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updated desc, fixed 2 warnings
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DESCRIPTION
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DESCRIPTION
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Package: daDoctoR
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Package: daDoctoR
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Type: Package
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Type: Package
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Title: FUNCTIONS FOR HEALTH RESEARCH
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Title: Functions For Health Research.
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Version: 0.1.0.9036
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Version: 0.1.0.9037
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Author: c(person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut")))
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Year: 2019
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Author: person("Andreas", "Gammelgaard Damsbo", email = "agdamsbo@pm.me", role = c("cre", "aut"))
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Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
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Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@pm.me>
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Description: I am a Danish medical doctor involved in neuropsychiatric research.
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Description: R functions for convenient data management an danalysis in health research.
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Here I have collected functions I use for my data analysis. You are very
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welcome to get inspired or to use my work.
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Imports: broom, dplyr, epiR, ggplot2, MASS, carData, eulerr
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Imports: broom, dplyr, epiR, ggplot2, MASS, carData, eulerr
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Suggest: shiny
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Suggest: shiny
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License: GPL (>= 2)
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License: GPL (>= 2)
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#' @param meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param ci flag to get results as OR with 95% confidence interval.
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#' @param ci flag to get results as OR with 95 percent confidence interval.
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#' @param data dataframe to pull variables from.
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#' @param data dataframe to pull variables from.
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#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate logistic regression is performed.
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#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate logistic regression is performed.
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#' @keywords logistic
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#' @keywords logistic
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#' @param meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param ci flag to get results as OR with 95% confidence interval.
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#' @param ci flag to get results as OR with 95 percent confidence interval.
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#' @param data data frame to pull variables from.
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#' @param data data frame to pull variables from.
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#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate linear regression is performed.
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#' @param fixed.var flag to set "vars" as fixed in the model. When FALSE, then true bivariate linear regression is performed.
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#' @keywords linear regression
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#' @keywords linear regression
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#' @param meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param meas Effect meassure. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param vars variables in model. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param string variables to test. Input as c() of columnnames, use dput().
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#' @param ci flag to get results as OR with 95% confidence interval.
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#' @param ci flag to get results as OR with 95 percent confidence interval.
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#' @param data data frame to pull variables from.
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#' @param data data.frame to pull variables from.
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#' @keywords olr
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#' @keywords olr
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#' @export
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#' @export
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@ -14,7 +14,7 @@ rep_glm(meas, vars = NULL, string, ci = FALSE, data,
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\item{string}{variables to test. Input as c() of columnnames, use dput().}
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\item{string}{variables to test. Input as c() of columnnames, use dput().}
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\item{ci}{flag to get results as OR with 95% confidence interval.}
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\item{ci}{flag to get results as OR with 95 percent confidence interval.}
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\item{data}{dataframe to pull variables from.}
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\item{data}{dataframe to pull variables from.}
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@ -14,7 +14,7 @@ rep_lm(meas, vars = NULL, string, ci = FALSE, data,
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\item{string}{variables to test. Input as c() of columnnames, use dput().}
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\item{string}{variables to test. Input as c() of columnnames, use dput().}
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\item{ci}{flag to get results as OR with 95% confidence interval.}
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\item{ci}{flag to get results as OR with 95 percent confidence interval.}
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\item{data}{data frame to pull variables from.}
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\item{data}{data frame to pull variables from.}
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@ -13,9 +13,9 @@ rep_olr(meas, vars, string, ci = FALSE, data)
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\item{string}{variables to test. Input as c() of columnnames, use dput().}
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\item{string}{variables to test. Input as c() of columnnames, use dput().}
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\item{ci}{flag to get results as OR with 95% confidence interval.}
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\item{ci}{flag to get results as OR with 95 percent confidence interval.}
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\item{data}{data frame to pull variables from.}
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\item{data}{data.frame to pull variables from.}
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}
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}
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\description{
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\description{
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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.
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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.
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