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make rsi
work in more cases, documentation update
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#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns `se_min` and `se_max`. See *Examples* for a real live example.
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#' @param object model data to be plotted
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#' @param col_ab column name of `x` containing antimicrobial interpretations (`"R"`, `"I"` and `"S"`)
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#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
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#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already - the default is the first column of with a date class
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#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in `col_date`
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#' @param year_max highest year to use in the prediction model, defaults to 10 years after today
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#' @param year_max highest year to use in the prediction model - the default is 10 years after today
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#' @param year_every unit of sequence between lowest year found in the data and `year_max`
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#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
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#' @param model the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = 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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