# Fitting Vector Generalized Additive Models Fit a vector generalized additive model (VGAM). Both 1st-generation VGAMs (based on backfitting) and 2nd-generation VGAMs (based on P-splines, with automatic smoothing parameter selection) are implemented. This is a large class of models that includes generalized additive models (GAMs) and vector generalized linear models (VGLMs) as special cases. ```r vgam(formula, family = stop("argument 'family' needs to be assigned"), data = list(), weights = NULL, subset = NULL, na.action, etastart = NULL, mustart = NULL, coefstart = NULL, control = vgam.control(...), offset = NULL, method = "vgam.fit", model = FALSE, x.arg = TRUE, y.arg = TRUE, contrasts = NULL, constraints = NULL, extra = list(), form2 = NULL, qr.arg = FALSE, smart = TRUE, ...) ``` ## Arguments - `formula`: a symbolic description of the model to be fit. The RHS of the formula is applied to each linear/additive predictor, and should include at least one `sm.os` term or `sm.ps` term or `s` term. Mixing both together is not allowed. Different variables in each linear/additive predictor can be chosen by specifying constraint matrices. - `family`: Same as for `vglm`. - `data`: an optional data frame containing the variables in the model. By default the variables are taken from `environment(formula)`, typically the environment from which `vgam` is called. - `weights, subset, na.action`: Same as for `vglm`. Note that `subset` may be unreliable and to get around this problem it is best to use `subset` to create a new smaller data frame and feed in the smaller data frame. See below for an example. This is a bug that needs fixing. - `etastart, mustart, coefstart`: Same as for `vglm`. - `control`: a list of parameters for controlling the fitting process. See `vgam.control` for details. - `method`: the method to be used in fitting the model. The default (and presently only) method `vgam.fit` uses iteratively reweighted least squares (IRLS). - `constraints, model, offset`: Same as for `vglm`. - `x.arg, y.arg`: logical values indicating whether the model matrix and response vector/matrix used in the fitting process should be assigned in the `x` and `y` slots. Note the model matrix is the LM model matrix; to get the VGAM model matrix type `model.matrix(vgamfit)` where `vgamfit` is a `vgam` object. - `contrasts, extra, form2, qr.arg, smart`: Same as for `vglm`. - ``...``: further arguments passed into `vgam.control`. ## Details A vector generalized additive model (VGAM) is loosely defined as a statistical model that is a function of $M$ additive predictors. The central formula is given by $$ \eta_j = \sum_{k=1}^p f_{(j)k}(x_k)%eta_j = sum_{k=1}^p f_{(j)k}(x_k) $$ where $x_k$ is the $k$th explanatory variable (almost always $x_1=1$ for the intercept term), and $f_{(j)k}$ are smooth functions of $x_k$ that are estimated by smoothers. The first term in the summation is just the intercept. Currently two types of smoothers are implemented: `s` represents the older and more traditional one, called a **vector (cubic smoothing spline) smoother** and is based on Yee and Wild (1996); it is more similar to the package `gam`. The newer one is represented by `sm.os` and `sm.ps`, and these are based on O-splines and P-splines---they allow automatic smoothing parameter selection; it is more similar to the package `mgcv`. In the above, $j=1,\ldots,M$ where $M$ is finite. If all the functions are constrained to be linear then the resulting model is a vector generalized linear model (VGLM). VGLMs are best fitted with `vglm`. Vector (cubic smoothing spline) smoothers are represented by `s()` (see `s`). Local regression via `lo()` is **not** supported. The results of `vgam` will differ from the `gam()` (in the `gam`) because `vgam()` uses a different knot selection algorithm. In general, fewer knots are chosen because the computation becomes expensive when the number of additive predictors $M$ is large. Second-generation VGAMs are based on the O-splines and P-splines. The latter is due to Eilers and Marx (1996). Backfitting is not required, and estimation is performed using IRLS. The function `sm.os` represents a **smart** implementation of O-splines. The function `sm.ps` represents a **smart** implementation of P-splines. Written G2-VGAMs or P-VGAMs, this methodology should not be used unless the sample size is reasonably large. Usually an UBRE predictive criterion is optimized (at each IRLS iteration) because the scale parameter for VGAMs is usually assumed to be known. This search for optimal smoothing parameters does not always converge, and neither is it totally reliable. G2-VGAMs implicitly set `criterion = "coefficients"` so that convergence occurs when the change in the regression coefficients between 2 IRLS iterations is sufficiently small. Otherwise the search for the optimal smoothing parameters might cause the log-likelihood to decrease between 2 IRLS iterations. Currently **outer iteration** is implemented, by default, rather than **performance iteration** because the latter is more easy to converge to a local solution; see Wood (2004) for details. One can use **performance iteration** by setting `Maxit.outer = 1` in `vgam.control`. The underlying algorithm of VGAMs is IRLS. First-generation VGAMs (called G1-VGAMs) are estimated by modified vector backfitting using vector splines. O-splines are used as the basis functions for the vector (smoothing) splines, which are a lower dimensional version of natural B-splines. The function `vgam.fit()` actually does the work. The smoothing code is based on F. O'Sullivan's BART code. A closely related methodology based on VGAMs called **constrained additive ordination** (CAO) first forms a linear combination of the explanatory variables (called **latent variables**) and then fits a GAM to these. This is implemented in the function `cao` for a very limited choice of family functions. ## Returns For G1-VGAMs and G2-VGAMs, an object of class `"vgam"` or `"pvgam"` respectively (see `vgam-class` and `pvgam-class` for further information). ## References Wood, S. N. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. **J. Amer. Statist. Assoc.**, 99 (467): 673--686. 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. (2008). The `VGAM` Package. **R News**, 8 , 28--39. Yee, T. W. (2015). Vector Generalized Linear and Additive Models: With an Implementation in R. New York, USA: **Springer**. Yee, T. W. (2016). Comments on ``Smoothing parameter and model selection for general smooth models'' by Wood, S. N. and Pya, N. and Safken, N., **J. Amer. Statist. Assoc.**, 110 (516). ## Author(s) Thomas W. Yee ## Note This function can fit a wide variety of statistical models. Some of these are harder to fit than others because of inherent numerical difficulties associated with some of them. Successful model fitting benefits from cumulative experience. Varying the values of arguments in the `VGAM` family function itself is a good first step if difficulties arise, especially if initial values can be inputted. A second, more general step, is to vary the values of arguments in `vgam.control`. A third step is to make use of arguments such as `etastart`, `coefstart` and `mustart`. Some `VGAM` family functions end in `"ff"` to avoid interference with other functions, e.g., `binomialff`, `poissonff`. This is because `VGAM` family functions are incompatible with `glm` (and also `gam()` in `gam` and `gam` in `mgcv`). The smart prediction (`smartpred`) library is packed with the `VGAM` library. The theory behind the scaling parameter is currently being made more rigorous, but it it should give the same value as the scale parameter for GLMs. ## WARNING For G1-VGAMs, currently `vgam` can only handle constraint matrices `cmat`, say, such that `crossprod(cmat)` is diagonal. It can be detected by `is.buggy`. VGAMs with constraint matrices that have non-orthogonal columns should be fitted with `sm.os` or `sm.ps` terms instead of `s`. See warnings in `vglm.control`. ## See Also `is.buggy`, `vgam.control`, `vgam-class`, `vglmff-class`, `plotvgam`, `summaryvgam`, `summarypvgam`, `sm.os`, `sm.ps`, `s`, `magic`, `vglm`, `vsmooth.spline`, `cao`. ## Examples ```r # Nonparametric proportional odds model pneumo <- transform(pneumo, let = log(exposure.time)) vgam(cbind(normal, mild, severe) ~ s(let), cumulative(parallel = TRUE), data = pneumo, trace = TRUE) # Nonparametric logistic regression hfit <- vgam(agaaus ~ s(altitude, df = 2), binomialff, hunua) ## Not run: plot(hfit, se = TRUE) phfit <- predict(hfit, type = "terms", raw = TRUE, se = TRUE) names(phfit) head(phfit$fitted) head(phfit$se.fit) phfit$df phfit$sigma ## Not run: # Fit two species simultaneously hfit2 <- vgam(cbind(agaaus, kniexc) ~ s(altitude, df = c(2, 3)), binomialff(multiple.responses = TRUE), data = hunua) coef(hfit2, matrix = TRUE) # Not really interpretable plot(hfit2, se = TRUE, overlay = TRUE, lcol = 3:4, scol = 3:4) ooo <- with(hunua, order(altitude)) with(hunua, matplot(altitude[ooo], fitted(hfit2)[ooo,], ylim = c(0, 0.8), las = 1,type = "l", lwd = 2, xlab = "Altitude (m)", ylab = "Probability of presence", main = "Two plant species' response curves")) with(hunua, rug(altitude)) # The 'subset' argument does not work here. Use subset() instead. set.seed(1) zdata <- data.frame(x2 = runif(nn <- 500)) zdata <- transform(zdata, y = rbinom(nn, 1, 0.5)) zdata <- transform(zdata, subS = runif(nn) < 0.7) sub.zdata <- subset(zdata, subS) # Use this instead if (FALSE) fit4a <- vgam(cbind(y, y) ~ s(x2, df = 2), binomialff(multiple.responses = TRUE), data = zdata, subset = subS) # This fails!!! fit4b <- vgam(cbind(y, y) ~ s(x2, df = 2), binomialff(multiple.responses = TRUE), data = sub.zdata) # This succeeds!!! fit4c <- vgam(cbind(y, y) ~ sm.os(x2), binomialff(multiple.responses = TRUE), data = sub.zdata) # This succeeds!!! par(mfrow = c(2, 2)) plot(fit4b, se = TRUE, shade = TRUE, shcol = "pink") plot(fit4c, se = TRUE, shade = TRUE, shcol = "pink") ## End(Not run) ```

vgam function