# [R] comparing glm models - lower AIC but insignificant coefficients

From: Constantinos Antoniou <antoniou_at_central.ntua.gr>
Date: Tue 24 May 2005 - 04:59:11 EST

Hello,

I am a new R user and I am trying to estimate some generalized linear models (glm). I am trying to compare a model with a gaussian distribution and an identity link function, and a poisson model with a log link function. My problem is that while the gaussian model has significantly lower (i.e. "better") AIC (Akaike Information Criterion) most of the coefficients are not significant. On the other hand, the poisson model has a higher (i.e. "worse") AIC, but almost all the coefficients are extremely significant (expect for one that still has p=0.07).

Summary output of the two models follows... [sorry for the large number of independent variables, but the issue is less pronounced with fewer covariates].

My question is two-fold:
- AIC supposedly can be used to compare non-nested models (although
there are concerns and I have also seen a couple in this list's archives). Is this a case where AIC is not a good measure to compare the two models? If so, is there another measure (besides choosing the model with the significant coefficients)? [These are time-series data, so I am also looking at acf/pacf of the residuals].
- Could the very high significance of the coefficients in the poisson
model hint at some issue?

Costas

```+++++++++++++++++++++++
```

```+++++++++++++++++++++++

```

Call:
glm(formula = TotalDeadInjured[3:48] ~ -1 + Month[3:48] + sin(pi *

Month[3:48]/6) + cos(pi * Month[3:48]/6) + sin(pi * Month[3:48]/ 12) +

```     cos(pi * Month[3:48]/12) + ThousandCars[3:48] + monthcycle[3:48] +

Deviance Residuals:
Min       1Q   Median       3Q      Max
```

-3.6900 -1.1901 -0.1847 0.9477 4.3967

Coefficients:

```                                 Estimate Std. Error z value Pr(>|z|)
Month[3:48]                   -7.712e-02  5.530e-03 -13.947  < 2e-16 ***
sin(pi * Month[3:48]/6)       -1.419e-01  2.759e-02  -5.144 2.68e-07 ***
cos(pi * Month[3:48]/6)       -8.407e-02  1.799e-02  -4.672 2.99e-06 ***
sin(pi * Month[3:48]/12)      -2.776e-02  1.558e-02  -1.782 0.074702 .
cos(pi * Month[3:48]/12)       5.195e-02  1.608e-02   3.232 0.001231 **
ThousandCars[3:48]             2.733e-02  2.255e-03  12.118  < 2e-16 ***
monthcycle[3:48]               6.307e-02  6.546e-03   9.635  < 2e-16 ***
TotalDeadInjured[1:46]        -2.925e-02  8.460e-03  -3.457 0.000546 ***
```
I((TotalDeadInjured[1:46])^2) 1.218e-04 3.613e-05 3.370 0.000750 *** I((TotalDeadInjured[1:46])^3) -1.640e-07 4.961e-08 -3.306 0.000946 ***
---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 78694.70 on 46 degrees of freedom Residual deviance: 130.03 on 36 degrees of freedom AIC: 476.08 Number of Fisher Scoring iterations: 4

```+++++++++++++++++++++++++
```

GAUSSIAN
```++++++++++++++++++++++++++

```

Call:
glm(formula = TotalDeadInjured[3:48] ~ -1 + Month[3:48] + sin(pi *

Month[3:48]/6) + cos(pi * Month[3:48]/6) + sin(pi * Month[3:48]/ 12) +

```     cos(pi * Month[3:48]/12) + ThousandCars[3:48] + monthcycle[3:48] +

Deviance Residuals:
Min       1Q   Median       3Q      Max
```

-61.326 -12.012 -1.756 14.204 78.991

Coefficients:

```                                 Estimate Std. Error t value Pr(>|t|)
Month[3:48]                   -8.111e+00  2.115e+00  -3.835 0.000487 ***
sin(pi * Month[3:48]/6)       -2.639e+01  1.095e+01  -2.409 0.021246 *
cos(pi * Month[3:48]/6)       -1.700e+01  7.138e+00  -2.382 0.022629 *
sin(pi * Month[3:48]/12)       2.392e-01  6.524e+00   0.037 0.970956
cos(pi * Month[3:48]/12)       8.785e+00  6.317e+00   1.391 0.172835
ThousandCars[3:48]             2.219e+00  8.604e-01   2.579 0.014146 *
monthcycle[3:48]               5.364e+00  2.494e+00   2.151 0.038301 *
```