mrqap.dsp: MRQAP with Double-Semi-Partialing (DSP)

In asnipe: Animal Social Network Inference and Permutations for Ecologists

MRQAP with Double-Semi-Partialing (DSP)

Description

Calculate MRQAP with Double-Semi-Partialing (DSP) from Dekker et al (2007)

Usage

mrqap.dsp(formula, intercept = TRUE, directed = "undirected",
	diagonal = FALSE, test.statistic = "t-value", 
	tol = 1e-07, randomisations = 1000)

Arguments

formula	input formula (e.g. y ~ x1 + x2), where y and each x are NxN matrices
intercept	calculate intercept (TRUE or FALSE value)
directed	whether the network is directed or undirected (enter either "directed" or "undirected")
diagonal	whether to include self-loop values (TRUE or FALSE)
test.statistic	what to calculate P-value, either t-statistic ("t-value") or regression coefficient ("beta")
tol	tolerance value for the qr function
randomisations	number of randomisations to perform for calculating P-value.

Details

Calculate the regression coefficient for each input matrix using the DSP method in Dekker et al (2007). This method randomises the residuals from the regression on each independent variable (fixed effect) in order to calculate the P value. This is the same as testing whether y is related to x1 on y while controlling for x2. This differs from regular mrqap, where the dependent (y) value is randomised, testing for whether y is related to x1 and x2 together.

Value

Returns a mrqap.dsp object containing the regression coefficient and P-values for each indendent matrix (x) and associated statistics

Author(s)

Damien R. Farine

References

Dekker, D., Krackhard, D., Snijders, T.A.B (2007) Sensitivity of MRQAP tests to collinearity and autocorellation conditions. Psychometrika 72(4): 563-581.

Examples

library(asnipe)
data("individuals")
data("group_by_individual")

# Generate network
network <- get_network(gbi)

# Create a species similarity matrix
species <- array(0,dim(network))

# Create a sex similarity matrix
sex <- array(0,dim(network))

# Fill each matrix with 1 (same) or 0 (different)
for (i in 1:nrow(network)) {
	species[i,-i] <- as.numeric(inds$SPECIES[i] == inds$SPECIES[-i])
	sex[i,-i] <- as.numeric(inds$SEX[i] == inds$SEX[-i])
}

# Run mrqap.dsp
# Note randomisations are limited to 10 to reduce runtime
reg <- mrqap.dsp(network ~ species + sex, randomisations=10)

# Look at results
reg

asnipe documentation built on Sept. 15, 2023, 9:07 a.m.