VP.gllvm: Calculate variance partitioning

In gllvm: Generalized Linear Latent Variable Models

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 class 'gllvm'
VP(
  object,
  group = NULL,
  groupnames = NULL,
  adj.cov = TRUE,
  grouplvs = FALSE,
  ...
)

## S3 method for class '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 class '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)

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.

Author(s)

Jenni Niku <[e-mail]>

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)

## Not run: 
# Plot the result of  variance partitioning
plot(VP, col = palette(hcl.colors(5, "Roma")))


## End(Not run)

gllvm documentation built on April 3, 2025, 6:09 p.m.