search for: par5

....coef, all I get is this: std.coef(path.model.SSI4) Std. Estimate par1 par1 0.39499 com_veg <--- tempm par2 par2 0.35231 SNutBili <--- tempm par3 par3 -0.68170 S_SSI4 <--- tempm par4 par4 -0.39145 com_veg <--- Wdeficit par5 par5 -0.60025 SNutBili <--- Wdeficit par6 par6 -0.20562 S_SSI4 <--- Wdeficit par7 par7 0.14871 SNutBili <--- com_veg par8 par8 0.14905 S_SSI4 <--- com_veg par9 par9 -0.39164 S_SSI4 <--- SNutBili -- View this message in context: http://www.nabble.co...

...itted values): V0V1V2V3 V4 V5 0.56374863838.6875400.1041095890.0572495640 1.47392252639.125400.2767123290.0553929370 0.51704068441.0625450.1808219180.0532145910 1.38181318737.5625400.3534246580.0505213210 and this is my call of gnls: fm1 <- gnls(V0 ~ mymodel(V1,V2,V3,V4,V5,par1,par2,par3,par4,par5,par6,par7,par8), data=input,start=list(par1=0.2,par2=0.4,par3=0.8,par4=0.2,par5=10,par6=0.8,par7=0.9,par8=-0.5)) and I get the following error: Error in `[.data.frame`(eval(model, data.frame(data, getParsGnls(plist,  :    undefined columns selected  My guess is that R is not able to create that...

...is generating many parameter sets that are identical after rounding. Can I impose rounding on the function and thereby lessen processing time? The only function argument that seems related is the solution.tolerance, but this is not for the parameter values. Run par1 par2 par3 par4 par5 par6 Bias MAE R2 E2 E1' 507 0.0239 0.0219 0.0267 0.0274 0.0283 0.0245 -0.0112 0.0804 0.9994 0.9994 0.9792 508 0.0239 0.0219 0.0267 0.0274 0.0283 0.0245 -0.0112 0.0804 0.9994 0.9994 0.9792 509 0.0239 0.0219 0.0267 0.0274 0.0283 0.0245 -0.0112 0.0804 0.9994 0.9994 0.9792 51...

Hi group, I found a module for adaptive kernel density estimation for Stata users, but unfortunetly I don't have access to Stata, can I find a similar approach using R? Thank u so much 4 ur time. [[alternative HTML version deleted]]