# Similarity Measure Infrastructure for Stability Assessment with Numerical Responses Functions that provide objects with functionality used by `stability` to measure the similarity between numeric predictions of two results in regression problems. ```r edist() msdist() rmsdist() madist() qadist(p = 0.95) cprob(kappa = 0.1) rbfkernel() tanimoto() cosine() ccc() pcc() ``` ## Arguments - `p`: A numeric value between 0 and 1 specifying the probability to which the sample quantile of the absolute distance between the predictions is computed. - `kappa`: A positive numeric value specifying the upper limit of the absolute distance between the predictions to which the coverage probability is computed. ## Details The similarity measure functions provide objects that include functionality used by `stability` to measure the similarity between numeric predictions of two results in regression problems. The `edist` (euclidean distance), `msdist` (mean squared distance), `rmsdist` (root mean squared distance), `madist` (mean absolute distance) and `qadist` (quantile of absolute distance) functions implement scale-variant distance measures that are unbounded. The `cprob` (coverage probability), `rbfkernel` (gaussian radial basis function kernel), `tanimoto` (tanimoto coefficient) and `cosine` (cosine similarity) functions implement scale-variant distance measures that are bounded. The `ccc` (concordance correlation coefficient) and `pcc` (pearson correlation coefficient) functions implement scale-invariant distance measures that are bounded between 0 and 1. ## See Also `stability` ## Examples ```r set.seed(0) library("partykit") airq <- subset(airquality, !is.na(Ozone)) m1 <- ctree(Ozone ~ ., data = airq[sample(1:nrow(airq), replace = TRUE),]) m2 <- ctree(Ozone ~ ., data = airq[sample(1:nrow(airq), replace = TRUE),]) p1 <- predict(m1) p2 <- predict(m2) ## euclidean distance m <- edist() m$measure(p1, p2) ## mean squared distance m <- msdist() m$measure(p1, p2) ## root mean squared distance m <- rmsdist() m$measure(p1, p2) ## mean absolute istance m <- madist() m$measure(p1, p2) ## quantile of absolute distance m <- qadist() m$measure(p1, p2) ## coverage probability m <- cprob() m$measure(p1, p2) ## gaussian radial basis function kernel m <- rbfkernel() m$measure(p1, p2) ## tanimoto coefficient m <- tanimoto() m$measure(p1, p2) ## cosine similarity m <- cosine() m$measure(p1, p2) ## concordance correlation coefficient m <- ccc() m$measure(p1, p2) ## pearson correlation coefficient m <- pcc() m$measure(p1, p2) ```

similarity_measures_regression function