PHENIX (version 1.0)

int.boot: Phenotypic integration (by Wagner) significance test

Description

This function estimates the confidence intervals of the phenotypic integration indices (Wagner, 1984) using bootstrap. After a number of replicates defined by the user, this function evaluates if the obtained indices are significantly different from 0.

Usage

int.boot(traits, replicates = 1000)

Arguments

traits

a dataframe or a matrix object with all your traits in which columns are the traits and rows are the individuals

replicates

a numeric, the number of randomizations performed to evaluate significance (1000 by default)

Value

• A nine rows matrix containing Mean, Median, standard deviation (SD), standard error (SE), lower and higer confidence interval at 99%, lower and higer confidence interval at 95%, and the number of randomizations.

Details

Rows containing missing values are removed.

References

Cheverud JM, GP Wagner, MM Dow 1989 Methods for the comparative analysis of variation patterns. Syst Zool 38:201–213

Pavlicev M, JM Cheverud, GPWagner 2009 Measuring morphological integration using eigenvalue variance. Evolutionary Biology 36: 157–170

See Also

int, intsc.boot

Examples

# Dataset from Torices & Méndez (2014)
# This data set represents the dry mass (in grames) of inflorescence components of the sunflower
# species Tussilago farfara. The inflorescences were dissected in 'SCAPE', 'RECEPTACLE', 'MALEFL'
# (male flowers), 'OVAR' (reproductive part of female flowers), and 'RAYS' (the petaloid ray of
# female flower). Furthermore in the last column the total weight of the inflorescence is added
# 'TOTALSIZE'

data(tussilago)
int.boot(tussilago[,1:5], 100) # The phenotypic integration index is significant because
# intervals do not include 0.
# For faster execution, we used 100 replicates in this example.
# For real data set increase this number:
# int.boot(tussilago[,1:5], 5000)