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as.rsi warning, site update

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dr. M.S. (Matthijs) Berends
2019-02-09 22:16:24 +01:00
parent ed30312048
commit c56b179857
73 changed files with 735 additions and 421 deletions
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man/resistance_predict.Rd
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@ -38,7 +38,7 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
\item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
\item{model}{the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} (or \code{"poisson"}) or \code{"linear"} (or \code{"lin"}).}
\item{model}{the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.}
\item{I_as_R}{a logical to indicate whether values \code{I} should be treated as \code{R}}
@ -69,6 +69,14 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
\description{
Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
}
\details{
Valid options for the statistical model are:
\itemize{
\item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution}
\item{\code{"loglin"} or \code{"poisson"}: a generalised log-linear regression model with poisson distribution}
\item{\code{"lin"} or \code{"linear"}: a linear regression model}
}
}
\section{Read more on our website!}{
\if{html}{\figure{logo.png}{options: height=40px style=margin-bottom:5px} \cr}