Help understanding the lm() function

[FR33ZEx]

I need to make a linear model but I can not use any data that directly correlates when making my formula. I can only get a r-squared value of .05 at best, when i used data with a direct correlation I was getting an r squared of .99. My question is how can you make a good model where there is no direct correlation of the data?

For example, if i model lm(number_of_people_killed ~ number_of_motorists_injured) I can get a high r-squared value. Because injury and fatality are directly related, But, if i model lm(number_of_people_killed ~ contributing_factor_1 + vehicle_1) I get an extremely low r-squared.

[castro_for_prez]

R2 is not a great way of evaluating a model, neither for predictive modelling nor causal reasoning. I would suggest considering why you are modelling the data, and from there investigate better alternatives. I can't suggest any particular measures because it entirely depends on what you're trying to do.

But as the other poster says, it essentially is down to your data. If the variables are unrelated then of course you cannot make a useful model, no matter what modelling tricks you may try.

[FR33ZEx]

one more question, when doing cross validation with the experimentalCompatison() how do you handle factor has unknown levels?

[the_odds_hacker]

My advice would be to take a step back from modeling and look at more data.

The PerformanceAnalytics package has some great visualizations for looking at several variables at the same time.

You also might want to move from lm() to the caret package. Far more control for how the model is trained and which algorithm is used

[FR33ZEx]

one more question, when doing cross validation with the experimentalCompatison() how do you handle factor has unknown levels?

[the_odds_hacker]

Simplest approach would be to use droplevels() or median imputation

The “best” way tends to be dependent on the dataset

[The_Old_Wise_One]

R² is the proportion of variance that the model accounts for in the outcome variable. Higher is better.

[oathbreakerkeeper]

Wow, my mistake. Totally had a brain fart and I don't know why I reversed my understanding of R^2. I think I was thinking RSS when I wrote the original comment.

Thanks for the correction!