View source: R/DroppingInterval.R

partition | R Documentation |

Estimate within-group variation in interval length

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

`x` |
object inheriting from class |

`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 |

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).

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

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.

# 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

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