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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 >
< / 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 >
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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://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 >
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< 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 >
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< 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 > ,
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< 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 > < div class = 'input' >
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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 >
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< span class = 'fu' > < a href = 'join.html' > left_join_microorganisms< / a > < / span > (< span class = 'st' > "mo"< / span > ) < span class = 'kw' > %> %< / span >
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< span class = 'co' > # calculate first isolates< / span >
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< 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 >
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< span class = 'co' > # filter on first E. coli isolates< / span >
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< 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 > ,
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< 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 > )< / div > < div class = 'output co' > #> < span class = 'message' > < span style = 'color: #0000BB;' > NOTE: Using column `< / span > < span style = 'color: #0000BB;font-weight: bold;' > mo< / span > < span style = 'color: #0000BB;' > ` as input for `col_mo`.< / span > < span > < / span > < / div > < div class = 'output co' > #> < span class = 'message' > < / span > < span style = 'color: #0000BB;' > NOTE: Using column `< / span > < span style = 'color: #0000BB;font-weight: bold;' > date< / span > < span style = 'color: #0000BB;' > ` as input for `col_date`.< / span > < span > < / span > < / div > < div class = 'output co' > #> < span class = 'message' > < / span > < span style = 'color: #0000BB;' > NOTE: Using column `< / span > < span style = 'color: #0000BB;font-weight: bold;' > patient_id< / span > < span style = 'color: #0000BB;' > ` as input for `col_patient_id`.< / span > < span > < / span > < / div > < div class = 'output co' > #> < span class = 'message' > => Found < / span > < span style = 'font-weight: bold;' > 1,317 first isolates< / span > < span > (65.9% of total)< / span > < / div > < div class = 'output co' > #>
#> Logistic regression model (logit) with binomial distribution
#> ------------------------------------------------------------
#>
#> Call:
#> glm(formula = cbind(R, S) ~ year, family = binomial)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.0751 -0.4675 -0.2840 -0.1530 1.5028
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(> |z|)
#> (Intercept) -686.7518 342.1219 -2.007 0.0447 *
#> year 0.3393 0.1698 1.998 0.0457 *
#> ---
#> Signif. codes: 0 ‘ ’ ‘ ’ ‘ ’ ‘ ’ ‘ ’
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 13.5547 on 15 degrees of freedom
#> Residual deviance: 6.8145 on 14 degrees of freedom
#> AIC: 19.128
#>
#> Number of Fisher Scoring iterations: 6
#> < / div > < div class = 'output co' > #> year value se_min se_max observations observed estimated
#> 1 2002 0.00000000 NA NA 12 0.00000000 0.0005265096
#> 2 2003 0.00000000 NA NA 13 0.00000000 0.0007390158
#> 3 2004 0.00000000 NA NA 12 0.00000000 0.0010372031
#> 4 2005 0.00000000 NA NA 15 0.00000000 0.0014555316
#> 5 2006 0.00000000 NA NA 16 0.00000000 0.0020422369
#> 6 2007 0.00000000 NA NA 17 0.00000000 0.0028647568
#> 7 2008 0.00000000 NA NA 17 0.00000000 0.0040172166
#> 8 2009 0.00000000 NA NA 18 0.00000000 0.0056306802
#> 9 2010 0.00000000 NA NA 13 0.00000000 0.0078870391
#> 10 2011 0.04761905 NA NA 21 0.04761905 0.0110375429
#> 11 2012 0.00000000 NA NA 10 0.00000000 0.0154269569
#> 12 2013 0.00000000 NA NA 13 0.00000000 0.0215239636
#> 13 2014 0.00000000 NA NA 19 0.00000000 0.0299572977
#> 14 2015 0.14285714 NA NA 14 0.14285714 0.0415545799
#> 15 2016 0.04761905 NA NA 21 0.04761905 0.0573759332
#> 16 2017 0.05000000 NA NA 20 0.05000000 0.0787262663
#> 17 2018 0.10711851 0.03829468 0.1759423 NA NA 0.1071185079
#> 18 2019 0.14414813 0.03838570 0.2499106 NA NA 0.1441481336
#> 19 2020 0.19123682 0.03582031 0.3466533 NA NA 0.1912368226
#> 20 2021 0.24922848 0.03244999 0.4660070 NA NA 0.2492284792
#> 21 2022 0.31789357 0.03249431 0.6032928 NA NA 0.3178935725
#> 22 2023 0.39551054 0.04249603 0.7485251 NA NA 0.3955105423
#> 23 2024 0.47877663 0.06981495 0.8877383 NA NA 0.4787766284
#> 24 2025 0.56323896 0.11983506 1.0000000 NA NA 0.5632389556< / div > < div class = 'input' >
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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 >
2018-12-31 01:48:53 +01:00
< 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 >
2018-12-23 21:26:21 +01:00
< 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 > ),
< span class = 'kw' > breaks< / span > < span class = 'kw' > =< / span > < span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/seq' > 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://www.rdocumentation.org/packages/base/topics/paste' > paste0< / a > < / span > (< span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/seq' > 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://www.rdocumentation.org/packages/base/topics/expression' > expression< / a > < / span > (< span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/base/topics/paste' > paste< / a > < / span > (< span class = 'st' > "Forecast of amoxicillin resistance in "< / span > ,
< span class = 'fu' > < a href = 'https://www.rdocumentation.org/packages/grDevices/topics/plotmath' > italic< / a > < / span > (< span class = 'st' > "E. coli"< / span > ))),
< 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 > ) +
< 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 > )
2018-12-31 14:30:06 +01:00
}< / div > < / span > < / pre >
2018-12-23 21:26:21 +01:00
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2018-12-29 22:24:19 +01:00
< p > Developed by < a href = 'https://www.rug.nl/staff/m.s.berends/' > Matthijs S. Berends< / a > , < a href = 'https://www.rug.nl/staff/c.f.luz/' > Christian F. Luz< / a > , < a href = 'https://www.rug.nl/staff/c.glasner/' > Corinna Glasner< / a > , < a href = 'https://www.rug.nl/staff/a.w.friedrich/' > Alex W. Friedrich< / a > , < a href = 'https://www.rug.nl/staff/b.sinha/' > Bhanu N. M. Sinha< / a > .< / p >
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2018-12-29 22:24:19 +01:00
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