I apologize for sending this message again. The last time I sent it, the subject line was not correct. I have corrected the subject line. I am trying to run a repeated measures analysis of data in which each subject (identified by SS) has 3 observations at three different times (0, 3, and 6). There are two groups of subjects (identified by group). I want to know if the response differs in the two groups. I have tried to used lme. Lme tell me if there is a time effect, but does not tell me if there is a group effect. Once I get this to work I will want to know if there is a significant group*time effect. Can someone tell me how to get an estimate for group. Once I get that, I believe getting an estimate for group*time should be straight forward. The code I have tired to use follows. Thank you, John > # This is my data > data1 SS group time value baseline 1 1 Cont 0 9.000000 9.000000 2 2 Cont 0 3.000000 3.000000 3 3 Cont 0 8.000000 8.000000 4 4 Inte 0 5.690702 5.690702 5 5 Inte 0 7.409493 7.409493 6 6 Inte 0 7.428018 7.428018 7 1 Cont 3 13.713148 9.000000 8 2 Cont 3 9.841107 3.000000 9 3 Cont 3 12.843236 8.000000 10 4 Inte 3 9.300899 5.690702 11 5 Inte 3 10.936389 7.409493 12 6 Inte 3 12.358499 7.428018 13 1 Cont 6 18.952390 9.000000 14 2 Cont 6 15.091527 3.000000 15 3 Cont 6 17.578812 8.000000 16 4 Inte 6 12.325499 5.690702 17 5 Inte 6 15.486513 7.409493 18 6 Inte 6 18.284965 7.428018 > # Create a grouped data object. SS identifies each subject > # group indentifies group, intervention or control. > GD<- groupedData(value~time|SS/group,data=data1,FUN=mean) > # Fit the model. > fit1 <- lme(GD) > cat("The results give information about time, but does not say if the gruops > are different\n") The results give information about time, but does not say if the gruops are different > summary(fit1) Linear mixed-effects model fit by REML Data: GD AIC BIC logLik 74.59447 81.54777 -28.29724
Random effects: Formula: ~time | SS Structure: General positive-definite StdDev Corr (Intercept) 1.3875111 (Intr) time 0.2208046 -0.243 Formula: ~time | group %in% SS Structure: General positive-definite StdDev Corr (Intercept) 1.3875115 (Intr) time 0.2208051 -0.243 Residual 0.3800788 Fixed effects: value ~ time Value Std.Error DF t-value p-value (Intercept) 6.747442 0.8135067 11 8.294268 0 time 1.588653 0.1326242 11 11.978601 0 Correlation: (Intr) time -0.268 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.11412947 -0.44986535 0.08034174 0.34615610 1.29943887 Number of Observations: 18 Number of Groups: SS group %in% SS 6 6 > John David Sorkin M.D., Ph.D. Professor of Medicine Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone) 410-605-7119 (Fax) 410-605-7913 (Please call phone number above prior to faxing) Confidentiality Statement: This email message, including any attachments, is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.