rcqo {VGAM} | R Documentation

## Constrained Quadratic Ordination

### Description

Random generation for constrained quadratic ordination (CQO).

### Usage

    rcqo(n, p, S, Rank = 1,
         family = c("poisson", "negbinomial", "binomial-poisson",
                    "Binomial-negbinomial", "ordinal-poisson",
                    "Ordinal-negbinomial", "gamma2"),
         eq.maximums = FALSE, eq.tolerances = TRUE, es.optimums = FALSE,
         lo.abundance = if (eq.maximums) hi.abundance else 10,
         hi.abundance = 100, sd.latvar = head(1.5/2^(0:3), Rank),
         sd.optimums = ifelse(es.optimums, 1.5/Rank, 1) *
                           ifelse(scale.latvar, sd.latvar, 1),
         sd.tolerances = 0.25, Kvector = 1, Shape = 1,
         sqrt.arg = FALSE, log.arg = FALSE, rhox = 0.5, breaks = 4,
         seed = NULL, optimums1.arg = NULL, Crow1positive = TRUE,
         xmat = NULL, scale.latvar = TRUE)

### Arguments

`n` | Number of sites. It is denoted by n below.
`p` | Number of environmental variables, including an intercept term. It is denoted by p below. Must be no less than 1+R in value.
`S` | Number of species. It is denoted by S below.
`Rank` | The rank or the number of latent variables or true dimension of the data on the reduced space. This must be either 1, 2, 3 or 4. It is denoted by R.

`family`

What type of species data is to be returned. The first choice is the default. If binomial then a 0 means absence and 1 means presence. If ordinal then the `breaks` argument is passed into the `breaks` argument of `cut`. Note that either the Poisson or negative binomial distributions are used to generate binomial and ordinal data, and that an upper-case choice is used for the negative binomial distribution (this makes it easier for the user). If `"gamma2"` then this is the 2-parameter gamma distribution.

`eq.maximums` | Logical. Does each species have the same maximum? See arguments `lo.abundance` and `hi.abundance`.
`eq.tolerances` | Logical. Does each species have the same tolerance? If `TRUE` then the common value is 1 along every latent variable, i.e., all species' tolerance matrices are the order-R identity matrix.
`es.optimums` | Logical. Do the species have equally spaced optimums? If `TRUE` then the quantity S^(1/R) must be an integer with value 2 or more. That is, there has to be an appropriate number of species in total. This is so that a grid of optimum values is possible in R-dimensional latent variable space in order to place the species' optimums. Also see the argument `sd.tolerances`.

`lo.abundance, hi.abundance`

Numeric. These are recycled to a vector of length S. The species have a maximum between `lo.abundance` and `hi.abundance`. That is, at their optimal environment, the mean abundance of each species is between the two componentwise values. If `eq.maximums` is `TRUE` then `lo.abundance` and `hi.abundance` must have the same values. If `eq.maximums` is `FALSE` then the logarithm of the maximums are uniformly distributed between `log(lo.abundance)` and `log(hi.abundance)`.

`sd.latvar` | Numeric, of length R (recycled if necessary). Site scores along each latent variable have these standard deviation values. This must be a decreasing sequence of values because the first ordination axis contains the greatest spread of the species' site scores, followed by the second axis, followed by the third axis, etc.

`sd.optimums`

Numeric, of length R (recycled if necessary). If `es.optimums = FALSE` then, for the rth latent variable axis, the optimums of the species are generated from a normal distribution centered about 0. If `es.optimums = TRUE` then the S optimums are equally spaced about 0 along every latent variable axis. Regardless of the value of `es.optimums`, the optimums are then scaled to give standard deviation `sd.optimums[r]`.

`sd.tolerances`

Logical. If `eq.tolerances = FALSE` then, for the rth latent variable, the species' tolerances are chosen from a normal distribution with mean 1 and standard deviation `sd.tolerances[r]`. However, the first species `y1` has its tolerance matrix set equal to the order-R identity matrix. All tolerance matrices for all species are diagonal in this function. This argument is ignored if `eq.tolerances` is `TRUE`, otherwise it is recycled to length R if necessary.

`Kvector` | A vector of positive k values (recycled to length S if necessary) for the negative binomial distribution (see `negbinomial` for details). Note that a natural default value does not exist, however the default value here is probably a realistic one, and that for large values of μ one has Var(Y) = mu^2 / k approximately.
`Shape` | A vector of positive lambda values (recycled to length S if necessary) for the 2-parameter gamma distribution (see `gamma2` for details). Note that a natural default value does not exist, however the default value here is probably a realistic one, and that Var(Y) = mu^2 / lambda.
`sqrt.arg` | Logical. Take the square-root of the negative binomial counts? Assigning `sqrt.arg = TRUE` when `family="negbinomial"` means that the resulting species data can be considered very crudely to be approximately Poisson distributed. They will not integers in general but much easier (less numerical problems) to estimate using something like `cqo(..., family="poissonff")`.
`log.arg` | Logical. Take the logarithm of the gamma random variates? Assigning `log.arg = TRUE` when `family="gamma2"` means that the resulting species data can be considered very crudely to be approximately Gaussian distributed about its (quadratic) mean. The result is that it is much easier (less numerical problems) to estimate using something like `cqo(..., family="gaussianff")`.

`rhox`

Numeric, less than 1 in absolute value. The correlation between the environmental variables. The correlation matrix is a matrix of 1's along the diagonal and `rhox` in the off-diagonals. Note that each environmental variable is normally distributed with mean 0. The standard deviation of each environmental variable is chosen so that the site scores have the determined standard deviation, as given by argument `sd.latvar`.

`breaks` | If `family` is assigned an ordinal value then this argument is used to define the cutpoints. It is fed into the `breaks` argument of `cut`.
`seed` | If given, it is passed into `set.seed`. This argument can be used to obtain reproducible results. If set, the value is saved as the `"seed"` attribute of the returned value. The default will not change the random generator state, and return `.Random.seed` as `"seed"` attribute.
`optimums1.arg` | If assigned and `Rank = 1` then these are the explicity optimums. Recycled to length `S`.
`Crow1positive` | See `qrrvglm.control` for details.
`xmat` | The n by p-1 environmental matrix can be inputted.
`scale.latvar` | Logical. If `FALSE` the argument `sd.latvar` is ignored and no scaling of the latent variable values is performed.

### Details

This function generates data coming from a constrained quadratic ordination (CQO) model. In particular, data coming from a species packing model can be generated with this function. The species packing model states that species have equal tolerances, equal maximums, and optimums which are uniformly distributed over the latent variable space. This can be achieved by assigning the arguments `es.optimums = TRUE`, `eq.maximums = TRUE`, `eq.tolerances = TRUE`.

At present, the Poisson and negative binomial abundances are generated first using `lo.abundance` and `hi.abundance`, and if `family` is binomial or ordinal then it is converted into these forms.

In CQO theory the n by p matrix X is partitioned into two parts X_1 and X_2. The matrix X_2 contains the ‘real’ environmental variables whereas the variables in X_1 are just for adjustment purposes; they contain the intercept terms and other variables that one wants to adjust for when (primarily) looking at the variables in X_2. This function has X_1 only being a matrix of ones, i.e., containing an intercept only.

### Value

A n by p-1+S data frame with components and attributes. In the following the attributes are labelled with double quotes.

`x2, x3, x4, ..., xp` | The environmental variables. This makes up the n by p-1 X_2 matrix. Note that `x1` is not present; it is effectively a vector of ones since it corresponds to an intercept term when `cqo` is applied to the data.
`y1, y2, x3, ..., yS` | The species data. This makes up the n by S matrix Y. This will be of the form described by the argument `family`.
`"concoefficients"` | The p-1 by R matrix of constrained coefficients (or canonical coefficients). These are also known as weights or loadings.
`"formula"` | The formula involving the species and environmental variable names. This can be used directly in the `formula` argument of `cqo`.
`"log.maximums"` | The S-vector of species' maximums, on a log scale. These are uniformly distributed between `log(lo.abundance)` and `log(hi.abundance)`.
`"latvar"` | The n by R matrix of site scores. Each successive column (latent variable) has sample standard deviation equal to successive values of `sd.latvar`.
`"eta"` | The linear/additive predictor value.
`"optimums"` | The S by R matrix of species' optimums.
`"tolerances"` | The S by R matrix of species' tolerances. These are the square root of the diagonal elements of the tolerance matrices (recall that all tolerance matrices are restricted to being diagonal in this function).

Other attributes are `"break"`, `"family"`, `"Rank"`, `"lo.abundance"`, `"hi.abundance"`, `"eq.tolerances"`, `"eq.maximums"`, `"seed"` as used.

### Note

This function is under development and is not finished yet. There may be a few bugs.

Yet to do: add an argument that allows absences to be equal to the first level if ordinal data is requested.

### Author(s)

T. W. Yee

### References

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.

ter Braak, C. J. F. and Prentice, I. C. (1988) A theory of gradient analysis. Advances in Ecological Research, 18, 271–317.

### See Also

`cqo`, `qrrvglm.control`, `cut`, `binomialff`, `poissonff`, `negbinomial`, `gamma2`, `gaussianff`.

### Examples

    ## Not run:
    # Example 1: Species packing model:
    n <- 100; p <- 5; S <- 5
    mydata <- rcqo(n, p, S, es.opt = TRUE, eq.max = TRUE)
    names(mydata)
    (myform <- attr(mydata, "formula"))
    fit <- cqo(myform, poissonff, mydata, Bestof = 3)  # eq.tol = TRUE
    matplot(attr(mydata, "latvar"), mydata[,-(1:(p-1))], col = 1:S)
    persp(fit, col = 1:S, add = TRUE)
    lvplot(fit, lcol = 1:S, y = TRUE, pcol = 1:S)  # The same plot as above

    # Compare the fitted model with the 'truth'
    concoef(fit)  # The fitted model
    attr(mydata, "concoefficients")  # The 'truth'

    c(apply(attr(mydata, "latvar"), 2, sd),
      apply(latvar(fit), 2, sd))  # Both values should be approx equal

    # Example 2: negative binomial data fitted using a Poisson model:
    n <- 200; p <- 5; S <- 5
    mydata <- rcqo(n, p, S, fam = "negbin", sqrt = TRUE)
    myform <- attr(mydata, "formula")
    fit <- cqo(myform, fam = poissonff, dat = mydata)  # I.tol = TRUE,
    lvplot(fit, lcol = 1:S, y = TRUE, pcol = 1:S)
    # Compare the fitted model with the 'truth'
    concoef(fit)  # The fitted model
    attr(mydata, "concoefficients")  # The 'truth'

    # Example 3: gamma2 data fitted using a Gaussian model:
    n <- 200; p <- 5; S <- 3
    mydata <- rcqo(n, p, S, fam = "gamma2", log.arg = TRUE)
    fit <- cqo(attr(mydata, "formula"),
               fam = gaussianff, data = mydata)  # I.tol = TRUE,
    matplot(attr(mydata, "latvar"),
            exp(mydata[, -(1:(p-1))]), col = 1:S)  # 'raw' data
    # Fitted model to transformed data:
    lvplot(fit, lcol = 1:S, y = TRUE, pcol = 1:S)
    # Compare the fitted model with the 'truth'
    concoef(fit)  # The fitted model
    attr(mydata, "concoefficients")  # The 'truth'

    ## End(Not run)

[Package VGAM version 1.0-4 Index]