# Calculate the Mean AMR Distance Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand. ## Usage ``` r mean_amr_distance(x, ...) # S3 method for class 'sir' mean_amr_distance(x, ..., combine_SI = TRUE) # S3 method for class 'data.frame' mean_amr_distance(x, ..., combine_SI = TRUE) amr_distance_from_row(amr_distance, row) ``` ## Arguments - x: A vector of class [sir](https://amr-for-r.org/reference/as.sir.md), [mic](https://amr-for-r.org/reference/as.mic.md) or [disk](https://amr-for-r.org/reference/as.disk.md), or a [data.frame](https://rdrr.io/r/base/data.frame.html) containing columns of any of these classes. - ...: Variables to select. Supports [tidyselect language](https://tidyselect.r-lib.org/reference/starts_with.html) such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and can thus also be [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_selectors.md). - combine_SI: A [logical](https://rdrr.io/r/base/logical.html) to indicate whether all values of S, SDD, and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant) - the default is `TRUE`. - amr_distance: The outcome of `mean_amr_distance()`. - row: An index, such as a row number. ## Details The mean AMR distance is effectively [the Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand. MIC values (see [`as.mic()`](https://amr-for-r.org/reference/as.mic.md)) are transformed with [`log2()`](https://rdrr.io/r/base/Log.html) first; their distance is thus calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`. SIR values (see [`as.sir()`](https://amr-for-r.org/reference/as.sir.md)) are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If `combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1. For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see *Examples*. Use `amr_distance_from_row()` to subtract distances from the distance of one row, see *Examples*. ## Interpretation Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious. ## Examples ``` r sir <- random_sir(10) sir #> Class 'sir' #> [1] R I R I I S R S I I mean_amr_distance(sir) #> [1] 1.449138 -0.621059 1.449138 -0.621059 -0.621059 -0.621059 1.449138 #> [8] -0.621059 -0.621059 -0.621059 mic <- random_mic(10) mic #> Class 'mic' #> [1] 0.25 0.5 8 2 0.004 4 1 0.001 #> [9] 0.004 <=0.0005 mean_amr_distance(mic) #> [1] 0.2626139 0.4520471 1.2097801 0.8309136 -0.8675039 1.0203468 #> [7] 0.6414804 -1.2463704 -0.8675039 -1.4358036 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #> [1] 0.2626139 0.4520471 1.2097801 0.8309136 -0.8675039 1.0203468 #> [7] 0.6414804 -1.2463704 -0.8675039 -1.4358036 disk <- random_disk(10) disk #> Class 'disk' #> [1] 49 38 33 31 17 42 43 46 37 46 mean_amr_distance(disk) #> [1] 1.14152462 -0.02113934 -0.54962296 -0.76101641 -2.24077054 0.40164755 #> [7] 0.50734427 0.82443445 -0.12683607 0.82443445 y <- data.frame( id = LETTERS[1:10], amox = random_sir(10, ab = "amox", mo = "Escherichia coli"), cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"), gent = random_mic(10, ab = "gent", mo = "Escherichia coli"), tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli") ) y #> id amox cipr gent tobr #> 1 A S 28 >=2 8 #> 2 B R 33 1 8 #> 3 C R 32 1 8 #> 4 D I 25 1 16 #> 5 E I 19 0.5 16 #> 6 F S 23 0.5 8 #> 7 G R 27 0.5 16 #> 8 H S 29 0.5 8 #> 9 I R 32 1 8 #> 10 J R 32 0.5 16 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent", #> and "tobr" #> [1] 0.06974787 0.45859464 0.40418392 0.03309016 -0.65092262 -0.91740267 #> [7] 0.25870477 -0.59093836 0.40418392 0.53075837 y$amr_distance <- mean_amr_distance(y, is.mic(y)) #> ℹ Calculating mean AMR distance based on columns "gent" and "tobr" y[order(y$amr_distance), ] #> id amox cipr gent tobr amr_distance #> 6 F S 23 0.5 8 -0.8163565 #> 8 H S 29 0.5 8 -0.8163565 #> 2 B R 33 1 8 -0.1012596 #> 3 C R 32 1 8 -0.1012596 #> 9 I R 32 1 8 -0.1012596 #> 5 E I 19 0.5 16 0.1518893 #> 7 G R 27 0.5 16 0.1518893 #> 10 J R 32 0.5 16 0.1518893 #> 1 A S 28 >=2 8 0.6138374 #> 4 D I 25 1 16 0.8669863 if (require("dplyr")) { y %>% mutate( amr_distance = mean_amr_distance(y), check_id_C = amr_distance_from_row(amr_distance, id == "C") ) %>% arrange(check_id_C) } #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent", #> and "tobr" #> id amox cipr gent tobr amr_distance check_id_C #> 1 C R 32 1 8 0.40418392 0.00000000 #> 2 I R 32 1 8 0.40418392 0.00000000 #> 3 B R 33 1 8 0.45859464 0.05441072 #> 4 J R 32 0.5 16 0.53075837 0.12657445 #> 5 G R 27 0.5 16 0.25870477 0.14547915 #> 6 A S 28 >=2 8 0.06974787 0.33443605 #> 7 D I 25 1 16 0.03309016 0.37109376 #> 8 H S 29 0.5 8 -0.59093836 0.99512228 #> 9 E I 19 0.5 16 -0.65092262 1.05510655 #> 10 F S 23 0.5 8 -0.91740267 1.32158659 if (require("dplyr")) { # support for groups example_isolates %>% filter(mo_genus() == "Enterococcus" & mo_species() != "") %>% select(mo, TCY, carbapenems()) %>% group_by(mo) %>% mutate(dist = mean_amr_distance(.)) %>% arrange(mo, dist) } #> ℹ Using column 'mo' as input for `mo_genus()` #> ℹ Using column 'mo' as input for `mo_species()` #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns "TCY", "IPM", and "MEM" #> # A tibble: 63 × 5 #> # Groups: mo [4] #> mo TCY IPM MEM dist #> #> 1 B_ENTRC_AVIM S S NA 0 #> 2 B_ENTRC_AVIM S S NA 0 #> 3 B_ENTRC_CSSL NA S NA NA #> 4 B_ENTRC_FACM S S NA -2.66 #> 5 B_ENTRC_FACM S R R -0.423 #> 6 B_ENTRC_FACM S R R -0.423 #> 7 B_ENTRC_FACM NA R R 0.224 #> 8 B_ENTRC_FACM NA R R 0.224 #> 9 B_ENTRC_FACM NA R R 0.224 #> 10 B_ENTRC_FACM NA R R 0.224 #> # ℹ 53 more rows ```