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
Help with the Error Message in R "Error in 1:nchid : result would be too long a vector"
9 messages · Rahul Chakraborty, David Winsemius, Rui Barradas
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 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.
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>
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 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 [[alternative HTML version deleted]]
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>
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>
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 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; 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
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.
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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. >>
Hello,
I apologize if the rest of quotes prior to David's email are missing,
for some reason today my mail client is not including them.
As for the question, there are two other problems:
1) Alt_name is misspelled, it should be ALT_name;
2) the data is in wide, not long, format.
A 3rd, problem is that in ?dfidx it says
alt.var
the name of the variable that contains the alternative index (for a long
data.frame only) or the name under which the alternative index will be
stored (the default name is alt)
So if shape = "wide", alt.var is not needed.
But I am not a user of package mlogit, I'm just guessing.
The following seems to fix it (it doesn't throw errors).
mldata1 <- dfidx(mydata, shape = "wide",
#alt.var = "ALT_name",
choice = "Choice_binary",
id.var = "IND")
Hope this helps,
Rui Barradas
?s 16:15 de 22/09/20, David Winsemius escreveu:
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,
My apologies with the first one. I was checking different tutorials on
mlogit where they were using mlogit.data, so I ended up using it.
I am not getting what you are saying by the "duplicates in first two
columns". See, my first column is IND which identifies my individuals,
second column is QES which identifies the question number each
individual faces, 3rd column is a stratification code that can be
ignored. Columns 6-13 are alternative specific variables and rest are
individual specific. So 1st 3 rows indicate 1st question faced by 1st
individual containing 3 alternatives, and so on. So, I have already
arranged the data in long format. Here, I could not get what the
"duplicate in first two columns" mean.
And I am really sorry that there was an error in my code as Rui has
pointed out. The correct code is
mldata1 <- dfidx(mydata, shape = "long",
alt.var = "ALT_name",
choice = "Choice_binary",
id.var = "IND")
It still shows the error- "the two indexes don't define unique observations"
It would be really helpful if you kindly help.
Regards,
On Tue, Sep 22, 2020 at 8:46 PM David Winsemius <dwinsemius at comcast.net> wrote:
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.
Rahul Chakraborty Research Fellow National Institute of Public Finance and Policy New Delhi- 110067