# similarity_measures_regression: Similarity Measure Infrastructure for Stability Assessment... In stablelearner: Stability Assessment of Statistical Learning Methods

 similarity_measures_regression R Documentation

## Similarity Measure Infrastructure for Stability Assessment with Numerical Responses

### Description

Functions that provide objects with functionality used by `stability` to measure the similarity between numeric predictions of two results in regression problems.

### Usage

``````  edist()
msdist()
rmsdist()

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.

`stability`

### Examples

``````

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\$measure(p1, p2)

## quantile of absolute distance
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)

``````

stablelearner documentation built on April 14, 2023, 12:40 a.m.