Hi, I am trying to fit my uptake data to different models using PROC NLMIXED. However, I am a new user of NLMIXED and looking for a code that will help in differentiating the parameters of the two levels of fixed effect.  I have two plants (Cabbage and grass) and will like to be able to estimate if parameter A for cabbage is different from parameter A of switchgrass. I have attached the code and will like to know if I am on the right track.  Thanks title 'Logistic growth model individual parameters for plants'; proc nlmixed data=uptake; parms A1=0.2 to 0.5 by 0.1 A2=0.2 to 0.5 by 0.1 B1=8 to 21 by 0.5 B2=8 to 21 by 0.5 k1=0.3 k2=0.3s2e=0.01528 s2u=0.01528; if Plant='Cabbage' then eta=(A1/(1+exp(-k1*Day+B1*k1))); else eta=(A2/(1+exp(-k2*Day+B2*k2))); p = eta; model Amount ~ normal(p,s2e); Estimate 'A1-A2' A1-A2; Estimate 'B1-B2' B1-B2; Estimate 'k1-k2' k1-k2; predict p out=uptakeout; run; ... View more

Hello, I am having trouble with my code. I have sorption data and trying to fit it to Langmuir Isotherm. There are 5 concentrations (represented by conc in the code) and two plants (C and L). I have 3 blocks which represent the time at which I ran the sorption experiment. qt is the amount sorbed at equilibrium. When I run the code,  I keep getting the error " WARNING: Maximum number of iterations exceeded. WARNING: PROC NLIN failed to converge. NOTE: The above message was for the following BY group: Plant=C block=3"  I tried changing the starting parameters and even the method but keep getting that error for that particular block. The data for that block seems fine and there appears to be no anomaly.  I will appreciate suggestions on how to fix this. Thanks.   Here is the code:   data Isotherm; input Plant $ conc block ce qt; Datalines; C 5 1 0.0019435976 0.0556402439 C 10 1 0.003973607 0.1026392962 C 15 1 0.0078216918 0.2178308178 C 20 1 0.0116147443 0.338525573 C 25 1 0.0159964979 0.4003502126 C 5 2 0.0018444556 0.0655544441 C 10 2 0.0040143691 0.0985630894 C 15 2 0.0076276945 0.237230553 C 20 2 0.0115231028 0.3476897185 C 25 2 0.0158549932 0.414500684 C 5 3 0.0021044099 0.0395590143 C 10 3 0.0040763926 0.0923607392 C 15 3 0.0084038988 0.1596101231 C 20 3 0.0122161836 0.2783816425 C 25 3 0.016261635 0.3738365034 L 5 1 0.0008449972 0.1655002772 L 10 1 0.0017438207 0.3256179305 L 15 1 0.0036381186 0.6361881362 L 20 1 0.0054052714 0.9594728589 L 25 1 0.0076868098 1.2313190151 L 5 2 0.0008488139 0.1651186053 L 10 2 0.0018084868 0.3191513247 L 15 2 0.00368791 0.6312089972 L 20 2 0.0056812271 0.931877295 L 25 2 0.0080631555 1.1936844505 L 5 3 0.001515348 0.0984651967 L 10 3 0.0028983664 0.2101633637 L 15 3 0.0056429549 0.4357045144 L 20 3 0.0087318841 0.6268115942 L 25 3 0.0120204371 0.797956293 ; title 'Sorption Isotherm: Langmuir'; proc nlin data=Isotherm method=marquardt hougaard;by plant block; parameters k=1 to 10 by 1 Smax=0.1 to 5 by 0.1; model qt = ((K*Smax*Ce)/(1+K*Ce)); output out=mod1 predicted=pred l95m=l95mean u95m=u95mean l95=l95ind u95=u95ind residual=res student=s sse=sse parms=k Smax run; Proc Sort Data=mod1; by conc plant ; run; data mod1; set mod1; proc print data=mod1; run; proc means noprint var std stderr uss css; var qt pred res; output out = mod2 n=numobs var=qt_v pred_v res_v uss=qt_uss pred_uss res_uss css=css pred_css res_css; run; data mod3; set mod1; set mod2; rsq = 1 - sse/css; *i.e., pseudo rsq; rmse=sqrt(res_v); run; proc print data=mod3; var rsq rmse; run; Title 'Print of parameterestimates'; proc print data=mod1; run; ... View more