gjam (version 2.6.2)

gjamIIE: Indirect effects and interactions for gjam data

Description

Evaluates direct, indirect, and interactions from a gjam object. Returns a list of objects that can be plotted by gjamIIEplot.

Usage

gjamIIE(output, xvector, MEAN = T, keepNames = NULL, omitY = NULL, 
          sdScaleX = T, sdScaleY = F)

Arguments

output

object of class inheriting from "gjam".

xvector

vector of predictor values, with names, corresponding to columns in output$x.

MEAN

logical, if false, then median used.

omitY

character vector of columns in output$y to omit from calculations.

keepNames

character vector of columns in output$y. If omitted, all columns used.

sdScaleX

standardize coefficients to X scale.

sdScaleY

standardize coefficients to correlation scale.

Value

A list of objects for plotting by gjamIIEplot.

Details

For plotting or recovering effects. The list fit$IIE has matrices for main effects (mainEffect), interactions (intEffect), direct effects (dirEffect), indirect effects (indEffectTo), and standard deviations for each. The direct effects are the sum of main effects and interactions. The indirect effects include main effects and interactions that come through other species, determined by covariance matrix sigma.

If sdScaleX = T effects are standandardized from the Y/X to Y scale. This is the typical standardization for predictor variables. If sdScaleY = T effects are given on the correlation scale. If both are true effects are dimensionless. See the gjam vignette on dimension reduction.

References

Clark, J.S., D. Nemergut, B. Seyednasrollah, P. Turner, and S. Zhang. 2016. Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs 87, 34-56.

See Also

gjamIIEplot plots output from gjamIIE

A more detailed vignette is can be obtained with:

browseVignettes('gjam')

web site 'http://sites.nicholas.duke.edu/clarklab/code/'.

Examples

# NOT RUN {
sim <- gjamSimData(S = 12, Q = 5, typeNames = 'CA')
ml  <- list(ng = 50, burnin = 5, typeNames = sim$typeNames)
out <- gjam(sim$formula, sim$xdata, sim$ydata, modelList = ml)

xvector <- colMeans(out$inputs$xStand)  #predict at mean values for data
xvector[1] <- 1

fit <- gjamIIE(output = out, xvector)

gjamIIEplot(fit, response = 'S1', effectMu = c('main','ind'), 
            effectSd = c('main','ind'), legLoc = 'topleft')
# }