R/similarity.R
apd_similarity.Rd
apd_similarity()
is used to analyze samples in terms of similarity scores
for binary data. All features in the data should be binary (i.e. zero or
one).
apd_similarity(x, ...) # S3 method for default apd_similarity(x, quantile = NA_real_, ...) # S3 method for data.frame apd_similarity(x, quantile = NA_real_, ...) # S3 method for matrix apd_similarity(x, quantile = NA_real_, ...) # S3 method for formula apd_similarity(formula, data, quantile = NA_real_, ...) # S3 method for recipe apd_similarity(x, data, quantile = NA_real_, ...)
x  Depending on the context:


...  Options to pass to 
quantile  A real number between 0 and 1 or NA for how the similarity
values for each sample versus the training set should be summarized. A value
of 
formula  A formula specifying the predictor terms on the righthand side. No outcome should be specified. 
data  When a recipe or formula is used,

A apd_similarity
object.
The function computes measures of similarity for different samples
points. For example, suppose samples A
and B
both contain p binary
variables. First, a 2x2 table is constructed between A
and B
across
their elements. The table will contain p entries across the four cells
(see the example below). From this, different measures of likeness are
computed.
For a training set of n samples, a new sample is compared to each, resulting in n similarity scores. These can be summarized into a single value; the median similarity is used by default by the scoring function.
For this method, the computational methods are fairly taxing for large data sets. The training set must be stored (albeit in a sparse matrix format) so object sizes may become large.
By default, the computations are run in parallel using all possible
cores. To change this, call the setThreadOptions
function in the
RcppParallel
package.
Leach, A. and Gillet V. (2007). An Introduction to Chemoinformatics. Springer, New York
#> Applicability domain via similarity #> Reference data were 67 variables collected on 4330 data points. #> New data summarized using the mean.# plot the empirical cumulative distribution function (ECDF) for the training set: library(ggplot2) autoplot(jacc_sim)# Example calculations for two samples: A < as.matrix(binary_tr[1,]) B < as.matrix(binary_tr[2,]) xtab < table(A, B) xtab#> B #> A 0 1 #> 0 62 0 #> 1 1 4# Jaccard statistic xtab[2, 2] / (xtab[1, 2] + xtab[2, 1] + xtab[2, 2])#> [1] 0.8#> [1] 0.9701493#> [1] 0.9552239# Summarize across all training set similarities mean_sim < score(jacc_sim, new_data = binary_unk) mean_sim#> # A tibble: 5 x 2 #> similarity similarity_pctl #> <dbl> <dbl> #> 1 0.376 49.8 #> 2 0.284 13.5 #> 3 0.218 6.46 #> 4 0.452 100 #> 5 0.0971 5.59# }