Given your comments I believe it might be better to treat your dependent variable as ordinal rather than nominal. Consider Maybe even recode it to 0, 1, 2 and make it distance from the left. Now it's easy to see that it's ordinal. There's a good package for this in R called, appropriately if not redundantly, 'ordinal'. If you have multiple judgments / subject then you want multi-level ordinal regression, which this package can do as well.
Why ordinal? It's much easier to discuss (and the regression is much nicer). You can talk about the probability of a response shifting more rightward and think of response distributions moving in directions, which don't exist in nominal space.
To analyze it graphically you want to look at distributions of the scores separated by sex, and age. Line plots are best for this with the ordinal variable on the x-axis and counts on the y. The ordinal regression can treat age continuous but for convenience of visualization you need to bin it into 2 or 3 groups separately for your two sexes... which would each be in separate plots. You can also plot the predicted counts from the regression on the graph as lines and leave the empirical as points.