL_64$Pos
tot<-neg+pos

params <- list(pos=pos, tot=tot, grid=grid, bw=30)

KeidingMonotonicity(params)

TeaKov/serostat documentation built on May 21, 2019, 1:21 p.m.