Tstats {cati} R Documentation

## 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)


[Package cati version 0.99.4 Index]