evaluate_partition_unsup {doMIsaul} | R Documentation |

Compares partitions on number of cluster, ARI and percentage of unclassified observations.

```
evaluate_partition_unsup(
partition,
partition.ref,
is.missing = NULL,
is.cens = NULL
)
```

`partition` |
vector (factor): the partition to evaluate. |

`partition.ref` |
reference partition 1 (ex partition on complete data or true partition if known). |

`is.missing` |
boolean vector identifying observations with missing data
(coded |

`is.cens` |
the incomplete dataframe with NA for missing and left-censored data (or the complete datasets if all data were observed). |

A list containing the following elements : `Nbclust`

: number of
clusters of the partition; `ARI`

: ARI value on cases classified by
both partitions ; `ARI.cc`

: ARI value on cases complete AND
classified by both partitions ; `ARI.nona`

; ARI on cases with no
missing data AND classified by both partitions; `ARI.nocens`

: ARI on
cases with no censored data AND classified by both partitions ;
`Per.Unclass`

: Percentage of observations unclassified in the
partition ; `Per.Unclass.cc`

: Among complete cases, percentage of
observations unclassified in the partition ; `Per.Unclass.na`

:
Among cases with missing data, percentage of observations unclassified in
the partition ; `Per.Unclass.cens`

: Among cases with censored data,
percentage of observations unclassified in the partition ;
`Per.Unclass.ic`

: Among incomplete cases, percentage of observations
unclassified in the partition

```
res <- evaluate_partition_unsup(
partition = factor(rep(c(1,2,3), each = 50)),
partition.ref = factor(rep(c(1,2,3), times = c(100, 25, 25))))
## With missing data
res2 <- evaluate_partition_unsup(
partition = factor(rep(c(1,2,3), each = 50)),
partition.ref = factor(rep(c(1,2,3), times = c(100, 25, 25))),
is.missing = sample(c(TRUE, FALSE), 150, replace = TRUE, prob = c(.2,.8)))
## With missing and censored data
missing.indicator <- sample(c(TRUE, FALSE), 150,
replace = TRUE, prob = c(.2,.8))
Censor.indicator <- data.frame(
X1 = runif(150, 1, 5),
X2 = runif(150, 6, 8),
X3 = runif(150, 3, 9))
Censor.indicator$X1[missing.indicator] <- NA
Censor.indicator$X1[
sample(c(TRUE, FALSE), 150,replace = TRUE, prob = c(.1,.9))] <- NA
Censor.indicator$X2[
sample(c(TRUE, FALSE), 150,replace = TRUE, prob = c(.3,.7))] <- NA
Censor.indicator$X3[
sample(c(TRUE, FALSE), 150,replace = TRUE, prob = c(.05,.95))] <- NA
res3 <- evaluate_partition_unsup(
partition = factor(rep(c(1,2,3), each = 50)),
partition.ref = factor(rep(c(1,2,3), times = c(100, 25, 25))),
is.missing = missing.indicator,
is.cens = Censor.indicator)
## With missing and censored data and unclassifed observations
res4 <- evaluate_partition_unsup(
partition = factor(rep(c(1,2, NA,3), times = c(50, 40, 20, 40))),
partition.ref = factor(rep(c(1,2,3), times = c(100, 25, 25))),
is.missing = missing.indicator,
is.cens = Censor.indicator)
```

[Package *doMIsaul* version 1.0.1 Index]