VGAM-package: Vector Generalized Linear and Additive Models

Description

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

Arguments

Warning

This package is undergoing continual development and improvement. Until my monograph comes out and this package is released as version 1.0-0 the user should treat everything subject to change. This includes the family function names, argument names, many of the internals, the use of link functions, and slot names. Some future pain can be minimized by using good programming techniques, e.g., using extractor/accessor functions such as coef(), weights(), vcov(), predict(). Nevertheless, please expect changes in all aspects of the package. See the NEWS file for a list of changes from version to version.

Details

This package centers on the iteratively reweighted least squares (IRLS) algorithm. Other key words include Fisher scoring, additive models, penalized likelihood, reduced-rank regression and constrained ordination. The central modelling functions are vglm, vgam, rrvglm, rcim, cqo, cao. For detailed control of fitting, each of these has its own control function, e.g., vglm.control. The package uses S4 (see methods-package). A companion package called VGAMdata contains some larger data sets which were shifted from VGAM.

The classes of GLMs and GAMs 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, time series, survival analysis, generalized estimating equations, extreme values, correlated binary data, quantile and expectile regression, bioassay data and nonlinear least-squares problems.

VGAMs are to VGLMs what GAMs are to GLMs. Vector smoothing (see vsmooth.spline) allows several additive predictors to be estimated as a sum of smooth functions of the covariates.

For a complete list of this package, use library(help = "VGAM"). New VGAM family functions are continually being written and added to the package. A monograph about VGLM and VGAMs etc. appeared in October 2015.

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. http://www.jstatsoft.org/v32/i10/.

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

My website for the VGAM package and book is at https://www.stat.auckland.ac.nz/~yee and I hope to put more resources there in the future, especially as relating to my book.

Examples

Run this code

# 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)


# Example 2; zero-inflated Poisson model
zdata <- data.frame(x2 = runif(nn <- 2000))
zdata <- transform(zdata, pstr0  = logit(-0.5 + 1*x2, inverse = TRUE),
                          lambda = loge(  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
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))


# 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))

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


# Example 5; GEV distribution for extremes
(fit5 <- vglm(maxtemp ~ 1, egev, 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
rlplot(fit5)

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