R/makejagsboralnullmodel.R

In emitanaka/boral: Bayesian Ordination and Regression AnaLysis

make.jagsboralnullmodel <- function(family, num.X = 0, X.ind = NULL, num.traits = 0, which.traits = NULL,
                                    row.eff = "none", row.ids = NULL, offset = NULL, trial.size = 1, n, p, model.name = NULL,
                                    prior.control = list(type = c("normal", "normal", "normal", "uniform"), hypparams = c(10, 10, 10, 30), ssvs.index = -1, ssvs.g = 1e-6, ssvs.traitsindex = -1)) {
  check_which_traits(num.traits = num.traits, which.traits = which.traits, num.X = num.X, makejagsboralfile_messages = TRUE)
  if (is.null(which.traits)) {
    which.traits <- vector("list", num.X + 1)
    for (k in 1:(num.X + 1)) {
      which.traits[[k]] <- 0
    }
  }


  complete_trial_size <- check_trial_size(family = family, trial.size = trial.size, create.complete.trial.size = TRUE, y = matrix(NA, nrow = 1, ncol = p))

  complete_family <- check_family(family = family, y = matrix(NA, nrow = 1, ncol = p), traits = NULL)

  if (row.eff != "none" && is.null(row.ids)) {
    row.ids <- matrix(1:n, ncol = 1)
    message("row.ids assumed to be matrix with one column and elements 1,2,...n i.e., a row-specific intercept")
  }
  if (!is.null(row.ids)) {
    row.ids <- as.matrix(row.ids)
    if (nrow(row.ids) != n) {
      stop("Number of rows in the matrix row.ids should be equal to n")
    }
    if (is.null(colnames(row.ids))) {
      colnames(row.ids) <- paste0("ID", 1:ncol(row.ids))
    }
  }


  if (!is.null(offset)) {
    if (!is.matrix(offset)) {
      stop("offset could be a matrix with the same dimensions as y")
    }
  }


  prior.control <- fillin_prior_control(x = prior.control)
  check_prior_control(prior.control = prior.control)

  if (length(prior.control$ssvs.index) == 1 & num.X > 0) prior.control$ssvs.index <- rep(prior.control$ssvs.index, num.X)
  if (num.traits > 0) {
    if (!is.list(prior.control$ssvs.traitsindex)) {
      prior.control$ssvs.traitsindex <- vector("list", num.X + 1)
      for (k in 1:(num.X + 1)) {
        prior.control$ssvs.traitsindex[[k]] <- rep(-1, length(which.traits[[k]]))
      }
    }
    if (is.list(prior.control$ssvs.traitsindex)) {
      check_ssvstraits(prior.control$ssvs.traitsindex, which.traits)
    }
  }

  X.ind <- check_X_ind(X.ind = X.ind, p = p, num.X = num.X, prior.control = prior.control)

  index.ord.cols <- which(complete_family == "ordinal")
  index.tweed.cols <- which(complete_family == "tweedie")


  ## Checks done; starting writing JAGS script!


  model_script <- paste0("## JAGS model written for boral version ", packageDescription("boral")$Version, " on ", as.character(Sys.time()), " ##\n\n model {")
  model_script <- c(model_script, "\t ## Data Level ## \n\t for(i in 1:n) {")

  write.resp.script <- setup.resp.families.lv(p = p, complete.family = complete_family, num.lv = 0, row.eff = row.eff, row.ids = row.ids, offset = offset, num.X = num.X, complete.trial.size = complete_trial_size, index.tweed.cols = index.tweed.cols, index.ord.cols = index.ord.cols)
  model_script <- c(model_script, write.resp.script)
  model_script <- c(model_script, paste0("\t\t } \n\n\t ## Process level and priors ##"))


  ## Build prior strings for all priors distributions
  prior.strings <- construct.prior.strings(x = prior.control)


  ## Code for column-specific intercept. Note this is set up different to how X variables are set up to save some coding space!
  ## No traits or traits included but not regressed against intercept
  if (num.traits == 0 || (num.traits > 0 & which.traits[[1]][1] == 0)) {
    ## Not ordinal columns, then as per usual
    if (length(index.ord.cols) == 0) {
      model_script <- c(model_script, paste0("\t for(j in 1:p) { lv.coefs[j,1] ~ ", prior.strings$p1, " } ## Separate species intercepts"))
    }
    ## If 1 ordinal column, then intercept for this column equal 0
    if (length(index.ord.cols) == 1) {
      model_script <- c(model_script, paste0("\t lv.coefs[", index.ord.cols, ",1] <- 0 ## Single ordinal species intercept"))
      for (j in (1:p)[-index.ord.cols]) {
        model_script <- c(model_script, paste0("\t lv.coefs[", j, ",1] ~ ", prior.strings$p1, "All other species intercepts"))
      }
    }
    ## More than 1 ordinal column, then set up random intercept for this species
    if (length(index.ord.cols) > 1) {
      if (length(index.ord.cols) == p) {
        model_script <- c(model_script, paste0("\t for(j in 1:p) { lv.coefs[j,1] ~ dnorm(0,pow(ordinal.sigma,-2)) } ## Random intercept for all ordinal species"))
      } else {
        for (j in index.ord.cols) {
          model_script <- c(model_script, paste0("\t lv.coefs[", j, ",1] ~ dnorm(0,pow(ordinal.sigma,-2)) ## Random intercept for all ordinal species"))
        }
        for (j in (1:p)[-index.ord.cols]) {
          model_script <- c(model_script, paste0("\t lv.coefs[", j, ",1] ~ ", prior.strings$p1, "All other species intercepts"))
        }
      }
      model_script <- c(model_script, paste0("\t ordinal.sigma ~ ", prior.strings$p4))
    }
    if ((num.traits > 0 & which.traits[[1]][1] == 0)) {
      model_script <- c(model_script, paste0("\t traits.int[1] <- 0; for(l in 1:num.traits) { traits.coefs[1,l] <- 0 } \n\t trait.sigma[1] <- 0 ## Traits not used for intercept"))
    }
  }
  ## Traits included and intercept regressed against them
  if (num.traits > 0 & all(which.traits[[1]] > 0)) {
    ## If there are 0 or > 1 ordinal columns, then regress all intercepts against traits
    if (length(index.ord.cols) != 1) {
      model_script <- c(model_script, paste0("\t for(j in 1:p) { lv.coefs[j,1] ~ dnorm(traits.int[1] + inprod(traits[j,],traits.coefs[1,1:num.traits]),pow(trait.sigma[1],-2)) } ## Species intercepts regressed against traits"))
    }
    ## If there is 1 ordinal column, do not regress this intercept against trait
    if (length(index.ord.cols) == 1) {
      model_script <- c(model_script, paste0("\t lv.coefs[", index.ord.cols, ",1] <- 0 ## Ordinal species intercept"))
      for (j in (1:p)[-index.ord.cols]) {
        model_script <- c(model_script, paste0("\t lv.coefs[", j, ",1] ~ dnorm(traits.int[1] + inprod(traits[", j, ",],traits.coefs[1,1:num.traits]),pow(trait.sigma[1],-2)) ## All other intercepts"))
      }
    }
    for (l in which.traits[[1]]) {
      if (prior.control$ssvs.traitsindex[[1]][which.traits[[1]] == l] == -1) {
        model_script <- c(model_script, paste0("\t traits.coefs[", 1, ",", l, "] ~ ", prior.strings$p1, " ## Traits used for intercept"))
      }
      if (prior.control$ssvs.traitsindex[[1]][which.traits[[1]] == l] == 0) {
        ssvs.prior.string <- paste0("dnorm(0,pow(", prior.control$hypparams[1], "*((1-ssvs.traitscoefs1", l, ")*", prior.control$ssvs.g, " + ssvs.traitscoefs1", l, "),-1)); ssvs.traitscoefs1", l, " ~ dbern(0.5)")
        model_script <- c(model_script, paste0("\t traits.coefs[", 1, ",", l, "] ~ ", ssvs.prior.string, " ## Traits used for intercept"))
      }
    }
    if (length((1:num.traits)[-which.traits[[1]]]) > 0) {
      for (l in (1:num.traits)[-which.traits[[1]]]) {
        model_script <- c(model_script, paste0("\t traits.coefs[", 1, ",", l, "] <- 0 ## Traits not used for intercept"))
      }
    }
    model_script <- c(model_script, paste0("\t traits.int[1] ~ ", prior.strings$p1))
    model_script <- c(model_script, paste0("\t trait.sigma[1] ~ ", prior.strings$p4))
  }

  if (any(complete_family == "tweedie")) {
    model_script <- c(model_script, paste0("\t powerparam ~ dunif(1,2) ## Tweedie power parameter"))
  }
  if (any(complete_family == "ordinal")) {
    model_script <- c(model_script, paste0("\t for(k in 1:(num.ord.levels-1)) { cutoffs0[k] ~ ", prior.strings$p1, " }"))
    model_script <- c(model_script, paste0("\t cutoffs[1:(num.ord.levels-1)] <- sort(cutoffs0) ## Ordinal cutoffs"))
  }


  ## Priors on row effects
  if (row.eff == "fixed") {
    for (k in 1:ncol(row.ids))
    {
      model_script <- c(model_script, paste0("\n\t row.coefs.ID", k, "[1] <- 0"))
      model_script <- c(model_script, paste0("\n\t for(i in 2:n.ID[", k, "]) { row.coefs.ID", k, "[i] ~ ", prior.strings$p1, " } "))
    }
  }
  if (row.eff == "random") {
    for (k in 1:ncol(row.ids)) {
      model_script <- c(model_script, paste0("\n\t for(i in 1:n.ID[", k, "]) { row.coefs.ID", k, "[i] ~ dnorm(0, pow(row.sigma.ID", k, ",-2)) } "))
      model_script <- c(model_script, paste0("\t row.sigma.ID", k, " ~ ", prior.strings$p4))
    }
    # model_script <- c(model_script, paste0("\t row.ranef.mean ~ ", prior.strings$p1))
  }


  ## Prior for X-coefficients, controlled by prior.control$hypparams[3]
  if (num.X > 0) {
    model_script <- c(model_script, paste0("\n"))

    ## Traits not included in model
    if (num.traits == 0) {
      for (i in 1:length(prior.control$ssvs.index)) {
        if (prior.control$ssvs.index[i] == -1) {
          if (!is.null(X.ind)) {
            model_script <- c(model_script, paste0("\t for(j in 1:p) { X.coefs[j,", i, "] ~ ", prior.strings$p3, "I(-X.ind[j,", i, "],X.ind[j,", i, "]) } "))
          }
          if (is.null(X.ind)) {
            model_script <- c(model_script, paste0("\t for(j in 1:p) { X.coefs[j,", i, "] ~ ", prior.strings$p3, " } "))
          }
        }
        if (prior.control$ssvs.index[i] == 0) {
          ssvs.prior.string <- paste0("dnorm(0,pow(", prior.control$hypparams[3], "*((1-ssvs.indX", i, "[j])*", prior.control$ssvs.g, " + ssvs.indX", i, "[j]),-1)); ssvs.indX", i, "[j] ~ dbern(0.5)")
          model_script <- c(model_script, paste0("\t for(j in 1:p) { X.coefs[j,", i, "] ~ ", ssvs.prior.string, " }"))
        }
        if (prior.control$ssvs.index[i] > 0) {
          ssvs.prior.string <- paste0("dnorm(0,pow(", prior.control$hypparams[3], "*((1-ssvs.gp", prior.control$ssvs.index[i], ")*", prior.control$ssvs.g, " + ssvs.gp", prior.control$ssvs.index[i], "),-1))")
          model_script <- c(model_script, paste0("\t for(j in 1:p) { X.coefs[j,", i, "] ~ ", ssvs.prior.string, " } "))
        }
      }
    }


    if (num.traits > 0) {
      for (i in 1:num.X) {
        ## Traits included but X coefs not regressed against them
        if (all(which.traits[[i + 1]] == 0)) {
          model_script <- c(model_script, paste0("\t for(j in 1:p) { X.coefs[j,", i, "] ~ ", prior.strings$p3, " } ## Coefficient not regressed against any traits"))
          model_script <- c(model_script, paste0("\t traits.int[", i + 1, "] <- 0; trait.sigma[", i + 1, "] <- 0; for(l in 1:num.traits) { traits.coefs[", i + 1, ",l] <- 0 } \n"))
        }
        ## Traits included and X coefs regressed against some of them
        if (all(which.traits[[i + 1]] > 0)) {
          model_script <- c(model_script, paste0("\t for(j in 1:p) { X.coefs[j,", i, "] ~ dnorm(traits.int[", i + 1, "] + inprod(traits[j,],traits.coefs[", i + 1, ",1:num.traits]),pow(trait.sigma[", i + 1, "],-2)) } "))
          for (l in which.traits[[i + 1]]) {
            if (prior.control$ssvs.traitsindex[[i + 1]][which.traits[[i + 1]] == l] == -1) {
              model_script <- c(model_script, paste0("\t traits.coefs[", i + 1, ",", l, "] ~ ", prior.strings$p3, " ## Traits used for this X.coefs"))
            }
            if (prior.control$ssvs.traitsindex[[i + 1]][which.traits[[i + 1]] == l] == 0) {
              ssvs.prior.string <- paste0("dnorm(0,pow(", prior.control$hypparams[3], "*((1-ssvs.traitscoefs", i + 1, l, ")*", prior.control$ssvs.g, " + ssvs.traitscoefs", i + 1, l, "),-1)); ssvs.traitscoefs", i + 1, l, " ~ dbern(0.5)")
              model_script <- c(model_script, paste0("\t traits.coefs[", i + 1, ",", l, "] ~ ", ssvs.prior.string, " ## Traits used for this X.coefs"))
            }
          }
          if (length((1:num.traits)[-which.traits[[i + 1]]]) > 0) {
            for (l in (1:num.traits)[-which.traits[[i + 1]]]) {
              model_script <- c(model_script, paste0("\t traits.coefs[", i + 1, ",", l, "] <- 0 ## traits not used for this X.coefs"))
            }
          }
          model_script <- c(model_script, paste0("\t traits.int[", i + 1, "] ~ ", prior.strings$p3))
          model_script <- c(model_script, paste0("\t trait.sigma[", i + 1, "] ~ ", prior.strings$p4, "\n"))
        }
      }
    }

    model_script <- c(model_script, paste0(""))
    if (any(prior.control$ssvs.index > 0)) {
      for (i in unique(prior.control$ssvs.index[prior.control$ssvs.index > 0])) {
        model_script <- c(model_script, paste0("\t ssvs.gp", i, " ~ dbern(0.5)"))
      }
    }
  }
  # 	if(num.X > 0 & any(family == "multinom")) {
  # 		model_script <- c(model_script, paste0("\t for(j in 1:",length(index_multinom_cols),") { for(i in 1:num.X) { X.multinom.params[j,i,1] <- 0 } } "))
  # 		model_script <- c(model_script, paste0("\t for(j in 1:",length(index_multinom_cols),") { for(i in 1:num.X) { for(k in 2:num.multinom.levels) { X.multinom.params[j,i,k] ~ dnorm(0,",1/prior.control$hypparams[1],") } } } "))
  # 		}


  ## Prior on dispersion parameters, controlled by prior.control$hypparams[4]
  if (!all(complete_family %in% c("poisson", "binomial", "ordinal", "multinom", "exponential"))) { # "bernoulli"
    model_script <- c(model_script, paste0("\t for(j in 1:p) { lv.coefs[j,num.lv+2] ~ ", prior.strings$p4, " } ## Dispersion parameters"))
  }

  model_script <- c(model_script, "\n\t }")

  if (!is.null(model.name)) {
    write(model_script, file = model.name)
  }
  if (is.null(model.name)) {
    write(model_script, file = "jagsboralmodel.txt")
  }
}