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(v0.7.1.9027) tibble printing
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@ -8,14 +8,12 @@
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\title{Predict antimicrobial resistance}
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\usage{
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resistance_predict(x, col_ab, col_date = NULL, year_min = NULL,
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year_max = NULL, year_every = 1, minimum = 30,
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model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
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info = TRUE, ...)
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year_max = NULL, year_every = 1, minimum = 30, model = NULL,
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I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)
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rsi_predict(x, col_ab, col_date = NULL, year_min = NULL,
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year_max = NULL, year_every = 1, minimum = 30,
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model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
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info = TRUE, ...)
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year_max = NULL, year_every = 1, minimum = 30, model = NULL,
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I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)
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\method{plot}{resistance_predict}(x,
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main = paste("Resistance Prediction of", x_name), ...)
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@ -38,7 +36,7 @@ ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
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\item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
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\item{model}{the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution (i.e. using \code{\link{glm}(..., family = \link{binomial})}), 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.}
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\item{model}{the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using \code{\link{glm}(..., family = \link{binomial})}), 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 all valid options.}
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\item{I_as_S}{a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R})}
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