epiR - RSurveillance function mapping"

\setmainfont{Calibri Light}

# If you want to create a PDF document paste the following after line 9 above:
#   pdf_document:
#     toc: true
#     highlight: tango
#     number_sections: no
#     latex_engine: xelatex    
# header-includes: 
#    - \usepackage{fontspec}

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)

The following tables lists each of the functions in RSurveillance and their equivalent in epiR.

Representative sampling

Sample size estimation

library(pander)
panderOptions('table.split.table', Inf)

set.caption("Functions to estimate sample size using representative population sampling data.")

ssrs.tab <- " 
Sampling        | Outcome               | RSurveillance              | epiR
Representative  | Prob disease freedom  | `n.pfree`                  | `rsu.sspfree.rs`
Representative  | SSe                   | `n.freedom`                | `rsu.sssep.rs`
Two stage representative | SSe         | `n.2stage`                 | `rsu.sssep.rs2st`
Representative  | SSe                   | `n.freecalc`               | `rsu.sssep.rsfreecalc`
Pooled representative    | SSe                   | `n.pooled`                 | `rsu.sssep.rspool`"

ssrs.df <- read.delim(textConnection(ssrs.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(ssrs.df) <- unname(as.list(ssrs.df[1,])) # put headers on
ssrs.df <- ssrs.df[-1,] # remove first row
row.names(ssrs.df) <- NULL
pander(ssrs.df, style = 'rmarkdown')

Estimation of surveillance system sensitivity

set.caption("Functions to estimate surveillance system sensitivity (SSe) using representative population sampling data.")

seprs.tab <- " 
Sampling                  | Outcome      | RSurveillance        | epiR
Representative            | SSe          | `sep.binom`          | `rsu.sep.rs`
Representative            | SSe          | `sep.hypergeo`       | `rsu.sep.rs`
Representative            | SSe          | `sep`                | `rsu.sep.rs`
Two stage representative  | SSe          | `sep.sys`            | `rsu.sep.rs2st`
Representative            | SSe          | `sse.combined`       | `rsu.sep.rsmult`
Representative            | SSe          | `sep.freecalc`       | `rsu.sep.rsfreecalc`
Representative            | SSe          | `sep.binom.imperfect`| `rsu.sep.rsfreecalc`
Pooled representative     | SSe          | `sep.pooled`         | `rsu.sep.rspool`
Representative            | SSe          | `sep.var.se`         | `rsu.sep.rsvarse`
Representative            | SSp          | `spp`                | `rsu.spp.rs`
Representative            | SSp          | `sph.hp`             | `rsu.spp.rs`"

seprs.df <- read.delim(textConnection(seprs.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(seprs.df) <- unname(as.list(seprs.df[1,])) # put headers on
seprs.df <- seprs.df[-1,] # remove first row
row.names(seprs.df) <- NULL
pander(seprs.df, style = 'rmarkdown')

Estimation of the probability of disease freedom

set.caption("Functions to estimate the probability of disease freedom using representative population sampling data.")

pfreers.tab <- " 
Sampling          | Outcome                             |  RSurveillance      | epiR
Representative    | Prob disease of freedom             | `pfree.1`           | `rsu.pfree.rs`
Representative    | Prob disease of freedom             | `pfree.calc`        | `rsu.pfree.rs`
Representative    | Equilibrium prob of disease freedom | `pfree.equ`         | `rsu.pfree.equ`"

pfreers.df <- read.delim(textConnection(pfreers.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(pfreers.df) <- unname(as.list(pfreers.df[1,])) # put headers on
pfreers.df <- pfreers.df[-1,] # remove first row
row.names(pfreers.df) <- NULL
pander(pfreers.df, style = 'rmarkdown')

Risk based sampling

Sample size estimation

set.caption("Functions to estimate sample size using risk based sampling data.")

ssrb.tab <- " 
Sampling       | Outcome | RSurveillance        | epiR
Risk-based     | SSe     | `n.rb`               | `rsu.sssep.rbsrg`
Risk-based     | SSe     | `n.rb.varse`         | `rsu.sssep.rbmrg`
Risk-based     | SSe     | `n.rb.2stage.1`      | `rsu.sssep.rb2st1rf`
Risk-based     | SSe     | `n.rb.2stage.2`      | `rsu.sssep.rb2st2rf`"

ssrb.df <- read.delim(textConnection(ssrb.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(ssrb.df) <- unname(as.list(ssrb.df[1,])) # put headers on
ssrb.df <- ssrb.df[-1,] # remove first row
row.names(ssrb.df) <- NULL
pander(ssrb.df, style = 'rmarkdown')

Estimation of surveillance system sensitivity

set.caption("Functions to estimate surveillance system sensitivity (SSe) using risk based sampling data.")

seprb.tab <- " 
Sampling       | Outcome | RSurveillance           | epiR
Risk-based     | SSe     | `sep.rb.bin.varse`      | `rsu.sep.rb`
Risk-based     | SSe     | `sep.rb.bin`            | `rsu.sep.rb1rf`
Risk-based     | SSe     | `sep.rb.hypergeo`       | `rsu.sep.rb1rf`
Risk-based     | SSe     | `sep.rb2.bin`           | `rsu.sep.rb2rf`
Risk-based     | SSe     | `sep.rb2.hypergeo`      | `rsu.sep.rb2rf`
Risk-based     | SSe     | `sep.rb.hypergeo.varse` | `rsu.sep.rbvarse`
Risk-based     | SSe     | `sse.rb2stage`          | `rsu.sep.rb2stage`"
seprb.df <- read.delim(textConnection(seprb.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(seprb.df) <- unname(as.list(seprb.df[1,])) # put headers on
seprb.df <- seprb.df[-1,] # remove first row
row.names(seprb.df) <- NULL
pander(seprb.df, style = 'rmarkdown')

Census data

Estimation of surveillance system sensitivity

set.caption("Functions to estimate surveillance system sensitivity (SSe) using census data.")

sepcen.tab <- " 
Sampling       | Outcome | RSurveillance           | epiR
Risk-based     | SSe     | `sep.exact`             | `rsu.sep.cens`"
sepcen.df <- read.delim(textConnection(sepcen.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(sepcen.df) <- unname(as.list(sepcen.df[1,])) # put headers on
sepcen.df <- sepcen.df[-1,] # remove first row
row.names(sepcen.df) <- NULL
pander(sepcen.df, style = 'rmarkdown')

Passive surveillance data

set.caption("Functions to estimate surveillance system sensitivity (SSe) using passively collected surveillance data.")

sepcen.tab <- " 
Sampling       | Outcome | RSurveillance           | epiR
Risk-based     | SSe     | `sep.passive`           | `rsu.sep.pass`"
seppas.df <- read.delim(textConnection(sepcen.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(seppas.df) <- unname(as.list(seppas.df[1,])) # put headers on
seppas.df <- seppas.df[-1,] # remove first row
row.names(seppas.df) <- NULL
pander(seppas.df, style = 'rmarkdown')

Miscellaneous functions

set.caption("Miscellaneous functions.")

misc.tab <- " 
Details                             | RSurveillance           | epiR
Adjusted risk                       | `adj.risk`              | `rsu.adjrisk`
Adjusted risk                       | `adj.risk.sim`          | `rsu.adjrisk`
Series test interpretation, Se      | `se.series`             | `rsu.dxtest`
Parallel test interpretation, Se    | `se.parallel`           | `rsu.dxtest`

Series test interpretation, Sp      | `sp.series`             | `rsu.dxtest`
Parallel test interpretation, Sp    | `sp.parallel`           | `rsu.dxtest`

Effective probability of infection  | `epi.calc`              | `rsu.epinf`
Design prevalence back calculation  | `pstar.calc`            | `rsu.pstar`
Prob disease is less than design prevalence |                 | `rsu.sep`"
misc.df <- read.delim(textConnection(misc.tab), header = FALSE, sep = "|", strip.white = TRUE, stringsAsFactors = FALSE)

names(misc.df) <- unname(as.list(misc.df[1,])) # put headers on
misc.df <- misc.df[-1,] # remove first row
row.names(misc.df) <- NULL
pander(misc.df, style = 'rmarkdown')


Try the epiR package in your browser

Any scripts or data that you put into this service are public.

epiR documentation built on Nov. 11, 2021, 1:10 a.m.