## Print JAGS model
print.JAGS_model <-
function(x, ...) {
l <- length(x)
spacer <- 0
for (i in seq(l)){
if (substr(x[i], nchar(x[i]), nchar(x[i])) == "}")
spacer <- spacer - 1
cat(rep(" ", 2 * spacer), x[i], "\n", sep = "")
if (substr(x[i], nchar(x[i]), nchar(x[i])) == "{")
spacer <- spacer + 1
}
}
## Print model average
print.avg <-
function(x, ...) {
## data type
from <-
c("count", "concentration", "presence/absence")[
which(x$fun == c("ea_count", "ea_conc", "ea_presence"))]
if (length(from) != 0) {
cat("Exposure assessment from", from, "data\n\n")
} else {
cat("Dose-response assessment\n\n")
}
## model estimates
cat("Individual model estimates:\n")
print(x$mod)
## model estimates
cat("\nModel average:\n")
print(x$avg)
}
## Print Goodness of Fit
print.gof <-
function(x, ...) {
if (is.na(x)) {
cat("Goodness of fit statistics not yet implemented for this function.\n")
} else {
cat("Chi-square goodness of fit test\n\n")
cat("Saturated likelihood:",
x$L0[1], "on", x$L0[2], "degree(s) of freedom\n")
cat(" Model likelihood:",
x$L1[1], "on", x$L1[2], "degree(s) of freedom\n\n")
df <- x$L0[2] - x$L1[2]
chisq <- -2 * log(x$L1[1] / x$L0[1])
p <- pchisq(chisq, df, lower.tail = FALSE)
out <- data.frame(chisq, df, p)
rownames(out) <- ""
colnames(out) <- c("ChiSq", "df", "Pr>ChiSq")
print(out)
}
}
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