Dear all, I'm trying to fit a statistical model to series of measurements. Unfortunately, my knowledge of statistics is rather limited, so I'm a bit at loss of what is going on with the model.
First of all, I've prepared a histogram. Then, I've tried to fit a Poisson model to express the relation between the middle points of classes (mids) and the corresponding frequencies (density). I've got the following Poisson models using R: > summary(fmDP) Deviance Residuals: Min 1Q Median 3Q Max -1.20831 -0.56363 -0.28010 0.08324 3.19099 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.2495 0.1528 8.18 2.84e-16 *** hD$mids -3.3683 0.4420 -7.62 2.53e-14 *** --- Signif. codes: 0 ~Q***~R 0.001 ~Q**~R 0.01 ~Q*~R 0.05 ~Q.~R 0.1 ~Q ~R 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 129.59 on 99 degrees of freedom Residual deviance: 55.67 on 98 degrees of freedom AIC: Inf Number of Fisher Scoring iterations: 5 > summary(fmDPAll) Deviance Residuals: Min 1Q Median 3Q Max -1.3687 -0.5672 -0.3386 0.0513 4.9680 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.478 0.148 9.988 <2e-16 *** hDAll$mids -4.327 0.501 -8.636 <2e-16 *** --- Signif. codes: 0 ~Q***~R 0.001 ~Q**~R 0.01 ~Q*~R 0.05 ~Q.~R 0.1 ~Q ~R 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 181.646 on 99 degrees of freedom Residual deviance: 74.457 on 98 degrees of freedom AIC: Inf Number of Fisher Scoring iterations: 5 As 55.67 < 74.457 the first model seems to fit better than the second one, but how good is it? Should I compare these residual deviances with chi-square? Should I look for some other model with smaller residual deviance? Best regards, Alexander ______________________________________________ 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.