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< title > Predict antimicrobial resistance — resistance_predict • AMR (for R)< / title >
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< meta property = "og:description" content = "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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< h1 > Predict antimicrobial resistance< / h1 >
< div class = "hidden name" > < code > resistance_predict.Rd< / code > < / div >
< / div >
< div class = "ref-description" >
< p > 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< / code > and < code > se_max< / code > . See Examples for a real live example.< / p >
< / div >
< pre class = "usage" > < span class = 'fu' > resistance_predict< / span > (< span class = 'no' > tbl< / span > , < span class = 'no' > col_ab< / span > , < span class = 'no' > col_date< / span > , < span class = 'kw' > year_min< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > ,
< span class = 'kw' > year_max< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > , < span class = 'kw' > year_every< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 1< / span > , < span class = 'kw' > minimum< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 30< / span > ,
< span class = 'kw' > model< / span > < span class = 'kw' > =< / span > < span class = 'st' > "binomial"< / span > , < span class = 'kw' > I_as_R< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > , < span class = 'kw' > preserve_measurements< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > ,
< span class = 'kw' > info< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > )
< span class = 'fu' > rsi_predict< / span > (< span class = 'no' > tbl< / span > , < span class = 'no' > col_ab< / span > , < span class = 'no' > col_date< / span > , < span class = 'kw' > year_min< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > , < span class = 'kw' > year_max< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > ,
< span class = 'kw' > year_every< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 1< / span > , < span class = 'kw' > minimum< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 30< / span > , < span class = 'kw' > model< / span > < span class = 'kw' > =< / span > < span class = 'st' > "binomial"< / span > , < span class = 'kw' > I_as_R< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > ,
< span class = 'kw' > preserve_measurements< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > , < span class = 'kw' > info< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > )< / pre >
< h2 class = "hasAnchor" id = "arguments" > < a class = "anchor" href = "#arguments" > < / a > Arguments< / h2 >
< table class = "ref-arguments" >
< colgroup > < col class = "name" / > < col class = "desc" / > < / colgroup >
< tr >
< th > tbl< / th >
< td > < p > a < code > data.frame< / code > containing isolates.< / p > < / td >
< / tr >
< tr >
< th > col_ab< / th >
< td > < p > column name of < code > tbl< / code > with antimicrobial interpretations (< code > R< / code > , < code > I< / code > and < code > S< / code > )< / p > < / td >
< / tr >
< tr >
< th > col_date< / th >
< td > < p > column name of the date, will be used to calculate years if this column doesn't consist of years already< / p > < / td >
< / tr >
< tr >
< th > year_min< / th >
< td > < p > lowest year to use in the prediction model, dafaults the lowest year in < code > col_date< / code > < / p > < / td >
< / tr >
< tr >
< th > year_max< / th >
< td > < p > highest year to use in the prediction model, defaults to 15 years after today< / p > < / td >
< / tr >
< tr >
< th > year_every< / th >
< td > < p > unit of sequence between lowest year found in the data and < code > year_max< / code > < / p > < / td >
< / tr >
< tr >
< th > minimum< / th >
< td > < p > minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.< / p > < / td >
< / tr >
< tr >
< th > model< / th >
< td > < p > the statistical model of choice. Valid values are < code > "binomial"< / code > (or < code > "binom"< / code > or < code > "logit"< / code > ) or < code > "loglin"< / code > or < code > "linear"< / code > (or < code > "lin"< / code > ).< / p > < / td >
< / tr >
< tr >
< th > I_as_R< / th >
< td > < p > treat < code > I< / code > as < code > R< / code > < / p > < / td >
< / tr >
< tr >
< th > preserve_measurements< / th >
< td > < p > logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be < code > NA< / code > .< / p > < / td >
< / tr >
< tr >
< th > info< / th >
< td > < p > print textual analysis with the name and < code > < a href = 'https://www.rdocumentation.org/packages/base/topics/summary' > summary< / a > < / code > of the model.< / p > < / td >
< / tr >
< / table >
< h2 class = "hasAnchor" id = "value" > < a class = "anchor" href = "#value" > < / a > Value< / h2 >
< p > < code > data.frame< / code > with columns:< / p > < ul >
< li > < p > < code > year< / code > < / p > < / li >
< li > < p > < code > value< / code > , the same as < code > estimated< / code > when < code > preserve_measurements = FALSE< / code > , and a combination of < code > observed< / code > and < code > estimated< / code > otherwise< / p > < / li >
< li > < p > < code > se_min< / code > , the lower bound of the standard error with a minimum of < code > 0< / code > < / p > < / li >
< li > < p > < code > se_max< / code > the upper bound of the standard error with a maximum of < code > 1< / code > < / p > < / li >
< li > < p > < code > observations< / code > , the total number of observations, i.e. S + I + R< / p > < / li >
< li > < p > < code > observed< / code > , the original observed values< / p > < / li >
< li > < p > < code > estimated< / code > , the estimated values, calculated by the model< / p > < / li >
< / ul >
< h2 class = "hasAnchor" id = "see-also" > < a class = "anchor" href = "#see-also" > < / a > See also< / h2 >
< div class = 'dont-index' > < p > The < code > < a href = 'portion.html' > portion< / a > < / code > function to calculate resistance, < br / > < code > < a href = 'https://www.rdocumentation.org/packages/stats/topics/lm' > lm< / a > < / code > < code > < a href = 'https://www.rdocumentation.org/packages/stats/topics/glm' > glm< / a > < / code > < / p > < / div >
< h2 class = "hasAnchor" id = "examples" > < a class = "anchor" href = "#examples" > < / a > Examples< / h2 >
< pre class = "examples" > < span class = 'co' > # NOT RUN {< / span >
< span class = 'co' > # use it with base R:< / span >
< span class = 'fu' > resistance_predict< / span > (< span class = 'kw' > tbl< / span > < span class = 'kw' > =< / span > < span class = 'no' > tbl< / span > [< span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/which' > which< / a > < / span > (< span class = 'no' > first_isolate< / span > < span class = 'kw' > ==< / span > < span class = 'fl' > TRUE< / span > < span class = 'kw' > & < / span > < span class = 'no' > genus< / span > < span class = 'kw' > ==< / span > < span class = 'st' > "Haemophilus"< / span > ),],
< span class = 'kw' > col_ab< / span > < span class = 'kw' > =< / span > < span class = 'st' > "amcl"< / span > , < span class = 'kw' > col_date< / span > < span class = 'kw' > =< / span > < span class = 'st' > "date"< / span > )
< span class = 'co' > # or use dplyr so you can actually read it:< / span >
< span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/library' > library< / a > < / span > (< span class = 'no' > dplyr< / span > )
< span class = 'no' > tbl< / span > < span class = 'kw' > %> %< / span >
< span class = 'fu' > < a href = 'http://dplyr.tidyverse.org/reference/filter.html' > filter< / a > < / span > (< span class = 'no' > first_isolate< / span > < span class = 'kw' > ==< / span > < span class = 'fl' > TRUE< / span > ,
< span class = 'no' > genus< / span > < span class = 'kw' > ==< / span > < span class = 'st' > "Haemophilus"< / span > ) < span class = 'kw' > %> %< / span >
< span class = 'fu' > resistance_predict< / span > (< span class = 'no' > amcl< / span > , < span class = 'no' > date< / span > )
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< span class = 'co' > # }< / span > < span class = 'co' > # NOT RUN {< / span >
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< span class = 'co' > # real live example:< / span >
< span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/library' > library< / a > < / span > (< span class = 'no' > dplyr< / span > )
< span class = 'no' > septic_patients< / span > < span class = 'kw' > %> %< / span >
< span class = 'co' > # get bacteria properties like genus and species< / span >
< span class = 'fu' > < a href = 'join.html' > left_join_microorganisms< / a > < / span > (< span class = 'st' > "mo"< / span > ) < span class = 'kw' > %> %< / span >
< span class = 'co' > # calculate first isolates< / span >
< span class = 'fu' > < a href = 'http://dplyr.tidyverse.org/reference/mutate.html' > mutate< / a > < / span > (< span class = 'kw' > first_isolate< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'first_isolate.html' > first_isolate< / a > < / span > (< span class = 'no' > .< / span > )) < span class = 'kw' > %> %< / span >
< span class = 'co' > # filter on first E. coli isolates< / span >
< span class = 'fu' > < a href = 'http://dplyr.tidyverse.org/reference/filter.html' > filter< / a > < / span > (< span class = 'no' > genus< / span > < span class = 'kw' > ==< / span > < span class = 'st' > "Escherichia"< / span > ,
< span class = 'no' > species< / span > < span class = 'kw' > ==< / span > < span class = 'st' > "coli"< / span > ,
< span class = 'no' > first_isolate< / span > < span class = 'kw' > ==< / span > < span class = 'fl' > TRUE< / span > ) < span class = 'kw' > %> %< / span >
< span class = 'co' > # predict resistance of cefotaxime for next years< / span >
< span class = 'fu' > resistance_predict< / span > (< span class = 'kw' > col_ab< / span > < span class = 'kw' > =< / span > < span class = 'st' > "cfot"< / span > ,
< span class = 'kw' > col_date< / span > < span class = 'kw' > =< / span > < span class = 'st' > "date"< / span > ,
< span class = 'kw' > year_max< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 2025< / span > ,
< span class = 'kw' > preserve_measurements< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > ,
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< span class = 'kw' > minimum< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 0< / span > )
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< span class = 'co' > # create nice plots with ggplot< / span >
< span class = 'kw' > if< / span > (!< span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/library' > require< / a > < / span > (< span class = 'no' > ggplot2< / span > )) {
< span class = 'no' > data< / span > < span class = 'kw' > < -< / span > < span class = 'no' > septic_patients< / span > < span class = 'kw' > %> %< / span >
< span class = 'fu' > < a href = 'http://dplyr.tidyverse.org/reference/filter.html' > filter< / a > < / span > (< span class = 'no' > mo< / span > < span class = 'kw' > ==< / span > < span class = 'fu' > < a href = 'as.mo.html' > as.mo< / a > < / span > (< span class = 'st' > "E. coli"< / span > )) < span class = 'kw' > %> %< / span >
< span class = 'fu' > resistance_predict< / span > (< span class = 'kw' > col_ab< / span > < span class = 'kw' > =< / span > < span class = 'st' > "amox"< / span > ,
< span class = 'kw' > col_date< / span > < span class = 'kw' > =< / span > < span class = 'st' > "date"< / span > ,
< span class = 'kw' > info< / span > < span class = 'kw' > =< / span > < span class = 'fl' > FALSE< / span > ,
< span class = 'kw' > minimum< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 15< / span > )
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/ggplot.html' > ggplot< / a > < / span > (< span class = 'no' > data< / span > ,
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/aes.html' > aes< / a > < / span > (< span class = 'kw' > x< / span > < span class = 'kw' > =< / span > < span class = 'no' > year< / span > )) +
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/geom_bar.html' > geom_col< / a > < / span > (< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/aes.html' > aes< / a > < / span > (< span class = 'kw' > y< / span > < span class = 'kw' > =< / span > < span class = 'no' > value< / span > ),
< span class = 'kw' > fill< / span > < span class = 'kw' > =< / span > < span class = 'st' > "grey75"< / span > ) +
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/geom_linerange.html' > geom_errorbar< / a > < / span > (< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/aes.html' > aes< / a > < / span > (< span class = 'kw' > ymin< / span > < span class = 'kw' > =< / span > < span class = 'no' > se_min< / span > ,
< span class = 'kw' > ymax< / span > < span class = 'kw' > =< / span > < span class = 'no' > se_max< / span > ),
< span class = 'kw' > colour< / span > < span class = 'kw' > =< / span > < span class = 'st' > "grey50"< / span > ) +
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/scale_continuous.html' > scale_y_continuous< / a > < / span > (< span class = 'kw' > limits< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/c' > c< / a > < / span > (< span class = 'fl' > 0< / span > , < span class = 'fl' > 1< / span > ),
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}
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