similarity_measures_regression | R Documentation |

Functions that provide objects with functionality used by
`stability`

to measure the similarity between numeric
predictions of two results in regression problems.

edist() msdist() rmsdist() madist() qadist(p = 0.95) cprob(kappa = 0.1) rbfkernel() tanimoto() cosine() ccc() pcc()

`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. |

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.

`stability`

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)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.