areaCAT | R Documentation |
Compute the area between the bisector and the concordance curve.
areaCAT(concordance, plotIt = FALSE)
concordance |
A long format |
plotIt |
Plot the concordance (default |
A long format data.frame
object with several columns:
comparison
which indicates the comparison number;
n_features
which indicates the total number of taxa in
the comparison dataset;
method1
which contains the first method name;
method2
which contains the first method name;
rank
;
concordance
which is defined as the cardinality of the
intersection of the top rank elements of each list, divided by rank, i.e.
, (L_{1:rank} \bigcap M_{1:rank})/(rank), where L and M represent
the lists of the extracted statistics of method1 and method2
respectively;
heightOver
which is the distance between the bisector and
the concordance value;
areaOver
which is the cumulative sum of the
heightOver
value.
createConcordance
and plotConcordance
data(ps_plaque_16S) # Balanced design for dependent samples my_splits <- createSplits( object = ps_plaque_16S, varName = "HMP_BODY_SUBSITE", balanced = TRUE, paired = "RSID", N = 10 # N = 100 suggested ) # Make sure the subject ID variable is a factor phyloseq::sample_data(ps_plaque_16S)[, "RSID"] <- as.factor( phyloseq::sample_data(ps_plaque_16S)[["RSID"]]) # Initialize some limma based methods my_limma <- set_limma(design = ~ RSID + HMP_BODY_SUBSITE, coef = "HMP_BODY_SUBSITESupragingival Plaque", norm = c("TMM", "CSS")) # Set the normalization methods according to the DA methods my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"), method = c("TMM", "CSS")) # Run methods on split datasets results <- runSplits(split_list = my_splits, method_list = my_limma, normalization_list = my_norm, object = ps_plaque_16S) # Concordance for p-values concordance_pvalues <- createConcordance( object = results, slot = "pValMat", colName = "rawP", type = "pvalue" ) # Add area over the concordance curve concordance_area <- areaCAT(concordance = concordance_pvalues)
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