Hi everybody, Got some doubts here. I'm kinda desperate for help, so please ask me if anything isn't clear.
I have a database with this structure (panel data structure): > head(dados_2) Tempo Safra Data Resposta Perc_Resg_Acum Alta_Temporada Flexi Promo 1 1 1 200701 0.04223216 0 1 0 0 2 1 2 200702 0.02801536 0 -1 0 0 3 1 3 200703 0.02786171 0 0 0 0 4 1 4 200704 0.02913633 0 0 0 0 5 1 5 200705 0.03953217 0 0 0 0 6 1 6 200706 0.05084010 0 0 0 0 Promo_Ponto_Frio Parceiros 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 > where I have 25 levels of "Tempo" and 34 for "Safra". I want to obtain the confidence intervals of the regression coefficients, and also forecast the "Resposta" variable with prediction intervals. But then, I've got some problems here: -When "Tempo" = 1 (the time index), the variable "Perc_Resg_Acum" gets 0. -I have some databases of the same kind (panel data structure) and some of then does not have any value on the variable "Promo" in the entire column. I'm modeling with the funcions pvcm() and lmList() (which are equivalent), but then, instead of giving 0 as coefficient for variable "promo", the function removes the entire column of the model and calculates the estimations. How can I do to consider the columns of zeros on the regression model and return a null coefficient instead of NA? To help with my doubts, here is part of my code: model_within1 <- pvcm(Resposta~Perc_Resg_Acum+Alta_Temporada+Flexi+Promo+Promo_Ponto_Frio+Parceiros, data = dados_2, model="within") model_within2 <- lmList(Resposta~Perc_Resg_Acum+Alta_Temporada+Flexi+Promo+Promo_Ponto_Frio+Parceiros|Tempo, data = dados_2) When I run the first model, I get this: > model_within1 <- > pvcm(Resposta~Perc_Resg_Acum+Alta_Temporada+Flexi+Promo+Promo_Ponto_Frio+Parceiros, > data = dados_2, model="within") *serie Promo_Ponto_Frio is constant and has been removed Error in eval(expr, envir, enclos) : object 'Promo_Ponto_Frio' not found*> (Well, I don't want to remove the constant column and then proceed using it) With the lmList function, I get no error message, but this outputs: >model_within2 > model_within2 Call: Model: Resposta ~ Perc_Resg_Acum + Alta_Temporada + Flexi + Promo + Promo_Ponto_Frio + Parceiros | Tempo Data: dados_2 Coefficients: (Intercept) Perc_Resg_Acum Alta_Temporada Flexi Promo 1 0.05575606 NA 0.0094899066 NA NA 2 0.02767265 0.602756910 0.0097098374 NA NA 3 0.01493001 0.216359571 0.0072083199 NA NA 4 0.01702644 0.130147260 0.0073664874 NA NA 5 0.02199162 0.077860221 0.0072053502 NA NA 6 0.02574624 0.049635548 0.0062181048 NA NA 7 0.02672193 0.035288194 0.0064811866 NA NA 8 0.03620546 0.001001478 0.0056185695 0.0117215422 NA 9 0.03834693 -0.007266645 0.0060674586 0.0107572932 NA 10 0.03851210 -0.011103720 0.0059166792 0.0099111959 NA 11 0.03877860 -0.011788541 0.0052854680 0.0085353595 NA 12 0.04213484 -0.017576921 0.0049738941 0.0084101158 NA 13 0.04217531 -0.017615294 0.0057095338 0.0094572949 NA 14 0.04591170 -0.027457745 0.0052304802 0.0091305518 NA 15 0.05575347 -0.047174244 0.0043227892 0.0070854218 NA 16 0.06751835 -0.068053502 0.0041113756 0.0035637901 NA 17 0.06743575 -0.066419074 0.0035628714 0.0027136668 NA 18 0.08494492 -0.092279778 0.0027045102 0.0025033917 0.0056991774 19 0.10540605 -0.122592396 0.0034576115 0.0007773916 0.0043499539 20 0.09374987 -0.102578612 0.0022937536 0.0003246327 -0.0033912104 21 0.09477620 -0.103937511 0.0018046064 -0.0019254150 0.0038385416 22 0.07984309 -0.081880920 0.0031004004 0.0012212949 -0.0001274436 23 0.04209354 -0.027693308 0.0033759713 0.0012759561 -0.0005342256 24 0.02248439 0.001793878 0.0019126335 0.0016330109 -0.0012942994 25 -0.04124712 0.093798787 -0.0009151255 0.0026952764 0.0002002742 Promo_Ponto_Frio Parceiros 1 NA 0.0085825438 2 NA -0.0040152859 3 NA -0.0015317053 4 NA -0.0016866579 5 NA -0.0014183949 6 NA -0.0016753846 7 NA -0.0012411159 8 NA -0.0016690746 9 NA -0.0018987163 10 NA -0.0016922052 11 NA -0.0017404386 12 NA -0.0017259225 13 NA -0.0014849246 14 NA -0.0014719829 15 NA -0.0016265977 16 NA -0.0015527121 17 NA -0.0014492467 18 NA -0.0016308425 19 NA -0.0013443498 20 NA -0.0012088912 21 NA -0.0006974880 22 NA -0.0006981946 23 NA -0.0006599528 24 NA -0.0004592202 25 NA -0.0020974059 And, because this NAs, when I run summary(model_within2), I've got only estimations, std.errors and quantiles of t of 3 variables. Is there a way to solve this problem? A way to consider also the constant columns on my model? Help me, please!!! -- View this message in context: http://r.789695.n4.nabble.com/Problems-with-Panel-Data-estimation-tp4306602p4306602.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list 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.
