R/deviance.vlm.q

In VGAM: Vector Generalized Linear and Additive Models

# These functions are
# Copyright (C) 1998-2024 T.W. Yee, University of Auckland.
# All rights reserved.






deviance.vlm <- function(object,
                         summation = TRUE,
                         ...) {
  if (summation) {
    object@criterion$deviance
  } else {


    Args <- formals(args(object@family@deviance))
    if (length(Args$summation) == 0)
      stop("there is no 'summation' argument for the function in ",
           "the 'deviance' slot of the object.")


    object@family@deviance(mu = fitted(object),
                           y = depvar(object),
                           w = weights(object, type = "prior"),
                           residuals = FALSE,
                           eta = predict(object),
                           extra = object@extra,
                           summation = summation)
  }
}



if (FALSE)
deviance.vglm <- function(object,
                          summation = TRUE,
                          ...)
  object@criterion$deviance











if (!isGeneric("deviance"))
  setGeneric("deviance", function(object, ...)
  standardGeneric("deviance"))


setMethod("deviance", "vlm", function(object, ...)
           deviance.vlm(object, ...))


if (FALSE)
setMethod("deviance", "vglm", function(object, ...)
           deviance.vglm(object, ...))










deviance.qrrvglm <- function(object,
                             summation = TRUE,
                             history = FALSE,
                             ...) {
  if (history) {
    if (summation) {
      return(object@misc$deviance.Bestof)
    } else {
      stop("cannot handle 'history = TRUE' when 'summation = FALSE'")
    }
  }

  deviance.vlm(object, summation = summation, ...)
}


setMethod("deviance", "qrrvglm", function(object, ...)
           deviance.qrrvglm(object, ...))

setMethod("deviance", "rrvgam",  function(object, ...)
           deviance.qrrvglm(object, ...))








df.residual_vlm <- function(object, type = c("vlm", "lm"), ...) {
  type <- type[1]


  switch(type,
         vlm = object@df.residual,
          lm = nobs(object, type = "lm") -
               nvar_vlm(object, type = "lm"),
         stop("argument 'type' unmatched"))
}



setMethod("df.residual", "vlm", function(object, ...)
           df.residual_vlm(object, ...))






df.residual_pvgam <-
  function(object,
           ...) {


  nobs(object, type = "lm") * npred(object) -
  sum(endf(object, diag.all = TRUE))
}



setMethod("df.residual", "pvgam", function(object, ...)
           df.residual_pvgam(object, ...))






nvar_vlm <- function(object, ...) {


  M <- npred(object)
  allH <- matrix(unlist(constraints(object, type = "lm")), nrow = M)
  checkNonZero <- function(m) sum(as.logical(m))
  numPars <- apply(allH, 1, checkNonZero)
  if (length(object@misc$predictors.names) == M)
    names(numPars) <- object@misc$predictors.names


  NumPars <- rep_len(0, M)
  for (jay in 1:M) {
    X.lm.jay <- model.matrix(object, type = "lm", linpred.index = jay)
    NumPars[jay] <- ncol(X.lm.jay)
  }
  if (length(object@misc$predictors.names) == M)
    names(NumPars) <- object@misc$predictors.names
  if (!all(NumPars == numPars)) {
    stop("something wrong in nvar_vlm(): ",
         paste(NumPars - numPars, collapse = ","),
         " should be all 0s")
  }

  numPars
}  # nvar_vlm












if (FALSE) {


set.seed(123)
zapdat <- data.frame(x2 = runif(nn <- 2000))
zapdat <-
  transform(zapdat, p0     = logitlink(-0.5 + 1*x2, inverse = TRUE),
                    lambda =  loglink( 0.5 + 2*x2, inverse = TRUE),
                    f1     =  gl(4, 50, labels = LETTERS[1:4]),
                    x3     =  runif(nn))
zapdat <- transform(zapdat, y = rzapois(nn, lambda, p0))
with(zapdat, table(y))


fit1 <- vglm(y ~ x2, zapoisson, zapdat, trace = TRUE)
fit1 <- vglm(y ~ bs(x2), zapoisson, zapdat, trace = TRUE)
coef(fit1, matrix = TRUE)  # These should agree with the above values


fit2 <- vglm(y ~ bs(x2) + x3, zapoisson(zero = 2),
             zapdat, trace = TRUE)
coef(fit2, matrix = TRUE)


clist <- list("(Intercept)" = diag(2), "x2" = rbind(0,1),
             "x3" = rbind(1,0))
fit3 <- vglm(y ~ x2 + x3, zapoisson(zero = NULL), zapdat,
            constraints = clist, trace = TRUE)
coef(fit3, matrix = TRUE)


constraints(fit2, type = "term")
constraints(fit2, type = "lm")
head(model.matrix(fit2, type = "term"))
head(model.matrix(fit2, type = "lm"))




allH <- matrix(unlist(constraints(fit1)), nrow = fit1@misc$M)
allH <- matrix(unlist(constraints(fit2)), nrow = fit2@misc$M)
allH <- matrix(unlist(constraints(fit3)), nrow = fit3@misc$M)


checkNonZero <- function(m) sum(as.logical(m))

(numPars <- apply(allH, 1, checkNonZero))


nvar_vlm(fit1)
nvar_vlm(fit2)
nvar_vlm(fit3)


}