EvaluateModel <-
function(data, model, cex.axis=1, cex.names=1, cex.main=1){
.pardefault <- par(no.readonly = T)
result <- data[[1]][[model]]
pars <- c("Mean", strsplit(names(data[[1]])[model], split = ", ")[[1]])
par.est <- summary(result)$coefficients[, 1]
se.est <- summary(result)$coefficients[, 2]
max.val <- max(par.est + se.est)
min.val <- min(par.est - se.est)
if(max.val < 0){
max.val <- 0
}
if(min.val > 0){
min.val <- 0
}
mp <- barplot(par.est, names.arg = pars,
main = "Single Model Means and Cond. SE",
ylim = c(min.val - 0.2, max.val + 0.2), cex.axis=cex.axis,
cex.names=cex.names, cex.main=cex.main)
high.se <- par.est + se.est
low.se <- par.est - se.est
segments(mp, high.se, mp, low.se, lwd = 3)
segments(mp - 0.1, high.se, mp + 0.1, high.se, lwd = 3)
segments(mp - 0.1, low.se, mp + 0.1, low.se, lwd = 3)
results <- matrix(, 2, length(pars))
results[1, ] <- par.est
results[2, ] <- se.est
colnames(results) <- pars
row.names(results) <- c("Estimate", "Cond. SE")
model.p <- 1 - pchisq(result$deviance, result$df.residual, lower.tail = T)
mult.test <- 0.05 / length(data)
final.results <- list()
final.results[[1]] <- results
final.results[[2]] <- model.p
final.results[[3]] <- mult.test
names(final.results) <- c("Estiamtes", "P.val", "Bonf.Corr.Alpha")
return(final.results)
par(.pardefault)
}
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