Hi Steve, I included the code as well as the SAS output. Is that all you need? I'm really grateful for your help here! proc glimmix data=candidates; nloptions maxiter=100;   class commune ;   model  binouverture (event='1') = EurostatA EurostatC sizelog ENP commune       / solution dist=binary link=logit ddfm=kenwardrogers ; /* Addresses individual commune levels, and adds the Kenward-Rogers adjustment for repeated measures designs.  This also applies the averaged effects of the continuous covariates across all communes */   random intercept / subject= commune solution residual; /* Converts to a repeated measures design.  Even though this is a RANDOM statement, the residual option means treat the values within a commune as correlated.  */ lsmeans commune/ilink cl; /* Outputs the estimates for each level of commune, on both the logit and original scale */ run; Jérémy Le Système SAS The GLIMMIX Procedure Data Set WORK.CANDIDATES Response Variable binouverture Response Distribution Binary Link Function Logit Variance Function Default Variance Matrix Blocked By Commune Estimation Technique Residual PL Degrees of Freedom Method Kenward-Roger Fixed Effects SE Adjustment Prasad-Rao-Kackar-Harville-Jeske Commune 252 Aiseau-Presles Amay Amel Andenne Anderlues Anhée Ans Anthisnes Antoing Arlon Assesse Ath Attert Aubange Aubel Awans Aywaille Baelen Bassange Bastogne Beaumont Beauraing Beauvechain Beloeil Berloz Bernissart Bertogne Bertrix Beyne-Heusay Binche Bièvre Blegny Bouillon Boussu Braine-l'Alleud Braine-le-Château Braine-le-Comte Braives Brugelette Brunehaut Burdinne Burg-Reuland Büllingen Bütgenbach Celles Cerfontaine Chapelle-Lez-Herlaimont Charleroi Chastre Chaudfontaine Chaumont-Gistoux Chimay Chiny Chièvres Châtelet Ciney Colfontaine Comblain-au-Pont Comines-Warneton Courcelles Court-St-Etienne Couvin Crisnée Dalhem Dinant Dison Doische Donceel Dour Durbuy Ecaussinnes Eghezée Ellezelles Enghien Engis Erezée Erquelinnes Esneux Estaimpuis Estinnes Etalle Eupen Faimes Farciennes Fauvillers Fernelmont Ferrières Fexhe-le-Haut-Clocher Flemalle Fleurus Flobecq Floreffe Florennes Florenville Fléron Fontaine-l'Evêque Fosses-la-Ville Frameries Frasnes-lez-Anvaing Froidchapelle Gedinne Geer Gembloux Genappe Gerpinnes Gesves Gouvy Grace-Hollogne Grez-Doiceau Habay Ham-sur-Heure-Nalinnes Hamois Hannut Hastière Havelange Hensies Herbeumont Herstal Herve Honnelles Hotton Houffalize Houyet Huy Hélecine Héron Incourt Ittre Jalhay Jemeppe-sur-Sambre Jodoigne Juprelle Jurbise Kelmis La Bruyère La Hulpe La Louvière La Roche-en-Ardenne Lasne Le Roeulx Lens Les-Bons-Villers Lessines Leuze-en-Hainaut Libramont-Chevigny Lierneux Limbourg Lincent Liège Lobbes Lontzen Malmedy Manage Manhay Marche-en-Famenne Marchin Martelange Meix-devant-Virton Merbes-le-Château Messancy Mettet Modave Momignies Mons Mont-St-Guibert Mont-de-l'Enclus Montigny-le-Tilleul Morlanwelz Mouscron Musson Namur Nandrin Nassogne Neufchâteau Neupré Nivelles Ohey Olne Onhaye Oreye Orp-Jauche Ottignies-LLN Ouffet Oupeye Paliseul Pecq Pepinster Perwez Philippeville Plombières Pont-à-Celles Profondeville Péruwelz Quaregnon Quiévrain Quévy Raeren Ramilies Rebecq Remicourt Rixensart Rochefort Rouvroy Rumes Saint Nicolas Saint-Georges-sur-Meuse Saint-Ghislain Saint-Hubert Sainte-Ode Sambreville Sankt Vith Seneffe Seraing Silly Sivry-Rance Soignies Sombreffe Somme-Leuze Soumagne Spa Sprimont Stavelot Stoumont Tellin Theux Thimister-Clermont Thuin Tinlot Tintigny Tournai Trooz Tubize Vaux-sur-Sûre Verlaine Verviers Vielsalm Villers-la-Ville Villers-le-Bouillet Viroinval Virton Visé Vresse-sur-Semois Walcourt Walhain Wanze Waremme Wasseiges Waterloo Wavre Welkenraedt Wellin Yvoir Number of Observations Read 804 Number of Observations Used 803 1 0 476 2 1 327 R-side Cov. Parameters 1 Columns in X 257 Columns in Z per Subject 0 Subjects (Blocks in V) 252 Max Obs per Subject 14 Optimization Technique None Parameters 0 Lower Boundaries 0 Upper Boundaries 0 Fixed Effects Profiled Residual Variance Profiled Starting From Data 0 0 0 1.340781E154 2.00000000 . 1 0 0 1.340781E154 1.13896683 . 2 0 0 1.340781E154 2.00000000 . 3 0 0 1.340781E154 2.00000000 . 4 0 0 1.340781E154 0.91900698 . 5 0 0 3460.7243613 2.00000000 . 6 0 0 1.340781E154 2.00000000 . 7 0 0 3728.5751363 2.00000000 . 8 0 0 1.340781E154 2.00000000 . 9 0 0 3993.8654206 2.00000000 . 10 0 0 4127.2354391 2.00000000 . 11 0 0 1.340781E154 2.00000000 . 12 0 0 4394.1129597 2.00000000 . 13 0 0 4525.2340423 2.00000000 . 14 0 0 4658.3500927 2.00000000 . 15 0 0 4791.7156 2.00000000 . 16 0 0 4924.1913727 2.00000000 . 17 0 0 5059.8010277 2.00000000 . 18 0 0 5191.3961115 2.00000000 . 19 0 0 5324.1823992 2.00000000 . 20 0 0 1.340781E154 2.00000000 . 21 0 0 1.340781E154 2.00000000 . 22 0 0 1.340781E154 2.00000000 . 23 0 0 1.340781E154 2.00000000 . 24 0 0 1.340781E154 2.00000000 . 25 0 0 1.340781E154 0.99810273 . 26 0 0 1.340781E154 0.81393176 . 27 0 0 1.340781E154 2.00000000 . 28 0 0 3148.7088811 2.00000000 . 29 0 0 3281.2624666 2.00000000 . 30 0 0 1.340781E154 2.00000000 . 31 0 0 3547.6660221 2.00000000 . 32 0 0 3677.9846413 2.00000000 . 33 0 0 3814.0718304 2.00000000 . 34 0 0 1.340781E154 2.00000000 . 35 0 0 1.340781E154 0.14868120 . 36 0 0 4210.5072254 2.00000000 . 37 0 0 4346.0003696 2.00000000 . 38 0 0 1.340781E154 2.00000000 . 39 0 0 1.340781E154 0.09425822 . 40 0 0 1.340781E154 0.09072392 . 41 0 0 4877.3197427 2.00000000 . 42 0 0 5009.6182614 2.00000000 . 43 0 0 1.340781E154 2.00000000 . 44 0 0 1.340781E154 0.06120650 . 45 0 0 5409.7912614 2.00000000 . 46 0 0 1.340781E154 2.00000000 . 47 0 0 1.340781E154 2.00000000 . 48 0 0 16935.289247 2.00000000 . 49 0 0 1.340781E154 2.00000000 . 50 0 0 1.340781E154 2.00000000 . 51 0 0 1.340781E154 0.99810273 . 52 0 0 1.340781E154 0.81393176 . 53 0 0 1.340781E154 2.00000000 . 54 0 0 3148.7088811 2.00000000 . 55 0 0 3281.2624666 2.00000000 . 56 0 0 1.340781E154 2.00000000 . 57 0 0 3547.6660221 2.00000000 . 58 0 0 3677.9846413 2.00000000 . 59 0 0 3814.0718304 2.00000000 . 60 0 0 1.340781E154 2.00000000 . 61 0 0 1.340781E154 0.14868120 . 62 0 0 4210.5072254 2.00000000 . 63 0 0 4346.0003696 2.00000000 . 64 0 0 1.340781E154 2.00000000 . 65 0 0 1.340781E154 0.09425822 . 66 0 0 1.340781E154 0.09072392 . 67 0 0 4877.3197427 2.00000000 . 68 0 0 5009.6182614 2.00000000 . 69 0 0 1.340781E154 2.00000000 . 70 0 0 1.340781E154 0.06120650 . 71 0 0 5409.7912614 2.00000000 . 72 0 0 1.340781E154 2.00000000 . 73 0 0 1.340781E154 2.00000000 . 74 0 0 16935.289247 2.00000000 . 75 0 0 1.340781E154 2.00000000 . 76 0 0 1.340781E154 2.00000000 . 77 0 0 1.340781E154 0.99810273 . 78 0 0 1.340781E154 0.81393176 . 79 0 0 1.340781E154 2.00000000 . 80 0 0 3148.7088811 2.00000000 . 81 0 0 3281.2624666 2.00000000 . 82 0 0 1.340781E154 2.00000000 . 83 0 0 3547.6660221 2.00000000 . 84 0 0 3677.9846413 2.00000000 . 85 0 0 3814.0718304 2.00000000 . 86 0 0 1.340781E154 2.00000000 . 87 0 0 1.340781E154 0.14868120 . 88 0 0 4210.5072254 2.00000000 . 89 0 0 4346.0003696 2.00000000 . 90 0 0 1.340781E154 2.00000000 . 91 0 0 1.340781E154 0.09425822 . 92 0 0 1.340781E154 0.09072392 . 93 0 0 4877.3197427 2.00000000 . 94 0 0 5009.6182614 2.00000000 . 95 0 0 1.340781E154 2.00000000 . 96 0 0 1.340781E154 0.06120650 . 97 0 0 5409.7912614 2.00000000 . 98 0 0 1.340781E154 2.00000000 . 99 0 0 1.340781E154 2.00000000 . Did not converge. Residual (VC) 1.0328 . ... View more

Many thanks to both of you! I've been extremely busy over the last days so I couldn't work that much on the model. I've thus need to have a closer look at this later. Now the model do not converge....I tried different dependent variable and this is always the same problem: "did not converge". Is it a problem in the modelling or the data? Best, Jérémy ... View more

Yes, sorry Steve. There is indeed a note: AVERTISSEMENT: Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance parameters failed. ... View more

Well, at least I could handle GLIMMIX in the future thanks to your help 😉 The only thing that I do not understand is that it does not show the results. Here is the only result given by SAS. Results of the The GLIMMIX Procedure                                    Number of Observations Read        1013                                    Number of Observations Used        1012                                                Response Profile                                      Ordered                        Total                                        Value    binouverture    Frequency                                            1    0                     614                                            2    1                     398                   The GLIMMIX procedure is modeling the probability that binouverture='1'.                                                  Dimensions                                      R-side Cov. Parameters           1                                      Columns in X                  1315                                      Columns in Z per Subject         0                                      Subjects (Blocks in V)         262                                      Max Obs per Subject             14 proc glimmix data=candidats_ouverture; nloptions maxiter=100; class commune ; model  binouverture (event='1') = EurostatA EurostatC sizelog ENP commune commune*EurostatA commune*EurostatC commune*sizelog commune*ENP       / solution dist=binary link=logit ddfm=kenwardroger  ; /* Addresses individual commune levels, and adds the Kenward-Rogers adjustment for repeated measures designs */   random intercept / subject= commune solution residual; /* Converts to a repeated measures design.  Even though this is a RANDOM statement, the residual option means treat the values within a commune as correlated.  Note also the inclusion of commune and commune by covariate interactions in the model statement. */ lsmeans commune/ilink cl ; /* Outputs the estimates for each level of commune, on both the logit and original scale */ run; ... View more

Actually, just got the answer in the meantime:  after the Iteration History it is mentioned "Did not converge"... I've to mention that I used only 691 observations out of the 1012 (for reasons specific to the subject under investigation). When I integrate all observations, then there is the results which 'stop' after the descriptions of the dimensions of the model. Number of Observations Read 1013 Number of Observations Used 1012       1 0 614 2 1 398     R-side Cov. Parameters 1 Columns in X 2104 Columns in Z per Subject 0 Subjects (Blocks in V) 262 Max Obs per Subject 14 ... View more

I've got my answers regarding the model now, thanks! I submitted the model but it is still charging. Is proc glimmix a time-consuming procedure? By the way, for the line lsmeans commune/ilink ci SAS proposed to change ci by cl. Is that correct in your opinion? I'll keep you updated! Jeremy ... View more

Actually, the observation is the electoral list itself (I did not code individual candidates). So for each of 1012 lists, I coded the information for : -commune (one of the 262 municipalities), -Eurostat (the municipality is either eurostatA, eurostatB, eurostatC. Reference used is EurostatB), -sizelog (log of the population of the municipality), -enp (effective number of parties in the municipality). -binouverture of the list (Yes/no), In other words, for each list from the same municipality, the variables 'commune' (that identifies the municipality), 'EurostatA', EurostatC', 'ENP', 'sizelog' is always the same while the dependent variable 'binouverture' varies for each list. Regarding the key questions you asked: the 262 municipalities is the population. Actually, we coded information for all the 1012 lists presented in all the 262 municipalities at the last local elections. I don't want to infer my results to other case studies but evaluate the impact of independent variables on 'binouverture' for that particular election. To some extent, it's an electoral report, not theory-building. Thus statistical significance is not important because my sample is my population of interest in that paper. The goal is thus to assess the marginal effect of each of the independent variables  'Eurostata' 'Eurostatc' 'Sizelog' 'Enp'  on the dependent variable 'binouverture' but taking into account the fact that lists are presented in 262 municipalities. Does it make clearer? Thanks for your help Steve! Jeremy ... View more

Dear Steve, Thanks very much for your quick response! It already helps although I'm not familiar enough with multi-level regression to be sure that I'm on the good tracks. So, just to be sure, here is my intention: -I've got 1012 observations (=1012 electoral lists). Those 1012 were present in 262 different 'communes'. In other words, in each municipality(=communes), you have a certain amount of electoral lists: sometimes only a couple, sometimes more. I'm seeking to use a logistic regression explaining whether or not these electoral lists include a certain kind of candidates (If yes=1; no=0). My independent variables are : EurostatA EurostatC sizelog ENP as written in the model. Because lists are embedded in distinct communes. I want to include the multi-level structure of my data. For that purpose, I was told to use a fixed effects model. Furthermore, to report my results, I'm asked to present the marginal effects of the independent variables on the dependent variable. Is that possible with the solution you proposed? Many thanks in advance! Jeremy ... View more