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updated desc, fixed 2 warnings

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agdamsbo 2019-11-26 14:11:37 +01:00
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DESCRIPTION
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Package: daDoctoR Package: daDoctoR
Type: Package Type: Package
Title: FUNCTIONS FOR HEALTH RESEARCH Title: Functions For Health Research.
Version: 0.1.0.9036 Version: 0.1.0.9037
Author: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut"))) Year: 2019
Author: person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut"))
Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me> Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
Description: I am a Danish medical doctor involved in neuropsychiatric research. Description: R functions for convenient data management an danalysis in health research.
Here I have collected functions I use for my data analysis. You are very
welcome to get inspired or to use my work.
Imports: broom, dplyr, epiR, ggplot2, MASS, carData, eulerr Imports: broom, dplyr, epiR, ggplot2, MASS, carData, eulerr
Suggest: shiny Suggest: shiny
License: GPL (>= 2) License: GPL (>= 2)

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R/rep_glm.R
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#' @param meas Effect meassure. Input as c() of columnnames, use dput(). #' @param meas Effect meassure. Input as c() of columnnames, use dput().
#' @param vars variables in model. Input as c() of columnnames, use dput(). #' @param vars variables in model. Input as c() of columnnames, use dput().
#' @param string variables to test. Input as c() of columnnames, use dput(). #' @param string variables to test. Input as c() of columnnames, use dput().
#' @param ci flag to get results as OR with 95% confidence interval. #' @param ci flag to get results as OR with 95 percent confidence interval.
#' @param data dataframe to pull variables from. #' @param data dataframe to pull variables from.
#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate logistic regression is performed. #' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate logistic regression is performed.
#' @keywords logistic #' @keywords logistic

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R/rep_lm.R
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#' @param meas Effect meassure. Input as c() of columnnames, use dput(). #' @param meas Effect meassure. Input as c() of columnnames, use dput().
#' @param vars variables in model. Input as c() of columnnames, use dput(). #' @param vars variables in model. Input as c() of columnnames, use dput().
#' @param string variables to test. Input as c() of columnnames, use dput(). #' @param string variables to test. Input as c() of columnnames, use dput().
#' @param ci flag to get results as OR with 95% confidence interval. #' @param ci flag to get results as OR with 95 percent confidence interval.
#' @param data data frame to pull variables from. #' @param data data frame to pull variables from.
#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate linear regression is performed. #' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate linear regression is performed.
#' @keywords linear regression #' @keywords linear regression

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R/rep_olr.R
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#' @param meas Effect meassure. Input as c() of columnnames, use dput(). #' @param meas Effect meassure. Input as c() of columnnames, use dput().
#' @param vars variables in model. Input as c() of columnnames, use dput(). #' @param vars variables in model. Input as c() of columnnames, use dput().
#' @param string variables to test. Input as c() of columnnames, use dput(). #' @param string variables to test. Input as c() of columnnames, use dput().
#' @param ci flag to get results as OR with 95% confidence interval. #' @param ci flag to get results as OR with 95 percent confidence interval.
#' @param data data frame to pull variables from. #' @param data data.frame to pull variables from.
#' @keywords olr #' @keywords olr
#' @export #' @export

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man/rep_glm.Rd
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@ -14,7 +14,7 @@ rep_glm(meas, vars = NULL, string, ci = FALSE, data,
\item{string}{variables to test. Input as c() of columnnames, use dput().} \item{string}{variables to test. Input as c() of columnnames, use dput().}
\item{ci}{flag to get results as OR with 95% confidence interval.} \item{ci}{flag to get results as OR with 95 percent confidence interval.}
\item{data}{dataframe to pull variables from.} \item{data}{dataframe to pull variables from.}

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man/rep_lm.Rd
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@ -14,7 +14,7 @@ rep_lm(meas, vars = NULL, string, ci = FALSE, data,
\item{string}{variables to test. Input as c() of columnnames, use dput().} \item{string}{variables to test. Input as c() of columnnames, use dput().}
\item{ci}{flag to get results as OR with 95% confidence interval.} \item{ci}{flag to get results as OR with 95 percent confidence interval.}
\item{data}{data frame to pull variables from.} \item{data}{data frame to pull variables from.}

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man/rep_olr.Rd
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@ -13,9 +13,9 @@ rep_olr(meas, vars, string, ci = FALSE, data)
\item{string}{variables to test. Input as c() of columnnames, use dput().} \item{string}{variables to test. Input as c() of columnnames, use dput().}
\item{ci}{flag to get results as OR with 95% confidence interval.} \item{ci}{flag to get results as OR with 95 percent confidence interval.}
\item{data}{data frame to pull variables from.} \item{data}{data.frame to pull variables from.}
} }
\description{ \description{
For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI. For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.