On 16/04/2013 1:19 PM, Noah Silverman wrote:
Hi,I have some data, that when plotted looks very close to a log-normal distribution. My goal is to build a regression model to test how this variable responds to several independent variables. To do this, I want to use the fitdistr tool from the MASS package to see how well my data fits the actual distribution, and also build a generalized linear model using the glm command. The summary of my data is: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.0000 0.0000 0.8617 0.8332 55.5600 So, no missing values, no negative values. When I try to use the fitdistr command, I get an error that I don't understand: m <- fitdistr(y, densfun="lognormal") Error in fitdistr(y, densfun = "lognormal") : need positive values to fit a log-Normal
You have zeros in your data. The lognormal distribution never takes on the value zero.
If they are zero because of rounding (e.g. 0.001 would be recorded as zero), and there aren't too many of them, then replacing the zeros with a small positive value (e.g. half the smallest non-zero value) might make sense. But your median is zero, so at least half of your observations are zero.
You need to come up with a better model than "lognormal". Duncan Murdoch
When I try to build a simple model, I also get an error: l <- glm(y~ x, family=gaussian(link="log")) Error in eval(expr, envir, enclos) : cannot find valid starting values: please specify some Can anyone offer some suggestions? Thanks! -- Noah Silverman, M.S. UCLA Department of Statistics 8117 Math Sciences Building Los Angeles, CA 90095 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
