gllvm (version 2.0.5)

VP.gllvm: Calculate variance partitioning

Description

Calculates variance partitioning for gllvm object with function VP() (alias varPartitioning()).

Function plotVarPartitioning() (alias plotVP() or just plot()) plots the results of variance partitioning of a fitted gllvm.

Usage

# S3 method for gllvm
VP(
  object,
  group = NULL,
  groupnames = NULL,
  adj.cov = TRUE,
  grouplvs = FALSE,
  ...
)
# S3 method for VP.gllvm
print(x, ...)
plotVarPartitioning(
  x,
  main = "Variance Partitioning",
  xlab = "Response",
  ylab = "Variance proportion",
  legend.text = NULL,
  args.legend = list(cex = 0.7, x = "topright", bty = "n", inset = c(0, -0.15)),
  mar = c(4, 4, 6, 2),
  ...
)
plotVP(x, ...)
# S3 method for VP.gllvm
plot(x, ...)

Arguments

object

an object of class 'gllvm'.

group

a vector of integers identifying grouping of X covariates, the default is to use model terms formula and lv.formula.

groupnames

a vector of strings given as names for the groups defined in group

adj.cov

logical, whether or not to adjust co-variation within the group

grouplvs

logical, whether or not to group latent variables to one group

...

additional graphical arguments passed to the barplot function

x

a variance partitioning object for a gllvm produced by function varPartitioning.

main

main title

xlab

a label for the x axis.

ylab

a label for the y axis.

legend.text

a vector of names for the groups, as a default 'groupnames' from varPartitioning. If FALSE, legend not printed.

args.legend

a list of additional arguments to pass to legend().

mar

Margins of the plot. Default c(4,4,6,2)

Author

Jenni Niku <[e-mail]>

Details

Variance for the linear predictor for response j can be calculated as

$$Var(\eta_j) = \sum_k \beta_{jk}^2*var(z_{.k}) + 2 \sum_{(k1=1,...,K-1)} \sum_{(k2=k1+1,...,K)} \beta_{j(k1)}\beta_{j(k2)} Cov(Z_{.k1},Z_{.k2}) , $$

where \(z_{.k}\) is a vector consisting of predictor/latent variable/row effect etc values for all sampling units i. If \(z_{.k}\)s are not correlated, covariance term is 0 and thus the variance explained of a response j for predictor \(z_{.k}\) is given as \(\beta_{jk}^2*var(z_{.k})/Var(\eta_j)\).

In case of correlated predictors, it is advised to group them into a same group. The variance explained is calculated for the correlated group of predictors together and adjusted with the covariance term.

Examples

# Extract subset of the microbial data to be used as an example
data(microbialdata)
X <- microbialdata$Xenv
y <- microbialdata$Y[, order(colMeans(microbialdata$Y > 0), 
                     decreasing = TRUE)[21:40]]
fit <- gllvm(y, X[,1:3], formula = ~ pH + Phosp, family = poisson(), 
             studyDesign = X[,4:5], row.eff = ~(1|Site))
VP <- VP(fit)
plot(VP)

if (FALSE) {
# Plot the result of  variance partitioning
plot(VP, col = palette(hcl.colors(5, "Roma")))

}