Piecewise HLM model using nlme package in R -


i have 2 time periods of interest , 4 observation points(0 months, 4 months, 12 months, 16 months) subjects. first time period of interest between observation 1 , observation 3. second time period of interest between observation 3 , observation 4.

i run hlm account correlation of observations on same subject. have pasted sample data , code , output below.

when compare model output actual means similar in case. however, when use actual data set less similar. imply? can tell me if have coded time appropriately? goal compare effect of treatment during time period 1 effect of treatment during time period 2. thank you!

library(nlme)  #run model , output model=lme(response~time1*treatment+time2*treatment,        random=~time1+time2|subject,data=test,control=list(opt="optim"))   round(summary(model)$ttable,dig=3)  # output                value std.error df t-value p-value (intercept)     172.357     2.390 41  72.110   0.000 time1             0.464     0.062 41   7.496   0.000 treatment       -10.786     3.499 13  -3.083   0.009 time2            -0.795     0.130 41  -6.113   0.000 time1:treatment  -0.089     0.091 41  -0.985   0.331 treatment:time2   0.563     0.190 41   2.956   0.005  # means treatment , time vs. model  mean(test$response[test$treatment==1 & test$observation==1]) [1] 161.1429 #model 172.357-10.786 [1] 161.571  mean(test$response[test$treatment==0 & test$observation==1]) [1] 171.75 #model [1] 172.357

sample data used output:

     subject  treatment  observation  time  time2  response              1   0   1   0   0   170             1   0   2   4   0   175             1   0   3   12  0   177             1   0   4   12  4   173             2   1   1   0   0   160             2   1   2   4   0   162             2   1   3   12  0   165             2   1   4   12  4   165             3   0   1   0   0   172             3   0   2   4   0   177             3   0   3   12  0   180             3   0   4   12  4   175             4   1   1   0   0   162             4   1   2   4   0   166             4   1   3   12  0   168             4   1   4   12  4   167             5   1   1   0   0   163             5   1   2   4   0   167             5   1   3   12  0   169             5   1   4   12  4   167             6   0   1   0   0   179             6   0   2   4   0   182             6   0   3   12  0   184             6   0   4   12  4   180             7   0   1   0   0   155             7   0   2   4   0   158             7   0   3   12  0   160             7   0   4   12  4   157             8   1   1   0   0   152             8   1   2   4   0   155             8   1   3   12  0   157             8   1   4   12  4   157             9   0   1   0   0   170             9   0   2   4   0   174             9   0   3   12  0   179             9   0   4   12  4   177             10  1   1   0   0   162             10  1   2   4   0   164             10  1   3   12  0   165             10  1   4   12  4   165             11  1   1   0   0   164             11  1   2   4   0   165             11  1   3   12  0   168             11  1   4   12  4   167             12  0   1   0   0   174             12  0   2   4   0   175             12  0   3   12  0   176             12  0   4   12  4   175             13  0   1   0   0   184             13  0   2   4   0   185             13  0   3   12  0   186             13  0   4   12  4   184             14  1   1   0   0   165             14  1   2   4   0   167             14  1   3   12  0   169             14  1   4   12  4   168             15  0   1   0   0   170             15  0   2   4   0   175             15  0   3   12  0   179             15  0   4   12  4   177

thanks.


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