Re: LinearModelFit regression estimated variance error
- To: mathgroup at smc.vnet.net
- Subject: [mg102348] Re: LinearModelFit regression estimated variance error
- From: Bill Rowe <readnews at sbcglobal.net>
- Date: Thu, 6 Aug 2009 06:31:42 -0400 (EDT)
On 8/5/09 at 5:43 AM, parita.patel at gmail.com (Parita) wrote: >I want to run linear regression in Mathematica and am using >LinearModelFit for the same. Following is the code that I am using. >data1 contains the 10 columns. The first 9 columns contains the data >through which I want to run regression and the last column contains >the response value. >model = LinearModelFit [data1, {a, b, c, d, e, f, g, h, i}, {a, b, >c, d, e, f, g, h, i}]; Print[model["BestFit"]]; <error messages snipped> >I am getting the coefficients for the regression model bu the >standard error, p-values and R-squared are indeterminate. Any ideas >what this error means and how can I go around it? You have not provided enough information for me to be certain what is causing the error messages. I would guess the source of the problem is collinearity between some subsets of the independent variables and/or a lack of variation in the dependent variables. I suggest looking for relationships between the independent variables. I find a useful way to quickly look for dependencies among what are supposed to be independent variables is PairwiseScatterPlot which is found in the package StatisticalPlots. Given a data matrix with n columns, this function produces a n x n array of plots showing each column plotted against every other column in the data matrix. Any of these plots showing something that doesn't look like a scatter diagram when the columns plotted are your independent variables is a potential issue when doing linear regression analysis. If one or more of this looks like a line, that is telling you those particular columns are essentially equal predictors for your dependent variable and one of them should be omitted before using LinearModelFit.