VGAM (version 0.9-0)

cao.control: Control Function for RR-VGAMs (CAO)

Description

Algorithmic constants and parameters for a constrained additive ordination (CAO), by fitting a reduced-rank vector generalized additive model (RR-VGAM), are set using this function. This is the control function for cao.

Usage

cao.control(Rank = 1, all.knots = FALSE, criterion = "deviance", Cinit=NULL,
            Crow1positive=TRUE, epsilon = 1.0e-05, Etamat.colmax = 10,
            GradientFunction=FALSE, iKvector = 0.1, iShape = 0.1,
            Norrr = ~ 1, SmallNo = 5.0e-13, Use.Init.Poisson.QO=TRUE,
            Bestof = if (length(Cinit)) 1 else 10, maxitl = 10,
            imethod = 1, bf.epsilon = 1.0e-7, bf.maxit = 10,
            Maxit.optim = 250, optim.maxit = 20, SD.sitescores = 1.0,
            SD.Cinit = 0.02, trace = TRUE, df1.nl = 2.5, df2.nl = 2.5,
            spar1 = 0, spar2 = 0, ...)

Arguments

Rank

The numerical rank $R$ of the model, i.e., the number of latent variables. Currently only Rank=1 is implemented.

all.knots

Logical indicating if all distinct points of the smoothing variables are to be used as knots. Assigning the value FALSE means fewer knots are chosen when the number of distinct points is large, meaning less computational expense.

criterion

Convergence criterion. Currently, only one is supported: the deviance is minimized.

Cinit

Optional initial C matrix which may speed up convergence.

Crow1positive

Logical vector of length Rank (recycled if necessary): are the elements of the first row of C positive? For example, if Rank is 4, then specifying Crow1positive=c(FALSE, TRUE) will force C[1,1]

epsilon

Positive numeric. Used to test for convergence for GLMs fitted in FORTRAN. Larger values mean a loosening of the convergence criterion.

Etamat.colmax

Positive integer, no smaller than Rank. Controls the amount of memory used by .Init.Poisson.QO(). It is the maximum number of columns allowed for the pseudo-response and its weights. In general, the larger the value

GradientFunction

Logical. Whether optim's argument gr is used or not, i.e., to compute gradient values. Used only if FastAlgorithm is TRUE. Currently, this argument must be

iKvector, iShape

See qrrvglm.control.

Norrr

Formula giving terms that are not to be included in the reduced-rank regression (or formation of the latent variables). The default is to omit the intercept term from the latent variables. Currently, only Norrr = ~ 1 is i

SmallNo

Positive numeric between .Machine$double.eps and 0.0001. Used to avoid under- or over-flow in the IRLS algorithm.

Use.Init.Poisson.QO

Logical. If TRUE then the function .Init.Poisson.QO is used to obtain initial values for the canonical coefficients C. If FALSE then random numbers are used instead.

Bestof

Integer. The best of Bestof models fitted is returned. This argument helps guard against local solutions by (hopefully) finding the global solution from many fits. The argument works only when the function generates its own initia

maxitl

Positive integer. Maximum number of Newton-Raphson/Fisher-scoring/local-scoring iterations allowed.

imethod

See qrrvglm.control.

bf.epsilon

Positive numeric. Tolerance used by the modified vector backfitting algorithm for testing convergence.

bf.maxit

Positive integer. Number of backfitting iterations allowed in the compiled code.

Maxit.optim

Positive integer. Number of iterations given to the function optim at each of the optim.maxit iterations.

optim.maxit

Positive integer. Number of times optim is invoked.

SD.sitescores

Numeric. Standard deviation of the initial values of the site scores, which are generated from a normal distribution. Used when Use.Init.Poisson.QO is FALSE.

SD.Cinit

Standard deviation of the initial values for the elements of C. These are normally distributed with mean zero. This argument is used only if Use.Init.Poisson.QO = FALSE.

trace

Logical indicating if output should be produced for each iteration. Having the value TRUE is a good idea for large data sets.

df1.nl, df2.nl

Numeric and non-negative, recycled to length S. Nonlinear degrees of freedom for smooths of the first and second latent variables. A value of 0 means the smooth is linear. Roughly, a value between 1.0 and 2.0 often has the approx

spar1, spar2

Numeric and non-negative, recycled to length S. Smoothing parameters of the smooths of the first and second latent variables. The larger the value, the more smooth (less wiggly) the fitted curves. These arguments are an alternativ

...

Ignored at present.

Value

• A list with the components corresponding to its arguments, after some basic error checking.

Details

Many of these arguments are identical to qrrvglm.control. Here, $R$ is the Rank, $M$ is the number of additive predictors, and $S$ is the number of responses (species). Thus $M=S$ for binomial and Poisson responses, and $M=2S$ for the negative binomial and 2-parameter gamma distributions.

Allowing the smooths too much flexibility means the CAO optimization problem becomes more difficult to solve. This is because the number of local solutions increases as the nonlinearity of the smooths increases. In situations of high nonlinearity, many initial values should be used, so that Bestof should be assigned a larger value. In general, there should be a reasonable value of df1.nl somewhere between 0 and about 3 for most data sets.

References

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

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

See Also

cao.

Examples

hspider[,1:6] = scale(hspider[,1:6]) # Standardized environmental vars
set.seed(123)
ap1 = cao(cbind(Pardlugu, Pardmont, Pardnigr, Pardpull, Zoraspin) ~
         WaterCon + BareSand + FallTwig +
         CoveMoss + CoveHerb + ReflLux,
         family = poissonff, data = hspider,
         df1.nl = c(Zoraspin=2.3, 2.1),
         Bestof = 10, Crow1positive = FALSE)
sort(ap1@misc$deviance.Bestof) # A history of all the iterations

Coef(ap1)

par(mfrow=c(2,3)) # All or most of the curves are unimodal; some are
plot(ap1, lcol = "blue") # quite symmetric. Hence a CQO model should be ok

par(mfrow=c(1,1), las=1)
index = 1:ncol(ap1@y)  # lvplot is jagged because only 28 sites
lvplot(ap1, lcol = index, pcol = index, y=TRUE)

trplot(ap1, label=TRUE, col=index)
abline(a = 0, b = 1, lty = 2)

persp(ap1, label=TRUE, col=1:4)