simulate.vlm {VGAM} | R Documentation

## Simulate Responses for VGLMs and VGAMs

### Description

Simulate one or more responses from the distribution corresponding to a fitted model object.

### Usage

    ## S3 method for class 'vlm'
    simulate(object, nsim = 1, seed = NULL, ...)

### Arguments

`object` | an object representing a fitted model. Usually an object of class `vglm-class` or `vgam-class`.
`nsim, seed` | Same as `simulate`.

`...` | additional optional arguments.

### Details

This is a methods function for `simulate` and hopefully should behave in a very similar manner. Only VGAM family functions with a `simslot` slot have been implemented for `simulate`.

### Value

Similar to `simulate`. Note that many VGAM family functions can handle multiple responses. This can result in a longer data frame with more rows (`nsim` multiplied by `n` rather than the ordinary `n`). In the future an argument may be available so that there is always `n` rows no matter how many responses were inputted.

### Warning

With multiple response and/or multivariate responses, the order of the elements may differ. For some VGAM families, the order is n x N x F, where n is the sample size, N is `nsim` and F is `ncol(fitted(vglmObject))`. For other VGAM families, the order is n x F x N. An example of each is given below.

### See Also

Currently the VGAM family functions with a `simslot` slot are: `alaplace1`, `alaplace2`, `betabinomial`, `betabinomialff`, `betaR`, `betaff`, `biamhcop`, `bifrankcop`, `bilogistic`, `binomialff`, `binormal`, `binormalcop`, `biclaytoncop`, `cauchy`, `cauchy1`, `chisq`, `dirichlet`, `dagum`, `erlang`, `exponential`, `bifgmcop`, `fisk`, `gamma1`, `gamma2`, `gammaR`, `gengamma.stacy`, `geometric`, `gompertz`, `gumbelII`, `hzeta`, `inv.lomax`, `inv.paralogistic`, `kumar`, `lgamma1`, `lgamma3`, `lindley`, `lino`, `logff`, `logistic1`, `logistic`, `lognormal`, `lomax`, `makeham`, `negbinomial`, `negbinomial.size`, `paralogistic`, `perks`, `poissonff`, `posnegbinomial`, `posnormal`, `pospoisson`, `polya`, `polyaR`, `posbinomial`, `rayleigh`, `riceff`, `simplex`, `sinmad`, `slash`, `studentt`, `studentt2`, `studentt3`, `triangle`, `uninormal`, `yulesimon`, `zageometric`, `zageometricff`, `zanegbinomial`, `zanegbinomialff`, `zapoisson`, `zapoissonff`, `zigeometric`, `zigeometricff`, `zinegbinomial`, `zipf`, `zipoisson`, `zipoissonff`.

See also `RNG` about random number generation in R, `vglm`, `vgam` for model fitting.

### Examples

    nn <- 10; mysize <- 20; set.seed(123)
    bdata <- data.frame(x2 = rnorm(nn))
    bdata <- transform(bdata,
      y1   = rbinom(nn, size = mysize, p = logit(1+x2, inverse = TRUE)),
      y2   = rbinom(nn, size = mysize, p = logit(1+x2, inverse = TRUE)),
      f1   = factor(as.numeric(rbinom(nn, size = 1,
                                      p = logit(1+x2, inverse = TRUE)))))
    (fit1 <- vglm(cbind(y1, aaa = mysize - y1) ~ x2,  # Matrix response (2-colns)
                  binomialff, data = bdata))
    (fit2 <- vglm(f1 ~ x2, binomialff, model = TRUE, data = bdata)) # Factor response

    set.seed(123); simulate(fit1, nsim = 8)
    set.seed(123); c(simulate(fit2, nsim = 3))  # Use c() when model = TRUE

    # An n x N x F example
    set.seed(123); n <- 100
    bdata <- data.frame(x2 = runif(n), x3 = runif(n))
    bdata <- transform(bdata, y1 = rnorm(n, 1 + 2 * x2),
                              y2 = rnorm(n, 3 + 4 * x2))
    fit1 <- vglm(cbind(y1, y2) ~ x2, binormal(eq.sd = TRUE), data = bdata)
    nsim <- 1000  # Number of simulations for each observation
    my.sims <- simulate(fit1, nsim = nsim)
    dim(my.sims)  # A data frame
    aaa <- array(unlist(my.sims), c(n, nsim, ncol(fitted(fit1))))  # n by N by F
    summary(rowMeans(aaa[, , 1]) - fitted(fit1)[, 1])  # Should be all 0s
    summary(rowMeans(aaa[, , 2]) - fitted(fit1)[, 2])  # Should be all 0s

    # An n x F x N example
    n <- 100; set.seed(111); nsim <- 1000
    zdata <- data.frame(x2 = runif(n))
    zdata <- transform(zdata, lambda1 =  loge(-0.5 + 2 * x2, inverse = TRUE),
                              lambda2 =  loge( 0.5 + 2 * x2, inverse = TRUE),
                              pstr01  = logit( 0,            inverse = TRUE),
                              pstr02  = logit(-1.0,          inverse = TRUE))
    zdata <- transform(zdata, y1 = rzipois(n, lambda = lambda1, pstr0 = pstr01),
                              y2 = rzipois(n, lambda = lambda2, pstr0 = pstr02))
    zip.fit  <- vglm(cbind(y1, y2) ~ x2, zipoissonff, data = zdata, crit = "coef")
    my.sims <- simulate(zip.fit, nsim = nsim)
    dim(my.sims)  # A data frame
    aaa <- array(unlist(my.sims), c(n, ncol(fitted(zip.fit)), nsim))  # n by F by N
    summary(rowMeans(aaa[, 1, ]) - fitted(zip.fit)[, 1])  # Should be all 0s
    summary(rowMeans(aaa[, 2, ]) - fitted(zip.fit)[, 2])  # Should be all 0s

[Package VGAM version 1.0-3 Index]