test_cwm: Testing the relationship between weighted-mean of species...

Description Usage Arguments Details Value See Also Examples

View source: R/test_cwm.R

Description

Function calculating relationship between weighted-mean of species attributes and sample attributes and performing standard (row based), modified (column based), or max test of significance.

Usage

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test_cwm(cwm, env, method = c("cor"), wcor = FALSE, wstand = FALSE,
  wreg = NULL, dependence = "cwm ~ env", perm = 499, test = "max",
  parallel = NULL, p.adjust.method = "holm", adjustP = FALSE)

## S3 method for class 'testCWM'
print(x, digits = max(3, getOption("digits") - 3),
  missing.summary = FALSE, adjustP = FALSE, eps.Pvalue = 0.001, ...)

## S3 method for class 'testCWM'
coef(object, ...)

## S3 method for class 'testCWM'
plot(x, alpha = 0.05, line = NA, cex.lab = 1.5,
  par.mar = c(0.5, 0.5, 0.5, 0.5), box.col = c("blue", "red"),
  box.lwd = 2, ...)

Arguments

cwm

An object of the class cwm.

env

Vector or matrix with variables. See details.

method

Statistical method used to analyse the relationship between cwm (of class cwm) and env (sample attributes); partial match to 'cor', 'lm' and 'aov'.

wcor

Logical; should the correlation be weighted by rowsums of com (extracted from cwm?). Default FALSE.

wstand

Logical; should the variables in correlation be first weighted-standardized? Default FALSE. If wstand = TRUE, both env and traits are weighted standardized prior to calcuation; weights are derived from com matrix (extracted from cwm); env is weighted by rowsums of com, while traits are weighted by colsums of com.

wreg

NULL (default) or numeric vector of weights used in regression (method = 'lm'). If weights are provided, weighted least squares are calculated instead of ordinary least squares.

dependence

Should cwm be dependent variable and env independent ('cwm ~ env'), or opposite? Applicable only for method = 'lm'. Partial match to 'cwm ~ env' and 'env ~ cwm'.

perm

Number of permutations.

test

Vector of character values. Which test should be conducted? Partial match to 'standard' or 'rowbased' for standard (row based) permutation test, 'modified' or 'colbased' for modified (column based) permutation test, 'max' for max test (selecting the higher from rowbased and colbased result), and 'all' including all three tests. See Details.

parallel

NULL (default) or integer number. Number of cores for parallel calculation of modified permutation test. Maximum number of cores should correspond to number of available cores on the processor.

p.adjust.method

A string indicating the method of P-value adjustement, see p.adjust.methods for possible choices.

adjustP

Logical, default FALSE. Should be the P-values adjusted? If adjustP = TRUE, the last column in the results is adjusted by method selected in p.adjust.method.

x, object

object of the class "cwm", generated by function cwm.

digits

number of digits reported by print method on object of "cwm" class (default is 3).

missing.summary

Logical; should be the summary of values missing in env, cwm and traits be printed along to the result of test_cwm analysis? Default is TRUE.

eps.Pvalue

Values of P below this threshold will be printed as < [eps] in the output.

...

Other arguments for print, summary, coef or plot functions (some not implemented yet).

alpha, line, cex.lab, par.mar, box.col, box.lwd

Graphical parameters for plot function.

Details

Currently implemented statistical methods: 'cor'. Plan to implement also: 'lm' and 'aov'. For fourth corner, please use test_fourth.

Argument env can be vector or matrix with one column. Only in the case of linear regression (method = 'lm') it is possible to use matrix with several variables, which will all be used as independent variables in the model. For ANOVA and Kruskal-Wallis test, make sure that 'env' is factor (warning will be returned if this is not the case, but the calculation will be conducted).

Difference between method = 'lm' and 'aov' is in the format of summary tables, returned by summary.cwm function. In case of 'aov', this summary is expressed in the traditional language of ANOVA rather than linear models.

Both method = 'lm' and 'slope' are based on linear regression and calculated by function lm, but differ by test statistic: while 'lm' is using F value and is testing the strength of the regression (measured by r2), 'slope' is using the slope of the regression line (b). This statistic is added here for comparison with the fourth corner method.

Specific issue related to weighted mean is the case of missing species attributes. In current implementation, species with missing species attributes are removed from sample x species matrix prior to permutation of species attributes among species.

Value

Function cwm returns list of the class "cwm" (with print and summary methods), which contains the following components:

See Also

cwm, snc

Examples

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data (vltava)
CWM <- cwm (com = vltava$herbs$spe, traits = vltava$herbs$traits)
re <- test_cwm (cwm = CWM, env = vltava$env[,c('pH', 'COVERE32')])
re
plot (re)

zdealveindy/weimea documentation built on June 14, 2018, 3:45 a.m.