gjamIIE: Indirect effects and interactions for gjam data

In gjam: Generalized Joint Attribute Modeling

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.

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.

Value

A list of objects for plotting by gjamIIEplot.

Author(s)

James S Clark, [e-mail]

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')

## End(Not run)

gjam documentation built on May 24, 2022, 1:06 a.m.