perspectev (version 1.1)

perspectev.test: Test for irreducibility of relationship between upper level traits and survivorship

Description

Performs permutation tests by permuting upper level labels between lower levels, recalculating upper trait value, and taking the correlation between upper level trait and survivorship. This process is repeated until a null distribution is generated. This is then compared against observed covariance to give a p value for the null hypothesis that a relationship between trait and survivorship is explainable by random aggregations of lower level traits.

Usage

perspectev.test(data,iterations=1000,cores=1,traitfun=mcpRange,vlist=NULL,na.rm=FALSE)

Arguments

data

Dataframe in perspectev format (see ?perspectev.read).

iterations

Number of iterations to perform. At least 1000 is recommended, though can be slow.

cores

Number of cores over which to parallelize the test.

traitfun

Function for calculating trait values at each level.

vlist

Optional variable list for trait function.

na.rm

Remove NA values from trait functions? Shouldn't need to be used if trim=TRUE from perspectev.read.

Value

correlation_permuted

Correlations between trait and survivorship obtained from permuted upper levels (Si)

correlation_observed

Observed correlation between upper level trait and survivorship (Ri)

pvalue

Portion of permuted genus correlations (S) larger than observed value (R)

permuted_quantiles

Matrix of interquartile trait values obtained from each upper level permutation

Examples

	data(testData)

  	data = perspectev.read(testData,extinctionAge=5,occurrenceAge="Age",
  	upper="Genus",lower="Species",traits=c("Lat","Long"),traitfun=mcpRange,projection=FALSE)

  	#4 iterations chosen out of convenience - use more!
	mcpTest  = perspectev.test(data,4,1,traitfun=mcpRange)
	mcpSim  = perspectev.simulate(data,4,1,traitfun=mcpRange)
	perspectev.plot(mcpTest,list(mcpSim),c("S1"),"Test")