Consider the following data, where x is an explanatory variable and y is the response variable. It appears that y varies nonlinearly with x and that the variance is approximately constant. A normal distribution with a log link function is chosen to model these data; that is, so that .
data nor; input x y; datalines; 0 5 0 7 0 9 1 7 1 10 1 8 2 11 2 9 3 16 3 13 3 14 4 25 4 24 5 34 5 32 5 30 ;
The following SAS statements produce the analysis with the normal distribution and log link:
proc genmod data=nor; model y = x / dist = normal link = log; output out = Residuals pred = Pred resraw = Resraw reschi = Reschi resdev = Resdev stdreschi = Stdreschi stdresdev = Stdresdev reslik = Reslik; run;
The OUTPUT statement is specified to produce a data set that contains predicted values and residuals for each observation. This data set can be useful for further analysis, such as residual plotting.
The results from these statements are displayed in Output 39.2.1.
Model Information | |
---|---|
Data Set | WORK.NOR |
Distribution | Normal |
Link Function | Log |
Dependent Variable | y |
Criteria For Assessing Goodness Of Fit | |||
---|---|---|---|
Criterion | DF | Value | Value/DF |
Deviance | 14 | 52.3000 | 3.7357 |
Scaled Deviance | 14 | 16.0000 | 1.1429 |
Pearson Chi-Square | 14 | 52.3000 | 3.7357 |
Scaled Pearson X2 | 14 | 16.0000 | 1.1429 |
Log Likelihood | -32.1783 | ||
Full Log Likelihood | -32.1783 | ||
AIC (smaller is better) | 70.3566 | ||
AICC (smaller is better) | 72.3566 | ||
BIC (smaller is better) | 72.6743 |
Analysis Of Maximum Likelihood Parameter Estimates | |||||||
---|---|---|---|---|---|---|---|
Parameter | DF | Estimate | Standard Error | Wald 95% Confidence Limits | Wald Chi-Square | Pr > ChiSq | |
Intercept | 1 | 1.7214 | 0.0894 | 1.5461 | 1.8966 | 370.76 | <.0001 |
x | 1 | 0.3496 | 0.0206 | 0.3091 | 0.3901 | 286.64 | <.0001 |
Scale | 1 | 1.8080 | 0.3196 | 1.2786 | 2.5566 |
Note: | The scale parameter was estimated by maximum likelihood. |
The PROC GENMOD scale parameter, in the case of the normal distribution, is the standard deviation. By default, the scale parameter is estimated by maximum likelihood. You can specify a fixed standard deviation by using the NOSCALE and SCALE= options in the MODEL statement.
proc print data=Residuals ; run;
Obs | x | y | Pred | Reschi | Resraw | Resdev | Stdreschi | Stdresdev | Reslik |
---|---|---|---|---|---|---|---|---|---|
1 | 0 | 5 | 5.5921 | -0.59212 | -0.59212 | -0.59212 | -0.34036 | -0.34036 | -0.34036 |
2 | 0 | 7 | 5.5921 | 1.40788 | 1.40788 | 1.40788 | 0.80928 | 0.80928 | 0.80928 |
3 | 0 | 9 | 5.5921 | 3.40788 | 3.40788 | 3.40788 | 1.95892 | 1.95892 | 1.95892 |
4 | 1 | 7 | 7.9324 | -0.93243 | -0.93243 | -0.93243 | -0.54093 | -0.54093 | -0.54093 |
5 | 1 | 10 | 7.9324 | 2.06757 | 2.06757 | 2.06757 | 1.19947 | 1.19947 | 1.19947 |
6 | 1 | 8 | 7.9324 | 0.06757 | 0.06757 | 0.06757 | 0.03920 | 0.03920 | 0.03920 |
7 | 2 | 11 | 11.2522 | -0.25217 | -0.25217 | -0.25217 | -0.14686 | -0.14686 | -0.14686 |
8 | 2 | 9 | 11.2522 | -2.25217 | -2.25217 | -2.25217 | -1.31166 | -1.31166 | -1.31166 |
9 | 3 | 16 | 15.9612 | 0.03878 | 0.03878 | 0.03878 | 0.02249 | 0.02249 | 0.02249 |
10 | 3 | 13 | 15.9612 | -2.96122 | -2.96122 | -2.96122 | -1.71738 | -1.71738 | -1.71738 |
11 | 3 | 14 | 15.9612 | -1.96122 | -1.96122 | -1.96122 | -1.13743 | -1.13743 | -1.13743 |
12 | 4 | 25 | 22.6410 | 2.35897 | 2.35897 | 2.35897 | 1.37252 | 1.37252 | 1.37252 |
13 | 4 | 24 | 22.6410 | 1.35897 | 1.35897 | 1.35897 | 0.79069 | 0.79069 | 0.79069 |
14 | 5 | 34 | 32.1163 | 1.88366 | 1.88366 | 1.88366 | 1.22914 | 1.22914 | 1.22914 |
15 | 5 | 32 | 32.1163 | -0.11634 | -0.11634 | -0.11634 | -0.07592 | -0.07592 | -0.07592 |
16 | 5 | 30 | 32.1163 | -2.11634 | -2.11634 | -2.11634 | -1.38098 | -1.38098 | -1.38098 |
The data set of predicted values and residuals (Output 39.2.2) is created by the OUTPUT statement. You can use the PLOTS= option in the PROC GENMOD statement to create plots of predicted values and residuals. Note that raw, Pearson, and deviance residuals are equal in this example. This is a characteristic of the normal distribution and is not true in general for other distributions.