Hi Matt, I am trying to perform MI with Amelia (R and Amelia are updated to the latest versions). After setting up the variables I received the following error: Error in if (sum(non.vary == 0)) { : argument is not interpretable as logical I searched online but was unable to find a way to correct this error. I have actually relatively few missing variables. any idea what I can do to get rid of the error. Your response would be highly appreciated! Guy # define variables' type ords = c("post_elections", "prev_elections", "election_year") noms = c("type", "region", "split_cat", "splinter", "mother", "breakup", "home_winner", "tribe_winner", "support_winner", "opposed", "irregularity", "home_loser", "tribe_loser", "support_loser", "politics_loser", "mobilize_loser", "ethnic_min", "ethnicity_dec_major", "dom_ethnic02", "turn_majority", "contig_border", "TYPE_2") idvars = c("county_name", "district_11", "regions_67", "district_80", "district_91", "district_96", "district_01", "district_02", "district_06", "district_07") lags = c("split_cat", "mshare_county") logs = c("area", "pop") lgstc = c("mshare", "mshare_county", "cshare_county", "elf02", "eduattain", "literacy", "employee", "pio", "nag_pio", "nag_employee", "pl02", "pi02", "perclit91", "percillit91") a.out <- amelia(DistrictFormation, m = 5, ts = "wave", cs = "id", polytime = 2, intercs = TRUE, idvars=idvars, ords=ords, noms=noms, logs=logs, lgstc=lgstc) Error in if (sum(non.vary == 0)) { : argument is not interpretable as logical 'data.frame': 474 obs. of 109 variables: $ id : num 1 1 1 2 2 2 3 3 3 4 ... $ wave : int 1 2 3 1 2 3 1 2 3 1 ... $ county_name : chr "BUJUMBA" "BUJUMBA" "BUJUMBA" "KYAMUSWA" ... $ county_id02 : num 1011 1011 1011 1012 1012 ... $ region : Factor w/ 4 levels "CENTRAL","EASTERN",..: 1 1 1 1 1 1 1 1 1 1 ... $ district_11 : chr "KALANGALA" "KALANGALA" "KALANGALA" "KALANGALA" ... $ regions_67 : chr "MASAKA" "MASAKA" "MASAKA" "MASAKA" ... $ district_80 : chr "MASAKA" "MASAKA" "MASAKA" "MASAKA" ... $ district_91 : chr "KALANGALA" "KALANGALA" "KALANGALA" "KALANGALA" ... $ district_96 : chr "KALANGALA" "KALANGALA" "KALANGALA" "KALANGALA" ... $ district_01 : chr "KALANGALA" "KALANGALA" "KALANGALA" "KALANGALA" ... $ district_02 : chr "KALANGALA" "KALANGALA" "KALANGALA" "KALANGALA" ... $ district_06 : chr "KALANGALA" "KALANGALA" "KALANGALA" "KALANGALA" ... $ district_07 : chr "KALANGALA" "KALANGALA" "KALANGALA" "KALANGALA" ... $ ndist80 : num 18 18 18 18 18 18 18 18 18 18 ... $ ndist91 : num 39 39 39 39 39 39 39 39 39 39 ... $ ndist02 : num 56 56 56 56 56 56 56 56 56 56 ... $ ndist06 : num 72 72 72 72 72 72 72 72 72 72 ... $ ndist07 : num 80 80 80 80 80 80 80 80 80 80 ... $ ndist11 : num 109 109 109 109 109 109 109 109 109 109 ... $ n1991 : num 2 2 2 2 2 2 1 1 1 4 ... $ n1996 : num 2 2 2 2 2 2 1 1 1 4 ... $ n2001 : num 2 2 2 2 2 2 1 1 1 3 ... $ n2006 : num 2 2 2 2 2 2 1 1 1 2 ... $ ncounties : num 2 2 2 2 2 2 1 1 1 4 ... $ type : chr "COUNTY" "COUNTY" "COUNTY" "COUNTY" ... $ post_elections : num 2001 2006 2011 2001 2006 ... $ prev_elections : num 1996 2001 2006 1996 2001 ... $ split_cat : Factor w/ 3 levels "No Split","Splinter",..: 1 1 1 1 1 1 1 1 1 3 ... $ splinter : num 0 0 0 0 0 0 0 0 0 0 ... $ mother : num 0 0 0 0 0 0 0 0 0 1 ... $ breakup : num 0 0 0 0 0 0 0 0 0 1 ... $ election_year : int 2001 2006 2011 2001 2006 2011 2001 2006 2011 2001 ... $ mshare_county : num 0.667 0.539 0.639 0.677 0.535 ... $ cshare_county : num 0.317 0.403 0.296 0.304 0.421 ... $ mmargin_county : num 0.343 0.137 0.342 0.365 0.115 ... $ mshare : num 0.693 0.585 0.689 0.693 0.585 ... $ mshare_cat : num 0.695 0.622 0.706 0.695 0.622 ... $ MP_nrm_share : num -999 1 1 -999 0 1 -999 1 1 -999 ... $ home_winner : int 0 0 0 1 1 1 1 1 1 0 ... $ tribe_winner : Factor w/ 37 levels "ACHOLI","ALUR",..: 6 6 6 6 6 6 6 6 6 6 ... $ support_winner : int 0 0 1 1 1 1 1 1 1 1 ... $ opposed : int 1 1 0 1 1 0 0 1 1 1 ... $ irregularity : int 0 0 0 0 0 0 0 0 0 0 ... $ home_loser : int 1 1 0 0 0 0 1 1 1 0 ... $ tribe_loser : Factor w/ 37 levels "ACHOLI","ALUR",..: 6 6 6 6 6 6 6 13 13 6 ... $ support_loser : int 1 1 0 0 0 0 0 0 0 0 ... $ politics_loser : int 0 1 0 0 1 0 0 1 0 1 ... $ mobilize_loser : int 0 0 0 0 0 0 0 0 0 1 ... $ county_dec_share : num 0.6 0.4 0.6 0.4 0.6 ... $ ethnic_min : num 0 0 0 0 0 0 0 0 0 0 ... $ ethnicity_dec_major : Factor w/ 37 levels "ACHOLI","ALUR",..: 6 6 6 6 6 6 6 6 6 6 ... $ control_dec_ethnicity: num 1 1 0.8 1 1 ... $ pop : num 17271 17271 17271 17428 17428 ... $ dom_ethnic02 : Factor w/ 37 levels "ACHOLI","ALUR",..: 6 6 6 6 6 6 6 6 6 6 ... $ share_dom_ethnic02 : num 67.7 67.7 67.7 60.2 60.2 ... $ turn_majority : num 0 0 0 0 0 0 0 0 0 0 ... $ pop02 : int 17271 17271 17271 17428 17428 17428 229297 229297 229297 137199 ... $ elf02 : num 0.528 0.528 0.528 0.624 0.624 ... $ schools_tot_pop : num 0.811 0.811 0.811 0.689 0.689 ... $ schools_tot : num 14 14 14 12 12 12 260 260 260 170 ... $ other_tot_pop : num 0.116 0.116 0.116 0 0 ... $ other_tot : num 2 2 2 0 0 0 1 1 1 1 ... $ primary_tot_pop : num 0.579 0.579 0.579 0.631 0.631 ... $ primary_tot : num 10 10 10 11 11 11 229 229 229 142 ... $ sec_tot_pop : num 0.1158 0.1158 0.1158 0.0574 0.0574 ... $ sec_tot : num 2 2 2 1 1 1 30 30 30 27 ... $ gov_primary_pop : num 0.521 0.521 0.521 0.459 0.459 ... $ gov_primary : num 9 9 9 8 8 8 188 188 188 81 ... $ gov_sec_pop : num 0.1158 0.1158 0.1158 0.0574 0.0574 ... $ gov_sec : num 2 2 2 1 1 1 7 7 7 6 ... $ eduattain : num 0.222 0.222 0.222 0.241 0.241 ... $ literacy : num 0.818 0.818 0.818 0.826 0.826 ... $ pl02 : num 0.12 0.12 0.12 0.04 0.04 ... $ se_po02 : num 1.89 1.89 1.89 0.93 0.93 ... $ pg02 : int 2 2 2 1 1 1 10 10 10 9 ... $ se_pg02 : num 0.48 0.48 0.48 0.2 0.2 ... $ pi02 : num 0.0033 0.0033 0.0033 0.0036 0.0036 ... $ sepi02 : num 1.14 1.14 1.14 1.63 1.63 ... $ npoor02 : int 2014 2014 2014 772 772 772 79497 79497 79497 45674 ... $ se_npoor02 : int 326 326 326 162 162 162 3164 3164 3164 1962 ... $ elf91 : num 0.43 0.43 0.43 0.458 0.458 ... $ area : num 2356 2356 2356 6672 6672 ... $ pop91 : int 7335 7335 7335 6872 6872 6872 132711 132711 132711 105562 ... $ pl91 : num 0.365 0.365 0.365 0.261 0.261 ... $ pg91 : num 10.1 10.1 10.1 7.1 7.1 ... $ gini91 : num 29.9 29.9 29.9 32.6 32.6 ... $ popdensity91 : num 3.11 3.11 3.11 1.03 1.03 ... $ censuspop91 : int 9192 9192 9192 7179 7179 7179 141607 141607 141607 108098 ... $ perclit91 : num 0.711 0.711 0.711 0.729 0.729 ... $ percillit91 : num 0.289 0.289 0.289 0.271 0.271 ... $ distance_border : num 81855 81855 81855 94097 94097 ... $ contig_border : int 0 0 0 0 0 0 0 0 0 0 ... $ distance_kampala : num 86.6 86.6 86.6 80.2 80.2 ... $ pio : num 0.668 0.668 0.668 0.704 0.704 ... $ employee : num 0.358 0.358 0.358 0.393 0.393 ... $ nag_pio : num 0.249 0.249 0.249 0.15 0.15 ... $ nag_employee : num 0.1628 0.1628 0.1628 0.0701 0.0701 ... $ pop_county18 : num 10257 10257 10257 12185 12185 ... [list output truncated] -- ___________________________ Guy Grossman Postdoctoral Research Associate Princeton University, NJ o: 212-854-4985 m: 917-664-6946 www.guygrossman.com