David Winsemius
2020-Sep-21 23:20 UTC
[R] Help with the Error Message in R "Error in 1:nchid : result would be too long a vector"
@Rahul; You need to learn to post in plain text and attachments may not be xls or xlsx. They need to be text files. And even if they are comma separated files and text, they still need to be named with a txt extension. I'm the only one who got the xlsx file. I got the error regardless of how many column I omitted, so my gues was possibly incorrect. But I did RTFM. See ?mlogit.datadfi The mlogit.data function is deprecated and you are told to use the dfidx function. Trying that you now get an error saying: " the two indexes don't define unique observations". > sum(duplicated( dfrm[,1:2])) [1] 12 > length(dfrm[,1]) [1] 18 So of your 18 lines in the example file, most of them appear to be duplicated in their first two rows and apparently that is not allowed by dfidx. Caveat: I'm not a user of the mlogit package so I'm just reading the manual and possibly coming up with informed speculation. Please read the Posting Guide. You have been warned. Repeated violations of the policies laid down in that hallowed document will possibly result in postings being ignored. -- David On 9/21/20 2:19 PM, Rahul Chakraborty wrote:> Hello, > > I tried to reduce the size of my dataframe. Now I have 57 columns of > which 29 are already dummy coded. If I run *mldata1<- > mlogit.data(mydata1, shape = "long", alt.var = "Alt_name", choice = > "Choice_binary", id.var = "IND") *it still gives me the same error > message-*?Error in 1:nchid : result would be too long a vector. * > * > * > I will not use all of those variables in one regression model, but I > need those for different model specifications. The Excel file I > created from my survey looks like the attached?file. The main data is > a panel of 516 individuals each answering 9 questions over 3 alternatives. > > Following is the output of the summary of the dataframe. > > summary(mydata1) > ? ? ? IND ? ? ? ? ? ? QES ? ? ? ? STR ? ? ? ? ?ALT_name ? Choice_binary > ?Min. ? : ?1.0 ? Min. ? :1 ? Min. ? : ?101 ? Length:13932 ? Min. ? > :0.0000 > ?1st Qu.:129.8 ? 1st Qu.:3 ? 1st Qu.:12978 ? Class :character ? 1st > Qu.:0.0000 > ?Median :258.5 ? Median :5 ? Median :25855 ? Mode ?:character ? Median > :0.0000 > ?Mean ? :258.5 ? Mean ? :5 ? Mean ? :25855 ?Mean ? :0.3333 > ?3rd Qu.:387.2 ? 3rd Qu.:7 ? 3rd Qu.:38732 ?3rd Qu.:1.0000 > ?Max. ? :516.0 ? Max. ? :9 ? Max. ? :51609 ?Max. ? :1.0000 > ? ? ?Price ? ? ? Refuel_availability Registration_charges ?Running_cost > ?Min. ? : 9.00 ? Min. ? :0.25 ? ? ? ?Min. ? :0.00000 ? ? ?Min. ? :115.0 > ?1st Qu.:10.00 ? 1st Qu.:0.75 ? ? ? ?1st Qu.:0.04000 ? ? ?1st Qu.:192.0 > ?Median :10.00 ? Median :0.90 ? ? ? ?Median :0.06000 ?Median :268.0 > ?Mean ? :10.33 ? Mean ? :0.80 ? ? ? ?Mean ? :0.05333 ? ? ?Mean ? :268.2 > ?3rd Qu.:11.00 ? 3rd Qu.:1.00 ? ? ? ?3rd Qu.:0.08000 ? ? ?3rd Qu.:383.0 > ?Max. ? :12.00 ? Max. ? :1.00 ? ? ? ?Max. ? :0.08000 ? ? ?Max. ? :383.0 > ? Market_share ? ?Friends_share ? ? Refuel_time ? ? ? Emission > ?Min. ? :0.0500 ? Min. ? :0.0000 ? Min. ? : 5.00 ? Min. :0.0000 > ?1st Qu.:0.1500 ? 1st Qu.:0.1500 ? 1st Qu.: 5.00 ? 1st Qu.:0.0000 > ?Median :0.2500 ? Median :0.3000 ? Median : 5.00 ? Median :0.7500 > ?Mean ? :0.3333 ? Mean ? :0.3333 ? Mean ? :13.33 ? Mean :0.5833 > ?3rd Qu.:0.6000 ? 3rd Qu.:0.5500 ? 3rd Qu.:30.00 ? 3rd Qu.:1.0000 > ?Max. ? :0.9000 ? Max. ? :1.0000 ? Max. ? :30.00 ? Max. :1.0000 > ? ? ? Sex ? ? ? ? ? ? ?Age2 ? ? ? ? ? ? Age3 ? ? ? ? ? ? Age4 > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. :0.0000 > ?1st Qu.:1.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 > ?Median :1.0000 ? Median :0.0000 ? Median :0.0000 ? Median :0.0000 > ?Mean ? :0.7791 ? Mean ? :0.4574 ? Mean ? :0.2326 ? Mean :0.1531 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.:0.0000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. :1.0000 > ? ? ?Edu_PG ? ? ? ? ?Edu_Oth ? ? ? ? ?Occu_Pvt ? ? ? ?Occu_Pub > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.000 ? Min. :0.0000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.000 ? 1st Qu.:0.0000 > ?Median :0.0000 ? Median :0.0000 ? Median :0.000 ? Median :0.0000 > ?Mean ? :0.4147 ? Mean ? :0.1841 ? Mean ? :0.376 ? Mean :0.2733 > ?3rd Qu.:1.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.:1.000 ? 3rd Qu.:1.0000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.000 ? Max. :1.0000 > ? ? Occu_SE ? ? ? Location_metro ? Location_majorcity ? ? ?Ahm > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? ? Min. :0.00000 > ?1st Qu.:0.0000 ? 1st Qu.:1.0000 ? 1st Qu.:0.0000 ? ? 1st Qu.:0.00000 > ?Median :0.0000 ? Median :1.0000 ? Median :0.0000 ? ? Median :0.00000 > ?Mean ? :0.2655 ? Mean ? :0.7655 ? Mean ? :0.1453 ? ? Mean :0.04457 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.0000 ? ? 3rd Qu.:0.00000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? ? Max. :1.00000 > ? ? ? Ben ? ? ? ? ? ? ? Chen ? ? ? ? ? ? ?NCR ? ? ? ? ? ? ?Hyd > ?Min. ? :0.00000 ? Min. ? :0.00000 ? Min. ? :0.0000 ? Min. :0.00000 > ?1st Qu.:0.00000 ? 1st Qu.:0.00000 ? 1st Qu.:0.0000 ? 1st Qu.:0.00000 > ?Median :0.00000 ? Median :0.00000 ? Median :0.0000 ? Median :0.00000 > ?Mean ? :0.06977 ? Mean ? :0.04651 ? Mean ? :0.2558 ? Mean :0.03682 > ?3rd Qu.:0.00000 ? 3rd Qu.:0.00000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.00000 > ?Max. ? :1.00000 ? Max. ? :1.00000 ? Max. ? :1.0000 ? Max. :1.00000 > ? ? ? Kol ? ? ? ? ? ? ?Mum ? ? ? ? ? ?MajCity ? ? ? ? ?HH_size > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? : 1.000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.: 3.000 > ?Median :0.0000 ? Median :0.0000 ? Median :0.0000 ? Median : 5.000 > ?Mean ? :0.2016 ? Mean ? :0.1105 ? Mean ? :0.1453 ? Mean ? : 4.463 > ?3rd Qu.:0.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.: 6.000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. :10.000 > ? ? Children ? ? ? ? ? IG2 ? ? ? ? ? ? ?IG3 ? ? ? ? ? ? ?IG4 > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. :0.0000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 > ?Median :1.0000 ? Median :0.0000 ? Median :0.0000 ? Median :0.0000 > ?Mean ? :0.8721 ? Mean ? :0.3818 ? Mean ? :0.4109 ? Mean :0.1841 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.0000 > ?Max. ? :4.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. :1.0000 > ? ? HH_cars ? ? ? PPC_morethan10 ? Daily_travel_medium Daily_travel_long > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? ? ?Min. :0.00000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? ? ?1st Qu.:0.00000 > ?Median :0.0000 ? Median :0.0000 ? Median :0.0000 ? ? ?Median :0.00000 > ?Mean ? :0.4864 ? Mean ? :0.4516 ? Mean ? :0.3702 ? ? ?Mean :0.02713 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? ? ?3rd Qu.:0.00000 > ?Max. ? :3.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? ? ?Max. :1.00000 > ? ? Garage_y ? ? ? ? ? DL_y ? ? ? ? ?Own_accom ?Freerider_tot > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. :2.000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:2.000 > ?Median :1.0000 ? Median :1.0000 ? Median :1.0000 ? Median :2.000 > ?Mean ? :0.7267 ? Mean ? :0.6357 ? Mean ? :0.6647 ? Mean :2.244 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:2.000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. :8.000 > ?Satisfaction_tot Political_view ? WTP_env_tot ?Warmglow_tot ? ? > ?Standout > ?Min. ? : 2.000 ? Min. ? :1.000 ? Min. ? : 2.000 ? Min. ? : 2.00 ? > Min. ? :1.000 > ?1st Qu.: 3.000 ? 1st Qu.:3.000 ? 1st Qu.: 7.000 ? 1st Qu.: 6.00 ? 1st > Qu.:2.000 > ?Median : 4.000 ? Median :3.000 ? Median : 8.000 ? Median : 8.00 ? > Median :3.000 > ?Mean ? : 4.264 ? Mean ? :3.258 ? Mean ? : 8.124 ? Mean ? : 7.61 ? > Mean ? :2.657 > ?3rd Qu.: 5.000 ? 3rd Qu.:4.000 ? 3rd Qu.: 9.000 ? 3rd Qu.: 9.00 ? 3rd > Qu.:3.000 > ?Max. ? :10.000 ? Max. ? :5.000 ? Max. ? :10.000 ? Max. :10.00 ? Max. > ? :5.000 > ?Acceptance_new Climate_perception ? ?Env_pref ?Tech_leader > ?Min. ? :1.0 ? ?Min. ? :1.000 ? ? ?Min. ? :1.000 ? Min. ? :1.0 > ?1st Qu.:2.0 ? ?1st Qu.:4.000 ? ? ?1st Qu.:2.000 ? 1st Qu.:2.0 > ?Median :3.0 ? ?Median :5.000 ? ? ?Median :3.000 ? Median :2.0 > ?Mean ? :2.8 ? ?Mean ? :4.483 ? ? ?Mean ? :3.093 ? Mean ? :2.5 > ?3rd Qu.:4.0 ? ?3rd Qu.:5.000 ? ? ?3rd Qu.:4.000 ? 3rd Qu.:3.0 > ?Max. ? :5.0 ? ?Max. ? :5.000 ? ? ?Max. ? :5.000 ? Max. ? :5.0 > ?Social_motivation_tot ?EV_risk_tot ? ? EV_awareness_tot > ?Min. ? : 3.00 ? ? ? ? Min. ? : 2.000 ? Min. ? : 3.000 > ?1st Qu.: 9.00 ? ? ? ? 1st Qu.: 8.000 ? 1st Qu.: 4.000 > ?Median :11.00 ? ? ? ? Median : 9.000 ? Median : 5.000 > ?Mean ? :10.62 ? ? ? ? Mean ? : 8.661 ? Mean ? : 5.419 > ?3rd Qu.:12.00 ? ? ? ? 3rd Qu.:10.000 ? 3rd Qu.: 6.000 > ?Max. ? :15.00 ? ? ? ? Max. ? :10.000 ? Max. ? :15.000 > > On Tue, Sep 22, 2020 at 2:07 AM Rahul Chakraborty > <chakrarahul at gmail.com <mailto:chakrarahul at gmail.com>> wrote: > > Hello, > > Here is the result of summary(mydata) > > summary(mydata) > ? ? ? IND ? ? ? ? ? ?Block ? ? ? ? ? ?QES ? ? ? ? STR ? ? ? ? ALT > ?Min. ? : ?1.0 ? Min. ? :1.000 ? Min. ? :1 ? Min. ? : ?101 ? Min. > ? :1 > ?1st Qu.:129.8 ? 1st Qu.:1.000 ? 1st Qu.:3 ? 1st Qu.:12978 ? 1st > Qu.:1 > ?Median :258.5 ? Median :2.000 ? Median :5 ? Median :25855 ? > Median :2 > ?Mean ? :258.5 ? Mean ? :2.467 ? Mean ? :5 ? Mean ? :25855 ? Mean > ? :2 > ?3rd Qu.:387.2 ? 3rd Qu.:4.000 ? 3rd Qu.:7 ? 3rd Qu.:38732 ? 3rd > Qu.:3 > ?Max. ? :516.0 ? Max. ? :4.000 ? Max. ? :9 ? Max. ? :51609 ? Max. > ? :3 > ? ?ALT_name ? ? ? ? ? ? ?ASC ? ? ? ? ? ? Choice ?Choice_binary > ?Length:13932 ? ? ? Min. ? :0.0000 ? Min. ? :1.000 ? Min. ? :0.0000 > ?Class :character ? 1st Qu.:0.0000 ? 1st Qu.:1.000 ? 1st Qu.:0.0000 > ?Mode ?:character ? Median :1.0000 ? Median :1.000 Median :0.0000 > ? ? ? ? ? ? ? ? ? ? Mean ? :0.6667 ? Mean ? :1.626 ? Mean ? :0.3333 > ? ? ? ? ? ? ? ? ? ? 3rd Qu.:1.0000 ? 3rd Qu.:2.000 ? 3rd Qu.:1.0000 > ? ? ? ? ? ? ? ? ? ? Max. ? :1.0000 ? Max. ? :3.000 ? Max. ? :1.0000 > ? ? ?Price ? ? ? Refuel_availability Registration_charges > ?Running_cost > ?Min. ? : 9.00 ? Min. ? :0.25 ? ? ? ?Min. ? :0.00000 ?Min. ? :115.0 > ?1st Qu.:10.00 ? 1st Qu.:0.75 ? ? ? ?1st Qu.:0.04000 ?1st Qu.:192.0 > ?Median :10.00 ? Median :0.90 ? ? ? ?Median :0.06000 ?Median :268.0 > ?Mean ? :10.33 ? Mean ? :0.80 ? ? ? ?Mean ? :0.05333 ?Mean ? :268.2 > ?3rd Qu.:11.00 ? 3rd Qu.:1.00 ? ? ? ?3rd Qu.:0.08000 ?3rd Qu.:383.0 > ?Max. ? :12.00 ? Max. ? :1.00 ? ? ? ?Max. ? :0.08000 ?Max. ? :383.0 > ? Market_share ? ?Friends_share ? ? Refuel_time Emission > ?Min. ? :0.0500 ? Min. ? :0.0000 ? Min. ? : 5.00 ? Min. :0.0000 > ?1st Qu.:0.1500 ? 1st Qu.:0.1500 ? 1st Qu.: 5.00 ? 1st Qu.:0.0000 > ?Median :0.2500 ? Median :0.3000 ? Median : 5.00 ? Median :0.7500 > ?Mean ? :0.3333 ? Mean ? :0.3333 ? Mean ? :13.33 ? Mean :0.5833 > ?3rd Qu.:0.6000 ? 3rd Qu.:0.5500 ? 3rd Qu.:30.00 ? 3rd Qu.:1.0000 > ?Max. ? :0.9000 ? Max. ? :1.0000 ? Max. ? :30.00 ? Max. :1.0000 > ? ? ? Sex ? ? ? ? ? ? ?Age2 ? ? ? ? ? ? Age3 Age4 > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. :0.0000 > ?1st Qu.:1.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 > ?Median :1.0000 ? Median :0.0000 ? Median :0.0000 ? Median :0.0000 > ?Mean ? :0.7791 ? Mean ? :0.4574 ? Mean ? :0.2326 ? Mean :0.1531 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.:0.0000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. :1.0000 > ? ? ?Edu_PG ? ? ? ? ?Edu_Oth ? ? ? ? ?Occu_Pvt ?Occu_Pub > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.000 ? Min. :0.0000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.000 ? 1st Qu.:0.0000 > ?Median :0.0000 ? Median :0.0000 ? Median :0.000 ? Median :0.0000 > ?Mean ? :0.4147 ? Mean ? :0.1841 ? Mean ? :0.376 ? Mean :0.2733 > ?3rd Qu.:1.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.:1.000 ? 3rd Qu.:1.0000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.000 ? Max. :1.0000 > ? ? Occu_SE ? ? ? Location_metro ? Location_majorcity ?Ahm > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? ? Min. ? :0.00000 > ?1st Qu.:0.0000 ? 1st Qu.:1.0000 ? 1st Qu.:0.0000 ? ? 1st Qu.:0.00000 > ?Median :0.0000 ? Median :1.0000 ? Median :0.0000 Median :0.00000 > ?Mean ? :0.2655 ? Mean ? :0.7655 ? Mean ? :0.1453 ? ? Mean ? :0.04457 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.0000 ? ? 3rd Qu.:0.00000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? ? Max. ? :1.00000 > ? ? ? Ben ? ? ? ? ? ? ? Chen ? ? ? ? ? ? ?NCR ?Hyd > ?Min. ? :0.00000 ? Min. ? :0.00000 ? Min. ? :0.0000 ? Min. ? :0.00000 > ?1st Qu.:0.00000 ? 1st Qu.:0.00000 ? 1st Qu.:0.0000 ? 1st Qu.:0.00000 > ?Median :0.00000 ? Median :0.00000 ? Median :0.0000 Median :0.00000 > ?Mean ? :0.06977 ? Mean ? :0.04651 ? Mean ? :0.2558 ? Mean ? :0.03682 > ?3rd Qu.:0.00000 ? 3rd Qu.:0.00000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.00000 > ?Max. ? :1.00000 ? Max. ? :1.00000 ? Max. ? :1.0000 ? Max. ? :1.00000 > ? ? ? Kol ? ? ? ? ? ? ?Mum ? ? ? ? ? ?MajCity ?HH_size > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. : 1.000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.: 3.000 > ?Median :0.0000 ? Median :0.0000 ? Median :0.0000 ? Median : 5.000 > ?Mean ? :0.2016 ? Mean ? :0.1105 ? Mean ? :0.1453 ? Mean : 4.463 > ?3rd Qu.:0.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.: 6.000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. :10.000 > ? ? Children ? ? ? ? ? IG2 ? ? ? ? ? ? ?IG3 ?IG4 > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. :0.0000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 > ?Median :1.0000 ? Median :0.0000 ? Median :0.0000 ? Median :0.0000 > ?Mean ? :0.8721 ? Mean ? :0.3818 ? Mean ? :0.4109 ? Mean :0.1841 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:0.0000 > ?Max. ? :4.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. :1.0000 > ? ? HH_cars ? ? ? PPC_morethan10 ? ? ?PPC_gr1 PPC_gr2 > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.000 ? Min. :0.00000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.000 ? 1st Qu.:0.00000 > ?Median :0.0000 ? Median :0.0000 ? Median :0.000 ? Median :0.00000 > ?Mean ? :0.4864 ? Mean ? :0.4516 ? Mean ? :0.405 ? Mean :0.04651 > ?3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.000 ? 3rd Qu.:0.00000 > ?Max. ? :3.0000 ? Max. ? :1.0000 ? Max. ? :1.000 ? Max. :1.00000 > ? ?Body_Sedan ? ? ? ?Body_SUV ? ? ?Daily_travel_medium > Daily_travel_long > ?Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 ?Min. ? :0.00000 > ?1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? ? ?1st > Qu.:0.00000 > ?Median :0.0000 ? Median :0.0000 ? Median :0.0000 ?Median :0.00000 > ?Mean ? :0.3178 ? Mean ? :0.2364 ? Mean ? :0.3702 ?Mean ? :0.02713 > ?3rd Qu.:1.0000 ? 3rd Qu.:0.0000 ? 3rd Qu.:1.0000 ? ? ?3rd > Qu.:0.00000 > ?Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 ?Max. ? :1.00000 > ? ?Long_drive ? ? ? Mode_Carpool ? ? ? ?Mode_PB ?Mode_PV > ?Min. ? :0.00000 ? Min. ? :0.00000 ? Min. ? :0.0000 ? Min. ? :0.0000 > ?1st Qu.:0.00000 ? 1st Qu.:0.00000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 > ?Median :0.00000 ? Median :0.00000 ? Median :0.0000 Median :0.0000 > ?Mean ? :0.03488 ? Mean ? :0.02519 ? Mean ? :0.2907 ? Mean ? :0.4419 > ?3rd Qu.:0.00000 ? 3rd Qu.:0.00000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.0000 > ?Max. ? :1.00000 ? Max. ? :1.00000 ? Max. ? :1.0000 ? Max. ? :1.0000 > ? ? Mode_WRC ? ? ? ? ? Garage_y ? ? ? ? ? DL_y ?Own_accom > ?Min. ? :0.000000 ? Min. ? :0.0000 ? Min. ? :0.0000 ? Min. ? :0.0000 > ?1st Qu.:0.000000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 ? 1st Qu.:0.0000 > ?Median :0.000000 ? Median :1.0000 ? Median :1.0000 Median :1.0000 > ?Mean ? :0.007752 ? Mean ? :0.7267 ? Mean ? :0.6357 ? Mean ? :0.6647 > ?3rd Qu.:0.000000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.0000 ? 3rd Qu.:1.0000 > ?Max. ? :1.000000 ? Max. ? :1.0000 ? Max. ? :1.0000 ? Max. ? :1.0000 > ?Freerider_water_electricity Freerider_tot ? Freerider_avg ? > Satisfaction_tot > ?Min. ? :1.000 ? ? ? ? ? ? ? Min. ? :2.000 ? Min. ? :1.000 ? Min. > ? : 2.000 > ?1st Qu.:2.000 ? ? ? ? ? ? ? 1st Qu.:2.000 ? 1st Qu.:1.000 ? 1st > Qu.: 3.000 > ?Median :3.000 ? ? ? ? ? ? ? Median :2.000 ? Median :1.000 ? > Median : 4.000 > ?Mean ? :3.002 ? ? ? ? ? ? ? Mean ? :2.244 ? Mean ? :1.122 ? Mean > ? : 4.264 > ?3rd Qu.:4.000 ? ? ? ? ? ? ? 3rd Qu.:2.000 ? 3rd Qu.:1.000 ? 3rd > Qu.: 5.000 > ?Max. ? :5.000 ? ? ? ? ? ? ? Max. ? :8.000 ? Max. ? :4.000 ? Max. > ? :10.000 > ?Satisfaction_avg Political_view ?Meet_friends Meet_colleagues > ?Min. ? :1.000 ? ?Min. ? :1.000 ? Length:13932 Length:13932 > ?1st Qu.:1.500 ? ?1st Qu.:3.000 ? Class :character ? Class :character > ?Median :2.000 ? ?Median :3.000 ? Mode ?:character ? Mode ?:character > ?Mean ? :2.132 ? ?Mean ? :3.258 > ?3rd Qu.:2.500 ? ?3rd Qu.:4.000 > ?Max. ? :5.000 ? ?Max. ? :5.000 > ?Meet_relatives ? ? Invite_colleagues ?Invite_friends > Invite_relatives > ?Length:13932 ? ? ? Length:13932 ? ? ? Length:13932 Length:13932 > ?Class :character ? Class :character ? Class :character Class > :character > ?Mode ?:character ? Mode ?:character ? Mode ?:character Mode > ?:character > > > > ?Lending_relatives ?Lending_friends ? ?Lending_colleagues > ?Length:13932 ? ? ? Length:13932 ? ? ? Length:13932 > ?Class :character ? Class :character ? Class :character > ?Mode ?:character ? Mode ?:character ? Mode ?:character > > > > ?Willingness_Purchase_Env_frnd EVuse_pollution ?WTP_env_tot ? ? > ?WTP_env_avg > ?Min. ? :1.000 ? ? ? ? ? ? ? ? Min. ? :1.000 ? Min. ? : 2.000 ? > Min. ? :1.000 > ?1st Qu.:4.000 ? ? ? ? ? ? ? ? 1st Qu.:3.000 ? 1st Qu.: 7.000 ? > 1st Qu.:3.500 > ?Median :4.000 ? ? ? ? ? ? ? ? Median :4.000 ? Median : 8.000 ? > Median :4.000 > ?Mean ? :4.132 ? ? ? ? ? ? ? ? Mean ? :3.992 ? Mean ? : 8.124 ? > Mean ? :4.062 > ?3rd Qu.:5.000 ? ? ? ? ? ? ? ? 3rd Qu.:5.000 ? 3rd Qu.: 9.000 ? > 3rd Qu.:4.500 > ?Max. ? :5.000 ? ? ? ? ? ? ? ? Max. ? :5.000 ? Max. :10.000 ? Max. > ? :5.000 > ?Social_recognition Car_social_status ?Warmglow_tot ?Warmglow_avg > ?Min. ? :1.000 ? ? ?Min. ? :1.00 ? ? ?Min. ? : 2.00 ? Min. ? :1.000 > ?1st Qu.:3.000 ? ? ?1st Qu.:4.00 ? ? ?1st Qu.: 6.00 ? 1st Qu.:3.000 > ?Median :4.000 ? ? ?Median :4.00 ? ? ?Median : 8.00 Median :4.000 > ?Mean ? :3.541 ? ? ?Mean ? :4.07 ? ? ?Mean ? : 7.61 ? Mean ? :3.805 > ?3rd Qu.:4.000 ? ? ?3rd Qu.:5.00 ? ? ?3rd Qu.: 9.00 ? 3rd Qu.:4.500 > ?Max. ? :5.000 ? ? ?Max. ? :5.00 ? ? ?Max. ? :10.00 ? Max. ? :5.000 > ? ? Standout ? ? Acceptance_new Climate_perception ?Env_pref ? ? > ?Tech_leader > ?Min. ? :1.000 ? Min. ? :1.0 ? ?Min. ? :1.000 ? ? ?Min. :1.000 ? > Min. ? :1.0 > ?1st Qu.:2.000 ? 1st Qu.:2.0 ? ?1st Qu.:4.000 ? ? ?1st Qu.:2.000 ? > 1st Qu.:2.0 > ?Median :3.000 ? Median :3.0 ? ?Median :5.000 ? ? ?Median :3.000 ? > Median :2.0 > ?Mean ? :2.657 ? Mean ? :2.8 ? ?Mean ? :4.483 ? ? ?Mean :3.093 ? > Mean ? :2.5 > ?3rd Qu.:3.000 ? 3rd Qu.:4.0 ? ?3rd Qu.:5.000 ? ? ?3rd Qu.:4.000 ? > 3rd Qu.:3.0 > ?Max. ? :5.000 ? Max. ? :5.0 ? ?Max. ? :5.000 ? ? ?Max. :5.000 ? > Max. ? :5.0 > ?Social_motivation_tot Social_motivation_avg Social_motivation_median > ?Min. ? : 3.00 ? ? ? ? Min. ? :1.000 ? ? ? ? Min. ? :1.000 > ?1st Qu.: 9.00 ? ? ? ? 1st Qu.:3.000 ? ? ? ? 1st Qu.:3.000 > ?Median :11.00 ? ? ? ? Median :3.667 ? ? ? ? Median :3.000 > ?Mean ? :10.62 ? ? ? ? Mean ? :3.539 ? ? ? ? Mean ? :3.514 > ?3rd Qu.:12.00 ? ? ? ? 3rd Qu.:4.000 ? ? ? ? 3rd Qu.:4.000 > ?Max. ? :15.00 ? ? ? ? Max. ? :5.000 ? ? ? ? Max. ? :5.000 > ? EV_risk_tot ? ? ?EV_risk_avg ? ? ?EV_price EV_awareness_tot > EV_awareness_avg > ?Min. ? : 2.000 ? Min. ? :1.00 ? Min. ? :1.000 ? Min. ? : 3.000 ? > Min. ? :1.000 > ?1st Qu.: 8.000 ? 1st Qu.:4.00 ? 1st Qu.:1.000 ? 1st Qu.: 4.000 ? > 1st Qu.:1.333 > ?Median : 9.000 ? Median :4.50 ? Median :2.000 ? Median : 5.000 ? > Median :1.667 > ?Mean ? : 8.661 ? Mean ? :4.33 ? Mean ? :2.244 ? Mean ? : 5.419 ? > Mean ? :1.806 > ?3rd Qu.:10.000 ? 3rd Qu.:5.00 ? 3rd Qu.:3.000 ? 3rd Qu.: 6.000 ? > 3rd Qu.:2.000 > ?Max. ? :10.000 ? Max. ? :5.00 ? Max. ? :5.000 ? Max. :15.000 ? > Max. ? :5.000 > ?EV_awareness_median ? ?Lost_env ? ? Investment_trust Lottery1 > ?Min. ? :1.000 ? ? ? Min. ? :1.000 ? Min. ? : ? ? 0 Length:13932 > ?1st Qu.:1.000 ? ? ? 1st Qu.:5.000 ? 1st Qu.: ? ? 0 Class :character > ?Median :2.000 ? ? ? Median :5.000 ? Median : ? ? 0 ? Mode > ?:character > ?Mean ? :1.806 ? ? ? Mean ? :4.913 ? Mean ? : ?1345 > ?3rd Qu.:2.000 ? ? ? 3rd Qu.:5.000 ? 3rd Qu.: ? ? 0 > ?Max. ? :5.000 ? ? ? Max. ? :5.000 ? Max. ? :100000 > ? ? Time1 ? ? ? ? ? ? Lottery2 ? ? ? ? ? ?Time2 > ?Length:13932 ? ? ? Length:13932 ? ? ? Length:13932 > ?Class :character ? Class :character ? Class :character > ?Mode ?:character ? Mode ?:character ? Mode ?:character > > > > Yes, I have many Likert items and many dummy variables. How do I > solve this issue? > > Best regards, > > On Tue, Sep 22, 2020 at 1:45 AM David Winsemius > <dwinsemius at comcast.net <mailto:dwinsemius at comcast.net>> wrote: > > If you had included output of summary(mydata) we might be more > capable > of giving a fact-based answer but I'm guessing that you have a > lot of > catagorical variables with multiple levels and some sort of > combinatoric > explosion is resulting in too many levels of a constructed factor. > > > -- > > David. > > On 9/21/20 12:55 PM, Rahul Chakraborty wrote: > > Hello everyone, > > > > I am using *mlogit* to analyse my choice experiment data. I > have *3 > > alternatives* for each individual and for each individual I > have *9 > > questions*. I have a response from *516 individuals*. So it > is a panel of > > 9*516 observations. I have arranged the data in long format > (it contains > > 100 columns indicating different variables and identifiers). > > > > In mlogit I tried the following command--- > > > > *mldata<- mlogit.data(mydata, shape = "long", alt.var > "Alt_name", choice > > = "Choice_binary", id.var = "IND")* > > > > It is giving me the following error message- Error in > 1:nchid : result > > would be too long a vector > > > > Could you please help me with this? I don't think it is too > big a data 100 > > ROWS*13932 columns. I faced no issue in Excel. I am stuck > due to this issue. > > Thanks in advance. > > > > -- Best Regards, > > Rahul Chakraborty > > Research Fellow > > National Institute of Public Finance and Policy > > New Delhi- 110067 > > > >? ? ? ?[[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help at r-project.org <mailto:R-help at r-project.org> mailing > list -- To UNSUBSCRIBE and more, see > > 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. > > > > -- > Rahul Chakraborty > Research Fellow > National Institute of Public Finance and Policy > New Delhi- 110067 > > > > -- > Rahul Chakraborty > Research Fellow > National Institute of Public Finance and Policy > New Delhi- 110067
Rahul Chakraborty
2020-Sep-22 05:20 UTC
[R] Help with the Error Message in R "Error in 1:nchid : result would be too long a vector"
Hello David and everyone, I am really sorry for not abiding by the specific guidelines in my prior communications. I tried to convert the present email in plain text format (at least it is showing me so in my gmail client). I have also converted the xlsx file into a csv format with .txt extension. So, my problem is I need to run panel mixed logit regression for a choice model. There are 3 alternatives, 9 questions for each individual and 516 individuals in data. I have created a csv file in long format from the survey questionnaire. Apart from the alternative specific variables I have many individual specific variables and most of these are dummies (dummy coded). I will use subsets of these in my alternative model specifications. So, in my data I have 100 columns with 13932 rows (3*9*516). After reading the csv file and creating a dataframe 'mydata' I used the following command for mlogit. mldata1<- mlogit.data(mydata, shape = "long", alt.var = "Alt_name", choice = "Choice_binary", id.var = "IND") It gives me the same error message- Error in 1:nchid : result would be too long a vector. The attached file (csv file with .txt extension) is an example of 2 individuals each with 3 questions. I have also reduced the number of columns to 57. Now, there are 18 rows. But still if I use the same command on my new data I get the same error message. Can anyone please help me out with this? Because of this error I am stuck at the dataframe level. Thanks in advance. Regards, Rahul Chakraborty On Tue, Sep 22, 2020 at 4:50 AM David Winsemius <dwinsemius at comcast.net> wrote:> > @Rahul; > > > You need to learn to post in plain text and attachments may not be xls > or xlsx. They need to be text files. And even if they are comma > separated files and text, they still need to be named with a txt extension. > > > I'm the only one who got the xlsx file. I got the error regardless of > how many column I omitted, so my gues was possibly incorrect. But I did > RTFM. See ?mlogit.datadfi The mlogit.data function is deprecated and you > are told to use the dfidx function. Trying that you now get an error > saying: " the two indexes don't define unique observations". > > > > sum(duplicated( dfrm[,1:2])) > [1] 12 > > length(dfrm[,1]) > [1] 18 > > So of your 18 lines in the example file, most of them appear to be > duplicated in their first two rows and apparently that is not allowed by > dfidx. > > > Caveat: I'm not a user of the mlogit package so I'm just reading the > manual and possibly coming up with informed speculation. > > Please read the Posting Guide. You have been warned. Repeated violations > of the policies laid down in that hallowed document will possibly result > in postings being ignored. >-------------- next part -------------- An embedded and charset-unspecified text was scrubbed... Name: example2.txt URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20200922/52cb1a83/attachment.txt>
David Winsemius
2020-Sep-22 15:15 UTC
[R] Help with the Error Message in R "Error in 1:nchid : result would be too long a vector"
You were told two things about your code: 1) mlogit.data is deprecated by the package authors, so use dfidx. 2) dfidx does not allow duplicate ids in the first two columns. Which one of those are you asserting is not accurate? -- David. On 9/21/20 10:20 PM, Rahul Chakraborty wrote:> Hello David and everyone, > > I am really sorry for not abiding by the specific guidelines in my > prior communications. I tried to convert the present email in plain > text format (at least it is showing me so in my gmail client). I have > also converted the xlsx file into a csv format with .txt extension. > > So, my problem is I need to run panel mixed logit regression for a > choice model. There are 3 alternatives, 9 questions for each > individual and 516 individuals in data. I have created a csv file in > long format from the survey questionnaire. Apart from the alternative > specific variables I have many individual specific variables and most > of these are dummies (dummy coded). I will use subsets of these in my > alternative model specifications. So, in my data I have 100 columns > with 13932 rows (3*9*516). After reading the csv file and creating a > dataframe 'mydata' I used the following command for mlogit. > > mldata1<- mlogit.data(mydata, shape = "long", alt.var = "Alt_name", > choice = "Choice_binary", id.var = "IND") > > It gives me the same error message- Error in 1:nchid : result would be > too long a vector. > > The attached file (csv file with .txt extension) is an example of 2 > individuals each with 3 questions. I have also reduced the number of > columns to 57. Now, there are 18 rows. But still if I use the same > command on my new data I get the same error message. Can anyone please > help me out with this? Because of this error I am stuck at the > dataframe level. > > > Thanks in advance. > > > Regards, > Rahul Chakraborty > > On Tue, Sep 22, 2020 at 4:50 AM David Winsemius <dwinsemius at comcast.net> wrote: >> @Rahul; >> >> >> You need to learn to post in plain text and attachments may not be xls >> or xlsx. They need to be text files. And even if they are comma >> separated files and text, they still need to be named with a txt extension. >> >> >> I'm the only one who got the xlsx file. I got the error regardless of >> how many column I omitted, so my gues was possibly incorrect. But I did >> RTFM. See ?mlogit.datadfi The mlogit.data function is deprecated and you >> are told to use the dfidx function. Trying that you now get an error >> saying: " the two indexes don't define unique observations". >> >> >> > sum(duplicated( dfrm[,1:2])) >> [1] 12 >> > length(dfrm[,1]) >> [1] 18 >> >> So of your 18 lines in the example file, most of them appear to be >> duplicated in their first two rows and apparently that is not allowed by >> dfidx. >> >> >> Caveat: I'm not a user of the mlogit package so I'm just reading the >> manual and possibly coming up with informed speculation. >> >> Please read the Posting Guide. You have been warned. Repeated violations >> of the policies laid down in that hallowed document will possibly result >> in postings being ignored. >>