Tstats: Computing observed T-statistics (T for Traits) and null...

In cati: Community Assembly by Traits: Individuals and Beyond

Computing observed T-statistics (T for Traits) and null expectations.

Description

Computing observed T-statistics (T for Traits) as three ratios of variance, namely T_IP.IC, T_IC.IR and T_PC.PR. This function can also return the distribution of this three statistics under null models.

Usage

	Tstats(traits, ind.plot, sp, SE = 0, reg.pool = NULL, 
	SE.reg.pool = NULL, nperm = 99, printprogress = TRUE, 
	independantTraits = TRUE)
	
	sum_Tstats(x, val.quant = c(0.025, 0.975), type = "all")
	
	ses.Tstats(x, val.quant = c(0.025, 0.975))
		
	## S3 method for class 'Tstats'
barplot(height, val.quant = c(0.025, 0.975), 
	col.index = c("red", "purple", "olivedrab3", "white"), ylim = NULL, ...)
                 
	## S3 method for class 'Tstats'
plot(x, type = "normal", col.index = c("red", "purple", "olivedrab3"),
	add.conf = TRUE, color.cond = TRUE, val.quant = c(0.025, 0.975), ...)
		
	## S3 method for class 'Tstats'
print(x, ...)
		
	## S3 method for class 'Tstats'
summary(object, ...)

Arguments

traits	Individual Matrix of traits with traits in columns. For one trait, use as.matrix().
ind.plot	Factor defining the name of the plot in which the individual is.
sp	Factor defining the species which the individual belong to.
SE	A single value or vector of standard errors associated with each traits. Especially allow to handle measurement errors. Not used with populational null model.
reg.pool	Regional pool data for traits. If not informed, 'traits' is considered as the regional pool. This matrix need to be larger (more rows) than the matrix "traits". Use only for null model 2 (regional.ind).
SE.reg.pool	A single value or vector of standard errors associated with each traits in each regional pool. Use only if reg.pool is used. Need to have the same dimension as reg.pool.
nperm	Number of permutations. If NULL, only observed values are returned;
printprogress	Logical value; print progress during the calculation or not.
independantTraits	Logical value (default: TRUE). If independantTraits is true (default), each traits is sample independently in null models, if not, each lines of the matrix are randomized, keeping the relation (and trade-off) among traits.
x	An object of class Tstats.
height	An object of class Tstats.
object	An object of class Tstats.
val.quant	Numeric vectors of length 2, giving the quantile to calculation confidence interval. By default val.quant = c(0.025,0.975) for a bilateral test with alpha = 5%.
ylim	Numeric vectors of length 2, giving the y coordinates range
col.index	A vector of three color correspond to the three T-statistics.
color.cond	Logical value; If color.cond = TRUE, color points indicate T-statistics values significatively different from the null model and grey points are not different from null model.
type	For the plot function, type of plot. Possible type = "simple", "simple_range", "normal", "barplot" and "bytraits". For the summary function, type of summary statistics. Either "binary", "percent", "p.value", "site" or "all".
add.conf	Logical value; Add confidence intervals or not.
...	Any additional arguments are passed to the plot function creating the core of the plot and can be used to adjust the look of resulting graph. See plot.listofindex for more arguments.

Details

S3 method plot:

-Normal type plot means, standard deviations, ranges and confidence intervals of T-statistics.

-Simple_range type plot means, standard deviations and range of T-statistics

-Simple type plot T-statistics for each site and traits and the mean confidence intervals by traits

-Barplot type plot means, standard deviations and confidence intervals of T-statistics in a barplot fashion

-Bysites type plot each metrics for each sites

-Bytraits type plot each metrics for each traits

S3 method print: print the structure if the object of class Tstats

S3 method summary: print the summary statistics of the three T-statistics

Method summary sum_Tstats:

-Binary type only test if a T-statistics is significatively different from the null expectation for each trait.

-Percent type determine the percentage of sites were the T-statistics is significatively different from the null expectation for each trait. Asterix shows global significance of the test.

-P-value type determine the p-value (two unilateral tests) of the T-statistics for each trait and sites.

-Site type allows to know in which sites T-statistics deviate from the null expectation.

-All type do all the precedent type of summary.

Value

A list of statistics:

Tstats$T_IP.IC	Observed ratio between variance of individuals in populations and individuals in communities
Tstats$T_IC.IR	Observed ratio between variance of individuals in communities and individuals in the region
Tstats$T_PC.PR	Observed ratio between variance of populations in communities and populations in the region
$Tstats$T_IP.IC_nm	If nperm is numeric; Result of simulation for T_IP.IC
$Tstats$T_IC.IR_nm	If nperm is numeric; Result of simulation for T_IC.IR
$Tstats$T_PC.PR_nm	If nperm is numeric; Result of simulation for T_PC.PR
$variances$var_IP	variance of individuals within populations
$variances$var_PC	variance of populations within communities
$variances$var_CR	variance of communities within the region
$variances$var_IC	variance of individuals within communities
$variances$var_PR	variance of populations within the region
$variances$var_IR	variance of individuals within the region
$variances$var_IP_nm1	variance of individuals within populations in null model 1
$variances$var_PC_nm2sp	variance of populations within communities in null model 2sp
$variances$var_IC_nm1	variance of communities within the region in null model 1
$variances$var_IC_nm2	variance of individuals within communities in null model 2
$variances$var_PR_nm2sp	variance of populations within the region in null model 2sp
$variances$var_IR_nm2	variance of individuals within the region in null model 2
$traits	traits data
$ind.plot	name of the plot in which the individual is
$sp	groups (e.g. species) which the individual belong to
$call	call of the function Tstats

Author(s)

Adrien Taudiere and Cyrille Violle

References

Violle, Cyrille, Brian J. Enquist, Brian J. McGill, Lin Jiang, Cecile H. Albert, Catherine Hulshof, Vincent Jung, et Julie Messier. 2012. The return of the variance: intraspecific variability in community ecology. Trends in Ecology & Evolution 27 (4): 244-252. doi:10.1016/j.tree.2011.11.014.

See Also

ComIndex; ComIndexMulti; plotCorTstats; plotSESvar; plot.listofindex

Examples

	data(finch.ind)
	
## Not run: 
	res.finch <- Tstats(traits.finch, ind.plot = ind.plot.finch, 
	sp = sp.finch, nperm = 9, print = FALSE)
	
	res.finch

	#Tstats class is associated to S3 methods plot, barplot and summary
	
	plot(res.finch)
	
	
	plot(res.finch, type = "simple")
	plot(res.finch, type = "simple_range")
	plot(res.finch, type = "barplot")
	plot(res.finch, type = "bysites")
	plot(res.finch, type = "bytraits")

	
	sum_Tstats(res.finch, type = "binary")
	sum_Tstats(res.finch, type = "site")
	sum_Tstats(res.finch, type = "p.value")

	barplot(res.finch)
	
	#### An other way to see "ses values" of T-statistics
	
	# Custom theme (from rasterVis package)
	require(rasterVis)
	
	my.theme <- BuRdTheme()
	# Customize the colorkey
	my.ckey <- list(col = my.theme$regions$col)
	
	levelplot(t(ses(res.finch$Tstats$T_IP.IC,res.finch$Tstats$T_IP.IC_nm)$ses), 
	colorkey = my.ckey, par.settings = my.theme,border = "black")
	
	#### Use a different regional pool than the binding of studied communities
	#create a random regional pool for the example
	
	reg.p <- rbind(traits.finch, traits.finch[sample(1:2000,300), ])
	
	res.finch2 <- Tstats(traits.finch, ind.plot = ind.plot.finch, 
	   sp = sp.finch, reg.pool=reg.p, nperm = 9, print = FALSE)	
	    
	plot(as.listofindex(list(res.finch,res.finch2)))
  
	#### Use a different regional pool for each communities
	#create a random regional pool for each communities for the example
	
	list.reg.p <- list(
	traits.finch[sample(1:290,200), ], traits.finch[sample(100:1200,300), ], 
	traits.finch[sample(100:1500, 1000), ], traits.finch[sample(300:800,300), ],
	traits.finch[sample(1000:2000, 500), ], traits.finch[sample(100:900, 700), ] )

	# Warning: the regional pool need to be larger than the observed communities
	table(ind.plot.finch)
	# For exemple, the third community need a regional pool of more than 981 individuals
		
	res.finch3 <- Tstats(traits.finch, ind.plot = ind.plot.finch, 
	   sp = sp.finch, reg.pool=list.reg.p, nperm = 9, print = FALSE)	
	    
	plot(as.listofindex(list(res.finch, res.finch2, res.finch3)))	

	#### Use the standard errors of measure in the analysis (argument SE)
	res.finch.SE0 <- Tstats(traits.finch, ind.plot = ind.plot.finch, 
	sp = sp.finch, SE = 0, print = FALSE)
		
	res.finch.SE5 <- Tstats(traits.finch, ind.plot = ind.plot.finch, 
	sp = sp.finch, SE = 5, print = FALSE)
	
	plot(as.listofindex(list(res.finch.SE0, res.finch.SE5)))
	
## End(Not run)

cati documentation built on March 18, 2022, 5:43 p.m.