Say I have collected data and used it to construct a linear model. I now have a new observation and want to use it to update my linear model.
Is there a more efficient way to update the model than recomputing the linear model from the complete data set? In other words, can I incrementally update a linear model as new observations come in without recomputing the linear model from the full data set? I've looked at the help for the lm package but can't see anything obvious that answers my question. I am trying to use a set of regressors to predict what the next observation will be. I would like my coeeficient and intercept estimates to improve as the data comes in - it will need to work well under limited information at first (or not at all if there is some lower bound of information quantity that is required). If the next data point comes in, I will have (binary or quantitative) information about whether the prediction was successful or not. Not sure if the option to use feedback would dictate a different approach than the one I am thinking of. Not sure if a backprop neural network can be used to estimate the coefficients in a dynamically evolving multiple linear regression equation. If anyone has any pointers to packages I might want to look at, suggestions, etc... I would appreciate it. Regards, Paul Meagher Datavore Productions 50 Wood Grove Drive Truro, Nova Scotia B2N-6Y4 1.902.895.9671 www.datavore.com ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
