# partition: Estimate within-group variation In intRvals: Analysis of Time-Ordered Event Data with Missed Observations

 partition R Documentation

## Estimate within-group variation

### Description

Estimate within-group variation in interval length

### Usage

```partition(x, conf.level = 0.9, alpha = 0.05, silent = F)
```

### Arguments

 `x` object inheriting from class `intRvals` `conf.level` confidence level passed to function fundamental, used in selecting fundamental intervals `alpha` significance level for differences within and between groups or subjects `silent` logical, if `TRUE` print no text to console

### Details

Within- and between-group variation is estimated on the subset of fundamental intervals only.

The subset of fundamental intervals is selected using fundamental.

We calculate sigma.within = s_w n_{ind}/(n_{ind}+1) with s_w the uncorrected sample standard deviation of within-group centered values (obtained from subtracting the group's mean value from each observation value), and n_{ind}/(n_{ind}+1) Bessel's correction with n_{ind} the average number of repeated measures per group. Significance of within-group variation is determined by testing for a random effect of group against a constant null model (van de Pol & Wright 2009), using the R-package lme4 (Bates et al. 2015).

### Value

A logical atomic vector indicating which intervals are fundamental.

`sigma.within`

within-group standard deviation in interval length, estimated on fundamental intervals with repeated measures only

`sigma`

the total standard deviation in interval length, copied from `x\$sigma`

`p.within`

p-value form a likelihood-ratio test indicating whether there is evidence for a random effect of group or subject

`n.within`

average number of intervals per group

`n.total`

total number of intervals

`n.repeat`

number of fundamental intervals with repeated measures, the size of the dataset on which `sigma.within` was estimated

`p<alpha`

logical. Whether there was significant evidence for a difference in within- and between-group/subject variance

### References

van de Pol, M. & Wright, J. (2009). A simple method for distinguishing within- versus between-subject effects using mixed models. Animal Behaviour, 77, 753-758.

Bates, D., M\"achler, M., Bolker, B.M. & Walker, S.C. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 1-48.

### Examples

```# select the group of intervals observed on Terschelling island
dropset=goosedrop[goosedrop\$site=="terschelling",]
# estimate an interval model, with separate within- and between-group variation:
dr=estinterval(data=dropset\$interval,group = dropset\$bout_id)
# plot the model fit:
plot(dr)
# estimate within-group variation, and its significance:
output=partition(dr)
# print within-group standard deviation:
output\$sigma.within
# is the model including within-group standard deviation signicant,
# relative to a null model without separate within-group sd,
# at the specified confidence level alpha?
output\$`p<alpha`  #> TRUE
```

intRvals documentation built on May 3, 2022, 1:07 a.m.