VGAM-package: Vector Generalized Linear and Additive Models and Other...

In VGAM: Vector Generalized Linear and Additive Models

Vector Generalized Linear and Additive Models and Other Associated Models

Description

VGAM provides functions for fitting vector generalized linear and additive models (VGLMs and VGAMs), and associated models (Reduced-rank VGLMs or RR-VGLMs, Doubly constrained RR-VGLMs (DRR-VGLMs), Quadratic RR-VGLMs, Reduced-rank VGAMs). This package fits many models and distributions by maximum likelihood estimation (MLE) or penalized MLE, under this statistical framework. Also fits constrained ordination models in ecology such as constrained quadratic ordination (CQO).

Details

This package centers on the iteratively reweighted least squares (IRLS) algorithm. Other key words include Fisher scoring, additive models, reduced-rank regression, penalized likelihood, and constrained ordination. The central modelling functions are vglm, vgam, rrvglm, rcim, cqo, cao. Function vglm operates very similarly to glm but is much more general, and many methods functions such as coef and predict are available. The package uses S4 (see methods-package).

Some notable companion packages: (1) VGAMdata mainly contains data sets useful for illustrating VGAM. Some of the big ones were initially from VGAM. Recently, some older VGAM family functions have been shifted into this package. (2) VGAMextra written by Victor Miranda has some additional VGAM family and link functions, with a bent towards time series models. (3) svyVGAM provides design-based inference, e.g., to survey sampling settings. This is because the weights argument of vglm can be assigned any positive values including survey weights.

Compared to other similar packages, such as gamlss and mgcv, VGAM has more models implemented (150+ of them) and they are not restricted to a location-scale-shape framework or (largely) the 1-parameter exponential family. The general statistical framework behind it all, once grasped, makes regression modelling unified. Some features of the package are: (i) many family functions handle multiple responses; (ii) reduced-rank regression is available by operating on latent variables (optimal linear combinations of the explanatory variables); (iii) basic automatic smoothing parameter selection is implemented for VGAMs (sm.os and sm.ps with a call to magic), although it has to be refined; (iv) smart prediction allows correct prediction of nested terms in the formula provided smart functions are used.

The GLM and GAM classes are special cases of VGLMs and VGAMs. The VGLM/VGAM framework is intended to be very general so that it encompasses as many distributions and models as possible. VGLMs are limited only by the assumption that the regression coefficients enter through a set of linear predictors. The VGLM class is very large and encompasses a wide range of multivariate response types and models, e.g., it includes univariate and multivariate distributions, categorical data analysis, extreme values, correlated binary data, quantile and expectile regression, time series problems. Potentially, it can handle generalized estimating equations, survival analysis, bioassay data and nonlinear least-squares problems.

Crudely, VGAMs are to VGLMs what GAMs are to GLMs. Two types of VGAMs are implemented: 1st-generation VGAMs with s use vector backfitting, while 2nd-generation VGAMs with sm.os and sm.ps use O-splines and P-splines so have a direct solution (hence avoids backfitting) and have automatic smoothing parameter selection. The former is older and is based on Yee and Wild (1996). The latter is more modern (Yee, Somchit and Wild, 2024) but it requires a reasonably large number of observations to work well because it is based on optimizing over a predictive criterion rather than using a Bayesian approach.

An important feature of the framework is that of constraint matrices. They apportion the regression coefficients according to each explanatory variable. For example, since each parameter has a link function applied to it to turn it into a linear or additive predictor, does a covariate have an equal effect on each parameter? Or no effect? Arguments such as zero, parallel and exchangeable, are merely easy ways to have them constructed internally. Users may input them explicitly using the constraint argument, and CM.symm0 etc. can make this easier.

Another important feature is implemented by xij. It allows different linear/additive predictors to have a different values of the same explanatory variable, e.g., multinomial for the conditional logit model and the like.

VGLMs with dimension reduction form the class of RR-VGLMs. This is achieved by reduced rank regression. Here, a subset of the constraint matrices are estimated rather than being known and prespecified. Optimal linear combinations of the explanatory variables are taken (creating latent variables) which are used for fitting a VGLM. Thus the regression can be thought of as being in two stages. The class of DRR-VGLMs provides further structure to RR-VGLMs by allowing constraint matrices to be specified for each column of A and row of C. Thus the reduced rank regression can be fitted with greater control.

This package is the first to check for the Hauck-Donner effect (HDE) in regression models; see hdeff. This is an aberration of the Wald statistics when the parameter estimates are too close to the boundary of the parameter space. When present the p-value of a regression coefficient is biased upwards so that a highly significant variable might be deemed nonsignificant. Thus the HDE can create havoc for variable selection!

Somewhat related to the previous paragraph, hypothesis testing using the likelihood ratio test, Rao's score test (Lagrange multiplier test) and (modified) Wald's test are all available; see summaryvglm. For all regression coefficients of a model, taken one at a time, all three methods require further IRLS iterations to obtain new values of the other regression coefficients after one of the coefficients has had its value set (usually to 0). Hence the computation load is overall significant.

For a complete list of this package, use library(help = "VGAM"). New VGAM family functions are continually being written and added to the package.

Warning

This package is undergoing continual development and improvement, therefore users should treat many things as subject to change. This includes the family function names, argument names, many of the internals, moving some functions to VGAMdata, the use of link functions, and slot names. For example, many link functions were renamed in 2019 so that they all end in "link", e.g., loglink() instead of loge(). Some future pain can be avoided by using good programming techniques, e.g., using extractor functions such as coef(), weights(), vcov(), predict(). Although changes are now less frequent, please expect changes in all aspects of the package. See the NEWS file for a list of changes from version to version.

Author(s)

Thomas W. Yee, [e-mail], with contributions from Victor Miranda and several graduate students over the years, especially Xiangjie (Albert) Xue and Chanatda Somchit.

Maintainer: Thomas Yee [e-mail].

References

Yee, T. W. (2015). Vector Generalized Linear and Additive Models: With an Implementation in R. New York, USA: Springer.

Yee, T. W. and Hastie, T. J. (2003). Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.

Yee, T. W. and Stephenson, A. G. (2007). Vector generalized linear and additive extreme value models. Extremes, 10, 1–19.

Yee, T. W. and Wild, C. J. (1996). Vector generalized additive models. Journal of the Royal Statistical Society, Series B, Methodological, 58, 481–493.

Yee, T. W. (2004). A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685–701.

Yee, T. W. (2006). Constrained additive ordination. Ecology, 87, 203–213.

Yee, T. W. (2008). The VGAM Package. R News, 8, 28–39.

Yee, T. W. (2010). The VGAM package for categorical data analysis. Journal of Statistical Software, 32, 1–34. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v032.i10")}.

Yee, T. W. (2014). Reduced-rank vector generalized linear models with two linear predictors. Computational Statistics and Data Analysis, 71, 889–902.

Yee, T. W. and Ma, C. (2024). Generally altered, inflated, truncated and deflated regression. Statistical Science, 39 (in press).

Yee, T. W. (2022). On the Hauck-Donner effect in Wald tests: Detection, tipping points and parameter space characterization, Journal of the American Statistical Association, 117, 1763–1774. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2021.1886936")}.

Yee, T. W. and Somchit, C. and Wild, C. J. (2024). Penalized vector generalized additive models. Manuscript in preparation.

The website for the VGAM package and book is https://www.stat.auckland.ac.nz/~yee/. There are some resources there, especially as relating to my book and new features added to VGAM.

Some useful background reference for the package include:

Chambers, J. and Hastie, T. (1991). Statistical Models in S. Wadsworth & Brooks/Cole.

Green, P. J. and Silverman, B. W. (1994). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. Chapman and Hall.

Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. Chapman and Hall.

See Also

vglm, vgam, rrvglm, rcim, cqo, TypicalVGAMfamilyFunction, CommonVGAMffArguments, Links, hdeff, glm, lm, https://CRAN.R-project.org/package=VGAM.

Examples

# Example 1; proportional odds model
pneumo <- transform(pneumo, let = log(exposure.time))
(fit1 <- vglm(cbind(normal, mild, severe) ~ let, propodds, data = pneumo))
depvar(fit1)  # Better than using fit1@y; dependent variable (response)
weights(fit1, type = "prior")  # Number of observations
coef(fit1, matrix = TRUE)      # p.179, in McCullagh and Nelder (1989)
constraints(fit1)              # Constraint matrices
summary(fit1)  # HDE could affect these results
summary(fit1, lrt0 = TRUE, score0 = TRUE, wald0 = TRUE)  # No HDE
hdeff(fit1)  # Check for any Hauck-Donner effect

# Example 2; zero-inflated Poisson model
zdata <- data.frame(x2 = runif(nn <- 2000))
zdata <- transform(zdata, pstr0  = logitlink(-0.5 + 1*x2, inverse = TRUE),
                          lambda = loglink(  0.5 + 2*x2, inverse = TRUE))
zdata <- transform(zdata, y = rzipois(nn, lambda, pstr0 = pstr0))
with(zdata, table(y))
fit2 <- vglm(y ~ x2, zipoisson, data = zdata, trace = TRUE)
coef(fit2, matrix = TRUE)  # These should agree with the above values


# Example 3; fit a two species GAM simultaneously
fit3 <- vgam(cbind(agaaus, kniexc) ~ s(altitude, df = c(2, 3)),
             binomialff(multiple.responses = TRUE), data = hunua)
coef(fit3, matrix = TRUE)   # Not really interpretable
## Not run:  plot(fit3, se = TRUE, overlay = TRUE, lcol = 3:4, scol = 3:4)

ooo <- with(hunua, order(altitude))
with(hunua,  matplot(altitude[ooo], fitted(fit3)[ooo, ], type = "l",
     lwd = 2, col = 3:4,
     xlab = "Altitude (m)", ylab = "Probability of presence", las = 1,
     main = "Two plant species' response curves", ylim = c(0, 0.8)))
with(hunua, rug(altitude)) 
## End(Not run)


# Example 4; LMS quantile regression
fit4 <- vgam(BMI ~ s(age, df = c(4, 2)), lms.bcn(zero = 1),
             data = bmi.nz, trace = TRUE)
head(predict(fit4))
head(fitted(fit4))
head(bmi.nz)  # Person 1 is near the lower quartile among people his age
head(cdf(fit4))

## Not run:  par(mfrow = c(1,1), bty = "l", mar = c(5,4,4,3)+0.1, xpd=TRUE)
qtplot(fit4, percentiles = c(5,50,90,99), main = "Quantiles", las = 1,
       xlim = c(15, 90), ylab = "BMI", lwd=2, lcol=4)  # Quantile plot

ygrid <- seq(15, 43, len = 100)  # BMI ranges
par(mfrow = c(1, 1), lwd = 2)  # Density plot
aa <- deplot(fit4, x0 = 20, y = ygrid, xlab = "BMI", col = "black",
    main = "Density functions at Age=20 (black), 42 (red) and 55 (blue)")
aa
aa <- deplot(fit4, x0 = 42, y = ygrid, add = TRUE, llty = 2, col = "red")
aa <- deplot(fit4, x0 = 55, y = ygrid, add = TRUE, llty = 4, col = "blue",
            Attach = TRUE)
aa@post$deplot  # Contains density function values

## End(Not run)


# Example 5; GEV distribution for extremes
(fit5 <- vglm(maxtemp ~ 1, gevff, data = oxtemp, trace = TRUE))
head(fitted(fit5))
coef(fit5, matrix = TRUE)
Coef(fit5)
vcov(fit5)
vcov(fit5, untransform = TRUE)
sqrt(diag(vcov(fit5)))  # Approximate standard errors
## Not run:  rlplot(fit5) 

VGAM documentation built on Sept. 18, 2024, 9:09 a.m.