Just realised that I also need to combine by animal, have observed more than one cow and all data include more than one cow... This is have2, timestamp is formatted at datetime20. in my sas dataset. Timestamp Cow_ID State Zone 1539246925 6300 Standing Feeding 1539247204 6300 Eating Feeding 1539247527 6300 Eating Feeding 1539247812 6300 Eating Feeding have2 start_time end_time cow_id 11OCT18:08:31:40 11OCT18:08:32:40 6300 11OCT18:08:32:40 11OCT18:08:33:40 6300 11OCT18:08:32:40 11OCT18:08:33:40 6300 11OCT18:08:33:40 11OCT18:08:34:40 6300 11OCT18:08:33:40 11OCT18:08:34:40 6300 so not only do I need to merge these two files by time but also by cow_id. There is a lot more variable in both files, but as I can see from what I can do in the in program you wrote, it does not really matter since they are all included ... View more

No sorry, these wa just copy and paste from two different dataset 🙂   What I am looking for is that the Have1 dataset, which also have a varible called 'behaviour', which is the behaviour recorded during the time span, can be merged with the have2 and the behaviour recorded on a specific time in the have2 is merged to the right time period in have1.   So if we in have2 have a datapoint with a corresponding recorded behaviour/variable on: 11oct18:08:31:57 then this datapoint is merged into have1 on the same line as 11oct18:08:31:40  11oct18:08:32:40   ... View more

******************************************************************************* * This program indicates how to construct a bivariate scatterplot with an * * overlay of the line of identity. * * * * This program also provides an IML module for calculating point and * * interval estimates of the Pearson correlation coefficient and the * * concordance correlation coefficient. * *******************************************************************************; data dice_baseline; input subject cort_auc1 cort_auc2; cards; 61001 5.28170 5.37851 61002 5.58796 5.33628 61003 6.47607 6.59770 61005 6.36019 6.39746 61007 5.81121 5.82528 61008 6.03036 6.21147 61009 5.84549 6.10434 61014 6.80349 6.90689 61015 6.28977 6.27369 61023 5.88446 5.94352 61024 5.79701 6.04876 61026 5.01302 4.72154 61027 6.48824 6.37891 61028 5.30862 5.53405 61033 5.70905 5.77803 61034 5.98545 5.77613 61035 6.19924 6.20880 61036 6.00639 5.98313 61037 6.27793 6.44342 61038 6.57390 6.62936 61039 5.69639 5.43509 61042 5.96588 6.01282 61044 6.04803 6.11529 61046 6.39423 6.03876 62002 7.44584 7.58421 62003 5.90813 5.87230 62004 6.05483 5.94695 62005 5.65735 5.64983 62006 6.44815 6.44280 62007 6.28611 6.45374 62009 6.40863 6.14994 62012 5.62564 5.58142 62013 6.68375 6.58815 62014 5.76951 5.84802 62015 5.94383 5.95489 62016 5.66024 5.73711 62017 4.77492 4.57465 62018 5.60468 5.43495 62019 6.82819 6.86652 62020 5.18986 5.05725 62021 6.48810 6.59655 62022 6.08867 5.76965 62023 5.91400 5.89672 62024 5.58217 5.57651 62026 6.32857 6.48921 62027 7.67703 7.76541 62028 5.92411 5.66689 62029 6.15313 6.16558 62030 5.20392 5.42481 62032 6.43962 6.46171 62033 6.20661 6.28542 63001 6.04767 5.82528 63002 6.46923 6.51728 63003 5.68370 5.79701 63004 5.11719 5.47363 63005 6.10993 6.13541 63006 4.91744 5.06968 63008 5.35972 5.56605 63010 6.73016 6.76285 63011 5.93700 5.94092 63012 6.07716 5.92548 63014 6.58185 6.52781 63015 5.84317 5.85030 63016 5.98144 6.22389 63017 5.77452 5.81662 63018 5.46142 5.84898 63021 5.44920 5.43688 63022 5.96519 6.01302 63026 6.29258 6.42339 63027 6.86608 6.92806 63028 5.47875 5.72634 63030 6.16190 6.16608 63032 5.66707 5.97114 63033 5.80634 5.63640 63034 6.37256 6.24416 63035 5.65755 5.98070 64001 6.07596 6.06010 64002 6.57898 6.54552 64003 6.60733 6.89724 64004 5.69611 5.82963 64005 6.60331 6.62972 64007 5.89963 5.83195 64008 6.19731 6.07044 64009 5.88875 6.03427 64010 5.64912 5.46135 64011 6.99962 7.10324 64012 6.61282 6.68520 64015 6.60477 6.76468 64016 5.35161 5.55307 64017 6.63249 6.77868 64020 5.37717 5.19760 64023 5.58781 5.87044 64025 5.74499 5.77090 64027 5.92655 6.09265 64028 5.01060 5.17112 64030 7.12400 7.17368 64031 5.79909 5.37603 64032 5.75609 5.85174 64033 6.79275 6.78255 64035 6.02198 6.03915 64036 5.43960 5.82229 64037 5.20163 5.12928 65001 6.18838 6.66593 65002 6.13860 6.26224 65003 6.98807 6.97658 65005 5.54628 5.50547 65006 4.47249 4.59256 65007 5.04034 5.07775 65008 5.42025 5.46227 65009 5.26772 5.41463 65011 6.43019 6.38438 65013 6.56323 6.46607 65014 5.06134 4.70619 65016 6.32666 6.31564 65017 5.90235 6.05890 65018 6.05800 5.99467 65020 5.96388 6.01204 65021 5.57324 5.61324 65022 6.21017 6.15262 65025 6.01934 6.00318 65026 5.52082 5.54575 65027 5.89237 5.67469 65028 6.24592 6.37106 65031 6.31524 6.44334 65032 6.19602 6.29576 65033 6.07305 6.07966 65037 5.60960 5.38492 65038 5.39806 5.18466 65040 5.95464 6.20802 66003 5.96880 5.86128 66004 5.89707 5.69116 66005 6.27067 6.35294 66006 5.39744 5.23236 66010 6.64408 6.64990 66011 6.29430 6.32724 66012 5.22507 5.28754 66013 5.15840 5.05580 66014 5.85385 5.54974 66018 6.49611 5.87795 66019 5.43285 5.50871 66021 6.32416 6.31612 66023 5.77253 5.74469 66024 5.89920 5.95774 ; run; proc means data=dice_baseline noprint; var cort_auc1 cort_auc2; output out=dice_baselinemin min=var1 var2; run; proc means data=dice_baseline noprint; var cort_auc1 cort_auc2; output out=dice_baselinemax max=var1 var2; run; data dice_baselineall; set dice_baselinemin dice_baselinemax; drop _type_ _freq_; if _N_=1 then var1=floor(min(var1,var2)); if _N_=1 then var2=floor(min(var1,var2)); if _N_=2 then var1=ceil(max(var1,var2)); if _N_=2 then var2=ceil(max(var1,var2)); run; data dice_baselineall; set dice_baseline dice_baselineall; run; proc gplot uniform data=dice_baselineall; plot cort_auc1*cort_auc2 var1*var2/overlay vaxis=axis1 haxis=axis2 nolegend frame; axis1 label=(a=90 'Cortisol Every Hour') minor=none; axis2 label=('Cortisol Every Two Hours') minor=none; symbol1 value=star color=black interpol=none; symbol2 value=none color=black interpol=join; title "Concordance Correlation Coefficient"; run; proc iml; ******************************************************************************* * Enter the appropriate SAS data set name in the use statement and enter the * * appropriate variable names in the read statements. * *******************************************************************************; use dice_baseline; read all var {cort_auc1} into var1; read all var {cort_auc2} into var2; ******************************************************************************* * The IML module, labeled concorr, starts next. * *******************************************************************************; start concorr; nonmiss=loc(var1#var2^=.); var1=var1[nonmiss]; var2=var2[nonmiss]; free nonmiss; n=nrow(var1); mu1=sum(var1)/n; mu1=round(mu1,0.0001); mu2=sum(var2)/n; mu2=round(mu2,0.0001); sigma11=ssq(var1-mu1)/(n-1); sigma11=round(sigma11,0.0001); sigma22=ssq(var2-mu2)/(n-1); sigma22=round(sigma22,0.0001); sigma12=sum((var1-mu1)#(var2-mu2))/(n-1); sigma12=round(sigma12,0.0001); lshift=(mu1-mu2)/((sigma11#sigma22)##0.25); rho=sigma12/sqrt(sigma11#sigma22); rho=round(rho,0.0001); z=log((1+rho)/(1-rho))/2; se_z=1/sqrt(n-3); t=tinv(0.975,n-3); z_low=z-(se_z#t); z_upp=z+(se_z#t); rho_low=(exp(2#z_low)-1)/(exp(2#z_low)+1); rho_low=round(rho_low,0.0001); rho_upp=(exp(2#z_upp)-1)/(exp(2#z_upp)+1); rho_upp=round(rho_upp,0.0001); crho=(2#sigma12)/((sigma11+sigma22)+((mu1-mu2)##2)); crho=round(crho,0.0001); z=log((1+crho)/(1-crho))/2; if sigma12^=0 then do; t1=((1-(rho##2))#(crho##2))/((1-(crho##2))#(rho##2)); t2=(2#(crho##3)#(1-crho)#(lshift##2))/(rho#((1-(crho##2))##2)); t3=((crho##4)#(lshift##4))/(2#(rho##2)#((1-(crho##2))##2)); se_z=sqrt((t1+t2-t3)/(n-2)); end; else se_z=sqrt(2#sigma11#sigma22)/((sigma11+sigma22+((mu1-mu2)##2))#(n-2)); t=tinv(0.975,n-2); z_low=z-(se_z#t); z_upp=z+(se_z#t); crho_low=(exp(2#z_low)-1)/(exp(2#z_low)+1); crho_low=round(crho_low,0.0001); crho_upp=(exp(2#z_upp)-1)/(exp(2#z_upp)+1); crho_upp=round(crho_upp,0.0001); Results=n//mu1//mu2//sigma11//sigma22//sigma12//rho_low//rho//rho_upp// crho_low//crho//crho_upp; r_name={'SampleSize' 'Mean_1' 'Mean_2' 'Variance_1' 'Variance_2' 'Covariance' 'Corr LowerCL' 'Corr' 'Corr UpperCL' 'ConcCorr LowerCL' 'ConcCorr' 'ConcCorr UpperCL'}; print 'The Estimated Correlation and Concordance Correlation (and 95% Confidence Limits)'; print Results [rowname=r_name]; finish concorr; ******************************************************************************* * The IML module, labeled concorr, is finished. * *******************************************************************************; run concorr; ... View more

Thanks for your swift responses. I assume the UNIQUE-LOC is the procedure that might work on my data and I will test it with my limited knowledge of programming out of my comfort zone in SAS. Otherwise I just have to perform 300 runs of the procedure 🙂 I will specify the animal number, out of 30, that I have in my dataset and then run the analysis. Would however have been nice to have a procedure in sas that does this for me in the proc iml, but I understand that it is not that simple 🙂   Kind regards, Per ... View more

Hi Art,   I hope it is okay that I email you with an additional question to this one. I have been asked to include repeated measures in my analysis, but have been discussing this with a statistician who on the other hand suggested that I did a CCC analysis for each animal (I have 20) and then made an additional coloum to the table with results for min-max for these 20 CCCs'. Normally when you run a Proc statement in SAS you can include a BY statement, do you know if this is possible in a PROC IMLas this one:   proc iml; use data.ice_cs; read all var {iceMinlying} into var1; read all var {csMinlying} into var2; start concorr; nonmiss=loc(var1#var2^=.); var1=var1[nonmiss]; var2=var2[nonmiss]; free nonmiss; n=nrow(var1); mu1=sum(var1)/n; mu1=round(mu1,0.0001); mu2=sum(var2)/n; mu2=round(mu2,0.0001); sigma11=ssq(var1-mu1)/(n-1); sigma11=round(sigma11,0.0001); sigma22=ssq(var2-mu2)/(n-1); sigma22=round(sigma22,0.0001); sigma12=sum((var1-mu1)#(var2-mu2))/(n-1); sigma12=round(sigma12,0.0001); lshift=(mu1-mu2)/((sigma11#sigma22)##0.25); rho=sigma12/sqrt(sigma11#sigma22); rho=round(rho,0.0001); z=log((1+rho)/(1-rho))/2; se_z=1/sqrt(n-3); t=tinv(0.975,n-3); z_low=z-(se_z#t); z_upp=z+(se_z#t); rho_low=(exp(2#z_low)-1)/(exp(2#z_low)+1); rho_low=round(rho_low,0.0001); rho_upp=(exp(2#z_upp)-1)/(exp(2#z_upp)+1); rho_upp=round(rho_upp,0.0001); crho=(2#sigma12)/((sigma11+sigma22)+((mu1-mu2)##2)); crho=round(crho,0.0001); z=log((1+crho)/(1-crho))/2; if sigma12^=0 then do;    t1=((1-(rho##2))#(crho##2))/((1-(crho##2))#(rho##2));    t2=(2#(crho##3)#(1-crho)#(lshift##2))/(rho#((1-(crho##2))##2));    t3=((crho##4)#(lshift##4))/(2#(rho##2)#((1-(crho##2))##2));    se_z=sqrt((t1+t2-t3)/(n-2)); end; else se_z=sqrt(2#sigma11#sigma22)/((sigma11+sigma22+((mu1-mu2)##2))#(n-2)); t=tinv(0.975,n-2); z_low=z-(se_z#t); z_upp=z+(se_z#t); crho_low=(exp(2#z_low)-1)/(exp(2#z_low)+1); crho_low=round(crho_low,0.0001); crho_upp=(exp(2#z_upp)-1)/(exp(2#z_upp)+1); crho_upp=round(crho_upp,0.0001); Results=n//mu1//mu2//sigma11//sigma22//sigma12//rho_low//rho//rho_upp//         crho_low//crho//crho_upp; r_name={'SampleSize' 'Mean_1' 'Mean_2' 'Variance_1' 'Variance_2' 'Covariance' 'Corr LowerCL'        'Corr' 'Corr UpperCL' 'ConcCorr LowerCL' 'ConcCorr' 'ConcCorr UpperCL'}; print 'The Estimated Correlation and Concordance Correlation (and 95% Confidence Limits) IceTag vs CowScout lying'; print Results [rowname=r_name]; finish concorr; run concorr;   Kind regards, Per ... View more

Worked perfectly, got the same results as from the home page, just feels a lot better to have a SAS program doing the analysis. Thanks for this suggestion! ... View more

Thanks Reeza for your suggestions. I have had this through a statistician, since I am also a bit rusty on that point, and they suggested this analysis ... View more

This works perfectly, Thanks for your help and thanks to all the participants in this discussion. Your input has been really valuable for me. ... View more

The data looks like this. I need to be able to summarize the data 'mot', 'stan', 'lie' and 'step' within 15 min interval starting at 10:05:48, so when summarizing the data I need to be able to specify when the first 15 min period should start. Date           Mot  stan   Lie Step   Time     Cow 27/02/2015    0    100    0    0    10:01:18    4009 27/02/2015    0    100    0    0    10:01:19    4009 27/02/2015    0    100    0    0    10:01:20    4009 27/02/2015    0    100    0    0    10:01:21    4009 27/02/2015    0    100    0    0    10:01:22    4009 27/02/2015    0    100    0    0    10:01:23    4009 27/02/2015    0    100    0    0    10:01:24    4009 27/02/2015    0    100    0    0    10:01:25    4009 27/02/2015    0    100    0    0    10:01:26    4009 27/02/2015    0    100    0    0    10:01:27    4009 27/02/2015    0    100    0    0    10:01:28    4009 27/02/2015    0    100    0    0    10:01:29    4009 27/02/2015    0    100    0    0    10:01:30    4009 27/02/2015    0    100    0    0    10:01:31    4009 I want something like this: datetime cow _TYPE_ _FREQ_ mmotion mstanding mlying msteps 27FEB15:11:16:23 4009 0 900 0 0 100 0 27FEB15:11:31:23 4009 0 900 0 0 100 0 Does this make sense? ... View more