adding more stringent defenses to the plottinng fn

# MSC1090, Fall 2018 - UofT/SciNet %%% Marcelo Ponce
# this script is used to visually explore the results of your
# assignment #5

# this function receives as arguments two vectors
# containing the sample sizes and probabilities values

# function to generate a semi-log plot of two vectors with connecting lines
plotMC <- function(NS,probs, wHist=T) {
	# DEFENSE ... DEFENSE ...
	# this internal fn takes care of protecting the fn for receiving the proper arguments
	defense <- function(input1,input2) {
		# check that the arguments are vectors, ie. not a list!
		if ( (class(input1) == "list") | (class(input2) == "list") ) {
			stop("The fn requires two numeric vectors!")
		}

		# check that the arguments are numbers!
		if ( !is.numeric(input1) | !is.numeric(input2) ) {
			stop("The fn requires two numeric vectors!")
		}

		# check that the arguments have matching dimensions
		if (length(input1) != length(input2)) {
			stop("This function requires two vectors with the same number of elements!")
		}
	}

	# execute defensive programming first!
	defense(NS,probs)

	# obtain lenght of vectors
	lNS <- length(NS)
	lprobs <- length(probs)

	# set up figure canvas
	par(fig = c(0,1,0,1))

	# semi-log plot of probs vs NS with connecting lines
	plot(log(NS),probs,'b')

	# determine values to consider for computing convergent prob.
	i0 <- 2*lprobs/3
	i1 <- lprobs
	convP <- mean(probs[i0:i1])
	# add line to display convergent value of P
	abline(v=0,h=convP, col='red', lw=2, lt=2)

	# add inset figure with a histogram
	if (wHist) {
		# figure out where to place inset
		if ( (convP-mean(probs[1:2])) < 0 ) {
			# mean below 1st value, inset above
			par(fig = c(0.47,1, 0.5, 1), new = T)
		} else {
			# mean above 1st value, inset below
			par(fig = c(0.47,1, 0.07, 0.5), new = T)
			}
		hist(probs, col='grey')
	}

}