goodnessOfFit: Goodness of fit measures for a gllvm

In gllvm: Generalized Linear Latent Variable Models

Goodness of fit measures for a gllvm

Description

Several goodness-of-fit measure are currently available and can be calculated for a gllvm model fit and predicted values.

Usage

goodnessOfFit(
  object = NULL,
  y = NULL,
  pred = NULL,
  measure = c("cor", "RMSE", "MAE", "MARNE"),
  species = FALSE
)

Arguments

object	an object of class 'gllvm', to calculate goodness of a model fit.
y	a response matrix of new observations
pred	predicted values for response matrix y if you want to calculate prediction accuracy for new values. Note that for ordinal model, you need to give the predicted classes.
measure	a goodness-of-fit measure to be calculated. Options are "cor" (correlation between observed and predicted values), "scor" (Spearman correlation between observed and predicted values), "RMSE" (root mean squared error of prediction), "MAE" (Mean Absolute Error), "MARNE" (Mean Absolute Range Normalized Error), "TjurR2" (Tjur's R2 measure, only for binary data), "R2" (R-squared as the square of the correlation), "AUC", "sR2" (R-squared as the square of the spearman correlation). Likelihood based pseudo R2 meaures "NagelkerkeR2", "McFaddenR2", "CoxSnellR2" can be calculated currently only for training data to measure the model's goodness of fit for full data, not response specific.
species	logical, if TRUE, goodness-of-fit measures are calculated for each species separately. If FALSE, goodness-of-fit measures are calculated for all species together.

Details

goodnessOfFit is used for evaluating the goodness-of-fit of a model or predictions. Available goodness-of-fit measures are correlation, RMSE, MARNE, and R2 measures. Definitions are below. Denote an observed response j (species) at sample i, i=1,...,n, as y_{ij}, and predicted value as \hat y_{ij}.

RMSE(\boldsymbol{y_{j}}, \boldsymbol{\hat y_{j}}) = \sqrt{\frac{1}{n}\Sigma_{i=1}^{n} {(y_{ij} - \hat y_{ij})^2}}

MAE(\boldsymbol{y_{j}}, \boldsymbol{\hat y_{j}}) = \frac{1}{n}\Sigma_{i=1}^{n} |y_{ij} - \hat y_{ij}|

MARNE(\boldsymbol{y_{j}}, \boldsymbol{\hat y_{j}}) = \frac{1}{n}\Sigma_{i=1}^{n} \frac{|y_{ij} - \hat y_{ij}|}{max(\boldsymbol{y_{j}}) - min(\boldsymbol{y_{j}})}

Tjur's R2(\boldsymbol{y_{j}}, \boldsymbol{\hat y_{j}}) = \frac{1}{n_1}\Sigma \hat y_{ij}\boldsymbol{1}_{y=1}(y_{ij}) - \frac{1}{n_0}\Sigma \hat y_{ij}\boldsymbol{1}_{y=0}(y_{ij})

Author(s)

Jenni Niku <[e-mail]>

See Also

gllvm, predict.gllvm

Examples

## Not run: 
# Fit gllvm model with Poisson family
data(microbialdata)
X <- microbialdata$Xenv
y <- microbialdata$Y[, order(colMeans(microbialdata$Y > 0), 
                     decreasing = TRUE)[21:40]]
fit <- gllvm(y, X, formula = ~ pH + Phosp, family = poisson())
# Calculate metrics
goodnessOfFit(object = fit, measure = c("cor", "RMSE"))


## End(Not run)

gllvm documentation built on May 10, 2026, 5:06 p.m.