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< title > Predict antimicrobial resistance — resistance_predict • AMR (for R)< / title >
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< h1 > Predict antimicrobial resistance< / h1 >
< div class = "hidden name" > < code > resistance_predict.Rd< / code > < / div >
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< 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 >
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< pre class = "usage" > < span class = 'fu' > resistance_predict< / span > (< span class = 'no' > x< / span > , < span class = 'no' > col_ab< / span > , < span class = 'kw' > col_date< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > , < span class = 'kw' > year_min< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > ,
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< 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 = 'kw' > NULL< / span > ,
< span class = 'kw' > I_as_S< / 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 = 'no' > ...< / span > )
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< span class = 'fu' > rsi_predict< / span > (< span class = 'no' > x< / span > , < span class = 'no' > col_ab< / span > , < span class = 'kw' > col_date< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > , < span class = 'kw' > year_min< / span > < span class = 'kw' > =< / span > < span class = 'kw' > NULL< / span > ,
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< 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 = 'kw' > NULL< / span > ,
< span class = 'kw' > I_as_S< / 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 = 'no' > ...< / span > )
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< span class = 'co' > # S3 method for resistance_predict< / span >
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< span class = 'fu' > < a href = 'https://rdrr.io/r/graphics/plot.html' > plot< / a > < / span > (< span class = 'no' > x< / span > ,
< span class = 'kw' > main< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'https://rdrr.io/r/base/paste.html' > paste< / a > < / span > (< span class = 'st' > "Resistance Prediction of"< / span > , < span class = 'no' > x_name< / span > ), < span class = 'no' > ...< / span > )
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< span class = 'fu' > ggplot_rsi_predict< / span > (< span class = 'no' > x< / span > , < span class = 'kw' > main< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'https://rdrr.io/r/base/paste.html' > paste< / a > < / span > (< span class = 'st' > "Resistance Prediction of"< / span > , < span class = 'no' > x_name< / span > ),
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< span class = 'kw' > ribbon< / span > < span class = 'kw' > =< / span > < span class = 'fl' > TRUE< / span > , < span class = 'no' > ...< / span > )< / pre >
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< 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 >
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< tr >
< th > x< / th >
< td > < p > a < code > data.frame< / code > containing isolates.< / p > < / td >
< / tr >
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< tr >
< th > col_ab< / th >
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< td > < p > column name of < code > x< / code > with antimicrobial interpretations (< code > R< / code > , < code > I< / code > and < code > S< / code > )< / p > < / td >
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< / tr >
< tr >
< th > col_date< / th >
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< td > < p > 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< / p > < / td >
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< / tr >
< tr >
< th > year_min< / th >
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< td > < p > lowest year to use in the prediction model, dafaults to the lowest year in < code > col_date< / code > < / p > < / td >
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< / tr >
< tr >
< th > year_max< / th >
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< td > < p > highest year to use in the prediction model, defaults to 10 years after today< / p > < / td >
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< / 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 >
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< td > < p > the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using < code > < a href = 'https://rdrr.io/r/stats/glm.html' > glm< / a > (..., family = < a href = 'https://rdrr.io/r/stats/family.html' > binomial< / a > )< / code > ), 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.< / p > < / td >
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< / tr >
< tr >
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< th > I_as_S< / th >
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< td > < p > a logical to indicate whether values < code > I< / code > should be treated as < code > S< / code > (will otherwise be treated as < code > R< / code > )< / p > < / td >
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< / tr >
< tr >
< th > preserve_measurements< / th >
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< td > < p > a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be < code > NA< / code > .< / p > < / td >
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< / tr >
< tr >
< th > info< / th >
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< td > < p > a logical to indicate whether textual analysis should be printed with the name and < code > < a href = 'https://rdrr.io/r/base/summary.html' > summary< / a > < / code > of the statistical model.< / p > < / td >
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< / tr >
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< tr >
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< th > ...< / th >
< td > < p > parameters passed on to functions< / p > < / td >
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< / tr >
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< tr >
< th > main< / th >
< td > < p > title of the plot< / p > < / td >
< / tr >
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< tr >
< th > ribbon< / th >
< td > < p > a logical to indicate whether a ribbon should be shown (default) or error bars< / p > < / td >
< / tr >
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< / table >
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< h2 class = "hasAnchor" id = "value" > < a class = "anchor" href = "#value" > < / a > Value< / h2 >
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< p > < code > data.frame< / code > with extra class < code > "resistance_predict"< / code > with columns:< / p > < ul >
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< 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 >
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< li > < p > < code > se_min< / code > , the lower bound of the standard error with a minimum of < code > 0< / code > (so the standard error will never go below 0%)< / p > < / li >
< li > < p > < code > se_max< / code > the upper bound of the standard error with a maximum of < code > 1< / code > (so the standard error will never go above 100%)< / p > < / li >
< li > < p > < code > observations< / code > , the total number of available observations in that year, i.e. S + I + R< / p > < / li >
< li > < p > < code > observed< / code > , the original observed resistant percentages< / p > < / li >
< li > < p > < code > estimated< / code > , the estimated resistant percentages, calculated by the model< / p > < / li >
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< / ul > < p > Furthermore, the model itself is available as an attribute: < code > attributes(x)$model< / code > , see Examples.< / p >
< h2 class = "hasAnchor" id = "details" > < a class = "anchor" href = "#details" > < / a > Details< / h2 >
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< p > Valid options for the statistical model are:< / p > < ul >
< li > < p > < code > "binomial"< / code > or < code > "binom"< / code > or < code > "logit"< / code > : a generalised linear regression model with binomial distribution< / p > < / li >
< li > < p > < code > "loglin"< / code > or < code > "poisson"< / code > : a generalised log-linear regression model with poisson distribution< / p > < / li >
< li > < p > < code > "lin"< / code > or < code > "linear"< / code > : a linear regression model< / p > < / li >
< / ul >
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< h2 class = "hasAnchor" id = "read-more-on-our-website-" > < a class = "anchor" href = "#read-more-on-our-website-" > < / a > Read more on our website!< / h2 >
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< p > On our website < a href = 'https://msberends.gitlab.io/AMR' > https://msberends.gitlab.io/AMR< / a > you can find < a href = 'https://msberends.gitlab.io/AMR/articles/AMR.html' > a tutorial< / a > about how to conduct AMR analysis, the < a href = 'https://msberends.gitlab.io/AMR/reference' > complete documentation of all functions< / a > (which reads a lot easier than here in R) and < a href = 'https://msberends.gitlab.io/AMR/articles/WHONET.html' > an example analysis using WHONET data< / a > .< / p >
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< h2 class = "hasAnchor" id = "see-also" > < a class = "anchor" href = "#see-also" > < / a > See also< / h2 >
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< div class = 'dont-index' > < p > The < code > < a href = 'portion.html' > portion< / a > < / code > function to calculate resistance, < br / > < code > < a href = 'https://rdrr.io/r/stats/lm.html' > lm< / a > < / code > < code > < a href = 'https://rdrr.io/r/stats/glm.html' > glm< / a > < / code > < / p > < / div >
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< h2 class = "hasAnchor" id = "examples" > < a class = "anchor" href = "#examples" > < / a > Examples< / h2 >
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< pre class = "examples" > < span class = 'no' > x< / span > < span class = 'kw' > < -< / span > < span class = 'fu' > resistance_predict< / span > (< span class = 'no' > example_isolates< / span > , < span class = 'kw' > col_ab< / span > < span class = 'kw' > =< / span > < span class = 'st' > "AMX"< / span > , < span class = 'kw' > year_min< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 2010< / span > , < span class = 'kw' > model< / span > < span class = 'kw' > =< / span > < span class = 'st' > "binomial"< / span > )
< span class = 'fu' > < a href = 'https://rdrr.io/r/graphics/plot.html' > plot< / a > < / span > (< span class = 'no' > x< / span > )
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< span class = 'fu' > ggplot_rsi_predict< / span > (< span class = 'no' > x< / span > )
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< span class = 'co' > # use dplyr so you can actually read it:< / span >
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< span class = 'fu' > < a href = 'https://rdrr.io/r/base/library.html' > library< / a > < / span > (< span class = 'no' > dplyr< / span > )
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< span class = 'no' > x< / span > < span class = 'kw' > < -< / span > < span class = 'no' > example_isolates< / span > < span class = 'kw' > %> %< / span >
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< span class = 'fu' > < a href = 'first_isolate.html' > filter_first_isolate< / a > < / span > () < span class = 'kw' > %> %< / span >
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< span class = 'fu' > < a href = 'https://dplyr.tidyverse.org/reference/filter.html' > filter< / a > < / span > (< span class = 'fu' > < a href = 'mo_property.html' > mo_genus< / a > < / span > (< span class = 'no' > mo< / span > ) < span class = 'kw' > ==< / span > < span class = 'st' > "Staphylococcus"< / span > ) < span class = 'kw' > %> %< / span >
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< span class = 'fu' > resistance_predict< / span > (< span class = 'st' > "PEN"< / span > , < span class = 'kw' > model< / span > < span class = 'kw' > =< / span > < span class = 'st' > "binomial"< / span > )
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< span class = 'fu' > < a href = 'https://rdrr.io/r/graphics/plot.html' > plot< / a > < / span > (< span class = 'no' > x< / span > )
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< span class = 'co' > # get the model from the object< / span >
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< span class = 'no' > mymodel< / span > < span class = 'kw' > < -< / span > < span class = 'fu' > < a href = 'https://rdrr.io/r/base/attributes.html' > attributes< / a > < / span > (< span class = 'no' > x< / span > )$< span class = 'no' > model< / span >
< span class = 'fu' > < a href = 'https://rdrr.io/r/base/summary.html' > summary< / a > < / span > (< span class = 'no' > mymodel< / span > )
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< span class = 'co' > # create nice plots with ggplot2 yourself< / span >
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< span class = 'kw' > if< / span > (!< span class = 'fu' > < a href = 'https://rdrr.io/r/base/library.html' > require< / a > < / span > (< span class = 'no' > ggplot2< / span > )) {
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< span class = 'no' > data< / span > < span class = 'kw' > < -< / span > < span class = 'no' > example_isolates< / span > < span class = 'kw' > %> %< / span >
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< span class = 'fu' > < a href = 'https://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 >
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< span class = 'fu' > resistance_predict< / span > (< span class = 'kw' > col_ab< / span > < span class = 'kw' > =< / span > < span class = 'st' > "AMX"< / span > ,
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< span class = 'kw' > col_date< / span > < span class = 'kw' > =< / span > < span class = 'st' > "date"< / span > ,
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< span class = 'kw' > model< / span > < span class = 'kw' > =< / span > < span class = 'st' > "binomial"< / span > ,
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< span class = 'kw' > info< / span > < span class = 'kw' > =< / span > < span class = 'fl' > FALSE< / span > ,
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< span class = 'kw' > minimum< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 15< / span > )
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< 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 > ),
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< span class = 'kw' > fill< / span > < span class = 'kw' > =< / span > < span class = 'st' > "grey75"< / span > ) +
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< 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 > ,
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< 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 > ) +
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< 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://rdrr.io/r/base/c.html' > c< / a > < / span > (< span class = 'fl' > 0< / span > , < span class = 'fl' > 1< / span > ),
< span class = 'kw' > breaks< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'https://rdrr.io/r/base/seq.html' > seq< / a > < / span > (< span class = 'fl' > 0< / span > , < span class = 'fl' > 1< / span > , < span class = 'fl' > 0.1< / span > ),
< span class = 'kw' > labels< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'https://rdrr.io/r/base/paste.html' > paste0< / a > < / span > (< span class = 'fu' > < a href = 'https://rdrr.io/r/base/seq.html' > seq< / a > < / span > (< span class = 'fl' > 0< / span > , < span class = 'fl' > 100< / span > , < span class = 'fl' > 10< / span > ), < span class = 'st' > "%"< / span > )) +
< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/labs.html' > labs< / a > < / span > (< span class = 'kw' > title< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'https://rdrr.io/r/base/expression.html' > expression< / a > < / span > (< span class = 'fu' > < a href = 'https://rdrr.io/r/base/paste.html' > paste< / a > < / span > (< span class = 'st' > "Forecast of amoxicillin resistance in "< / span > ,
< span class = 'fu' > < a href = 'https://rdrr.io/r/grDevices/plotmath.html' > italic< / a > < / span > (< span class = 'st' > "E. coli"< / span > ))),
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< span class = 'kw' > y< / span > < span class = 'kw' > =< / span > < span class = 'st' > "%IR"< / span > ,
< span class = 'kw' > x< / span > < span class = 'kw' > =< / span > < span class = 'st' > "Year"< / span > ) +
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< span class = 'fu' > < a href = 'https://ggplot2.tidyverse.org/reference/ggtheme.html' > theme_minimal< / a > < / span > (< span class = 'kw' > base_size< / span > < span class = 'kw' > =< / span > < span class = 'fl' > 13< / span > )
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