Tools for the analysis of epidemiological data.
epi.about()The following is a summary of the main functions and datasets in the epiR package. An alphabetical list of all functions and datasets is available by typing library(help = epiR).
For further information on any of these functions, type help(name) or ?name where name is the name of the function or dataset.
For details on how to use epR for routine epidemiological work start R, type help.start() to open the help browser and navigate to Packages > epiR > Vignettes.
The functions in epiR can be categorised as follows:
epi.conf |
Confidence intervals. |
epi.descriptives |
Descriptive statistics. |
epi.directadj |
Directly adjusted incidence rate estimates. |
epi.edr |
Compute estimated dissemination ratios from outbreak event data. |
epi.empbayes |
Empirical Bayes estimates of observed event counts. |
epi.indirectadj |
Indirectly adjusted incidence risk estimates. |
epi.insthaz |
Instantaneous hazard estimates based on Kaplan-Meier survival estimates. |
epi.2by2 |
Measures of association from data presented in a 2 by 2 table. |
epi.herdtest |
Estimate the characteristics of diagnostic tests applied at the herd (group) level. |
epi.nomogram |
Compute the post-test probability of disease given characteristics of a diagnostic test. |
epi.pooled |
Estimate herd test characteristics when samples are pooled. |
epi.prev |
Compute the true prevalence of a disease in a population on the basis of an imperfect test. |
epi.tests |
Sensitivity, specificity and predictive value of a diagnostic test. |
epi.dsl |
Mixed-effects meta-analysis of binary outcome data using the DerSimonian and Laird method. |
epi.iv |
Fixed-effects meta-analysis of binary outcome data using the inverse variance method. |
epi.mh |
Fixed-effects meta-analysis of binary outcome data using the Mantel-Haenszel method. |
epi.smd |
Fixed-effects meta-analysis of continuous outcome data using the standardised mean difference method. |
epi.cp |
Extract unique covariate patterns from a data set. |
epi.cpresids |
Compute covariate pattern residuals from a logistic regression model. |
epi.interaction |
Relative excess risk due to interaction in a case-control study. |
epi.asc |
Write matrix to an ASCII raster file. |
epi.betabuster |
An R version of Wes Johnson and Chun-Lung Su's Betabuster. |
epi.convgrid |
Convert British National Grid georeferences to easting and northing coordinates. |
epi.dms |
Convert decimal degrees to degrees, minutes and seconds and vice versa. |
epi.ltd |
Calculate lactation to date and standard lactation (that is, 305 or 270 day) milk yields. |
epi.offset |
Create an offset vector based on a list suitable for WinBUGS. |
epi.RtoBUGS |
Write data from an R list to a text file in WinBUGS-compatible format. |
The general naming convention for the sample size functions in epiR is: epi.ss (sample size) + an abbreviation to represent the sampling design (e.g. simple, strata, clus1, clus2) + an abbreviation of the objectives of the study (est when you want to estimate a population parameter or comp when you want to compare two groups) + a single letter defining the outcome variable type (b for binary, c for continuous and s for survival data).
| Sample size to estimate a binary outcome using simple random sampling. | |
epi.sssimpleestc |
Sample size to estimate a continous outcome using simple random sampling. |
epi.ssstrataestb |
Sample size to estimate a binary outcome using stratified random sampling. |
epi.ssstrataestc |
Sample size to estimate a continous outcome using stratified random sampling. |
epi.ssclus1estb |
Sample size to estimate a binary outcome using one-stage cluster sampling. |
epi.ssclus1estc |
Sample size to estimate a continuous outcome using one-stage cluster sampling. |
epi.ssclus2estb |
Sample size to estimate a binary outcome using two-stage cluster sampling. |
epi.ssclus2estc |
Sample size to estimate a continuous outcome using two-stage cluster sampling. |
epi.ssxsectn |
Sample size, power or detectable prevalence ratio for a cross-sectional study. |
epi.sscohortc |
Sample size, power or detectable risk ratio for a cohort study using count data. |
epi.sscohortt |
Sample size, power or detectable risk ratio for a cohort study using time at risk data. |
epi.sscc |
Sample size, power or detectable odds ratio for case-control studies. |
epi.sscompb |
Sample size, power and detectable risk ratio when comparing binary outcomes. |
epi.sscompc |
Sample size, power and detectable risk ratio when comparing continuous outcomes. |
epi.sscomps |
Sample size, power and detectable hazard when comparing time to event. |
epi.ssequb |
Sample size for a parallel equivalence trial, binary outcome. |
epi.ssequc |
Sample size for a parallel equivalence trial, continuous outcome. |
epi.sssupb |
Sample size for a parallel superiority trial, binary outcome. |
epi.sssupc |
Sample size for a parallel superiority trial, continuous outcome. |
epi.ssninfb |
Sample size for a non-inferiority trial, binary outcome. |
epi.ssninfc |
Sample size for a non-inferiority trial, continuous outcome. |
epi.ssdetect |
Sample size to detect an event. |
epi.epidural |
Rates of use of epidural anaesthesia in trials of caregiver support. |
epi.incin |
Laryngeal and lung cancer cases in Lancashire 1974 - 1983. |
epi.SClip |
Lip cancer in Scotland 1975 - 1980. |
The most recent version of the epiR package can be obtained from: https://fvas.unimelb.edu.au/research/groups/veterinary-epidemiology-melbourne