Error Error In Computing The Variance Function
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First | Previous | Next | Last ] By Author: [ First | Previous | Next | https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=allstat;4d785bda.02 Last ] Font: Proportional Font LISTSERV Archives ALLSTAT Home ALLSTAT https://onlinecourses.science.psu.edu/stat501/node/254 2002 Options Subscribe or Unsubscribe Log In Get Password Subject: Problem in PROC GENMOD in a SAS program. From: Khaled Hassan <[log in to unmask]> Reply-To:Khaled Hassan <[log in to unmask]> Date:Mon, 8 Apr 2002 08:17:28 +0200 Content-Type:text/plain Parts/Attachments: text/plain (59 lines) error in Dear allstat users. In a SAS program. the procedure was as following: proc genmod data=ADJUST; CLASS RID &X AGECAT4 BCSEASON BCBFEXCL BCGBCONI BCCROWD; model &Y/days= &X AGECAT4 BCSEASON BCBFEXCL BCGBCONI BCCROWD / dist=poisson link=log; REPEATED SUBJECT=RID/type=exch; run; where &y is a variable =1 if its an event and 0 if nonevent and days error error in is a person years of follow up. and all the independent variables are numeric and no missing observations. when I run the modle if the following log message: 567 proc genmod data=ADJUST; 568 CLASS RID &X AGECAT4 BCSEASON BCBFEXCL BCGBCONI BCCROWD; 569 model &Y/days= &X AGECAT4 BCSEASON BCBFEXCL BCGBCONI BCCROWD / dist=poisson 570 link=log; 571 REPEATED SUBJECT=RID/type=exch; 572 run; WARNING: The binomial response distribution is usually appropriate for a ratio response variable. NOTE: Non-integer response values have been detected for the POISSON distribution. NOTE: The scale parameter was held fixed. ERROR: Error in computing the variance function. NOTE: No convergence of GEE parameter estimates after 2 iterations. NOTE: The scale parameter for GEE estimation was computed as the square root of the normalized Pearson's chi-square. NOTE: The PROCEDURE GENMOD used 41.13 seconds. Can any one help me and explain the source of error and how i correct it. Wait to hear from you. Have a n
entrance test scores for each subpopulation have equal variance. We denote the value of this common variance as σ2. That is, σ2 quantifies how much the responses (y) vary around the (unknown) mean population regression line \(\mu_Y=E(Y)=\beta_0 + \beta_1x\). Why should we care about σ2? The answer to this question pertains to the most common use of an estimated regression line, namely predicting some future response. Suppose you have two brands (A and B) of thermometers, and each brand offers a Celsius thermometer and a Fahrenheit thermometer. You measure the temperature in Celsius and Fahrenheit using each brand of thermometer on ten different days. Based on the resulting data, you obtain two estimated regression lines — one for brand A and one for brand B. You plan to use the estimated regression lines to predict the temperature in Fahrenheit based on the temperature in Celsius. Will this thermometer brand (A) yield more precise future predictions …? … or this one (B)? As the two plots illustrate, the Fahrenheit responses for the brand B thermometer don't deviate as far from the estimated regression equation as they do for the brand A thermometer. If we use the brand B estimated line to predict the Fahrenheit temperature, our prediction should never really be too far off from the actual observed Fahrenheit temperature. On the other hand, predictions of the Fahrenheit temperatures using the brand A thermometer can deviate quite a bit from the actual observed Fahrenheit temperature. Therefore, the brand B thermometer should yield more precise future predictions than the brand A thermometer. To get an idea, therefore, of how precise future predictions would be, we need to know how much the responses (y) vary around the (unknown) mean population regression line \(\mu_Y=E(Y)=\beta_0 + \beta_1x\). As stated earlier, σ2 quantifies this variance in the responses. Will we ever know this value σ2? No! Because σ2 is a population parameter, we will rarely know its true value. The best we can do is estimate it! To understand the formula for the estimate of σ2 in the simple linear regression setting, it is helpful to recall the formula for the estimate of the variance of the responses, σ2, when there is only one population. The following is a plot of the (one) population of IQ measurements. As the plot suggests, the average of the IQ measurements in the population is 100. But, how much do the IQ measurements vary from the mean? That is, how "spread out" are the IQs? The sample variance: \[s^2=\frac{\sum_{i=1}^{n}(y_i-\bar{y})^2}{n-1}\] estimates σ2, the variance of the one population. The estimate is r