# 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] I I R I R S S S I S mean_amr_distance(sir) #> [1] -0.4743416 -0.4743416 1.8973666 -0.4743416 1.8973666 -0.4743416 #> [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416 mic <- random_mic(10) mic #> Class 'mic' #> [1] 0.004 2 0.002 0.0001 0.004 0.002 >=4 0.0002 0.032 0.004 mean_amr_distance(mic) #> [1] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557 #> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #> [1] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557 #> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915 disk <- random_disk(10) disk #> Class 'disk' #> [1] 43 12 28 32 22 31 35 25 43 35 mean_amr_distance(disk) #> [1] 1.30998909 -1.96498364 -0.27467513 0.14790199 -0.90854082 0.04225771 #> [7] 0.46483484 -0.59160798 1.30998909 0.46483484 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 31 2 >=16 #> 2 B S 27 <=1 8 #> 3 C R 25 2 4 #> 4 D R 25 <=1 2 #> 5 E I 31 <=1 2 #> 6 F S 32 <=1 8 #> 7 G I 29 2 2 #> 8 H S 18 <=1 4 #> 9 I S 28 <=1 4 #> 10 J R 17 <=1 2 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent", #> and "tobr" #> [1] 0.90606144 -0.03989270 0.66241774 -0.09230226 -0.32300020 0.19914999 #> [7] 0.09893189 -0.70734036 -0.22925499 -0.47477055 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 #> 4 D R 25 <=1 2 -0.7848712 #> 5 E I 31 <=1 2 -0.7848712 #> 10 J R 17 <=1 2 -0.7848712 #> 8 H S 18 <=1 4 -0.3105295 #> 9 I S 28 <=1 4 -0.3105295 #> 2 B S 27 <=1 8 0.1638121 #> 6 F S 32 <=1 8 0.1638121 #> 7 G I 29 2 2 0.2502272 #> 3 C R 25 2 4 0.7245688 #> 1 A S 31 2 >=16 1.6732521 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 25 2 4 0.66241774 0.0000000 #> 2 A S 31 2 >=16 0.90606144 0.2436437 #> 3 F S 32 <=1 8 0.19914999 0.4632678 #> 4 G I 29 2 2 0.09893189 0.5634858 #> 5 B S 27 <=1 8 -0.03989270 0.7023104 #> 6 D R 25 <=1 2 -0.09230226 0.7547200 #> 7 I S 28 <=1 4 -0.22925499 0.8916727 #> 8 E I 31 <=1 2 -0.32300020 0.9854179 #> 9 J R 17 <=1 2 -0.47477055 1.1371883 #> 10 H S 18 <=1 4 -0.70734036 1.3697581 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 ```