categorical variable

[Michael Okoro]

Hi, I am a beginner with stata, please I need help, is it possible to run my categorical variables (outcomes) in groups. example
I have an outcome that has 3 groups. a b c generalized as A. (for logistic regression )
A
a (
b (
c (

[Clyde Schechter]

I'm not sure I understand your question. The outcome variable in a logistic regression must have exactly two groups: 0 and 1 (or 0 and non-zero, more generally). A three-group variable cannot be the outcome in logistic regression. There are other regression approaches available for that. Perhaps if you explained more about what this variable, the other data you are working with, and the kinds of questions you are trying to answer, some more specific, positive advice would be possible.

[Weiwen Ng]

Your question is unclear. At the very least, giving the names of the variables in question and their values would help - I think you're saying that the values are a, b, and c, but it's not clear what "generalized as A" means.

It does sound like you have one multiple categorical variable. If so, are you asking if you can change it to a series of 3 binary outcomes? The answer is yes, you can. This isn't the theoretically correct way to do it, but multinomial logistic regression is not really a good model for beginners to learn on, either. You'd type something like

gen outcome_a = outcome == "a"
*or
gen outcome_a = outcome == 1

Depending on how the outcome variable is coded, anyway. Be aware that computers distinguish between string and numeric variables. Stata can only handle numeric variables as independent or dependent variables. The first line of code will actually generate a numeric variable. If your original variable is string, you have to enclose anything in quotes.

[Michael Okoro]

Thank you @ Clyde and @Ng. let me explain more. I want to find the association between VI and participation. VI - exposure and participation - outcome.
participation has domains and I wish to find the associations in each of those domains of participation. I have data from activities of daily living (answered yes/no or 1 / 0) and I have to group the different ADLs to the appropriate subgroups of participation (or domains of participation). so is there a way stata can help run this generally?

PARTICIPATION

A. Domestic life
a) lighthouse activities	--------------------------------------------------------
b) heavy house activities

c)

[Clyde Schechter]

OK, the context is now much clearer. But you still have not shown an example of your data, so it is not possible to give specific advice how to proceed. Please post back with example data using the -dataex- command. If you are running version 16 or a fully updated version 15.1 or 14.2, -dataex- is already part of your official Stata installation. If not, run -ssc install dataex- to get it. Either way, run -help dataex- to read the simple instructions for using it. -dataex- will save you time; it is easier and quicker than typing out tables. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data.



When asking for help with code, always show example data. When showing example data, always use -dataex-.

[Michael Okoro]

Thank you.
it seems not working getting exactly an example of my data with -dataex-,

[Clyde Schechter]

What does "not working" mean. Just open your Stata data set and run -dataex-. Then copy the output here. If -dataex- is giving you an error message, copy that here, and we can troubleshoot.

[Michael Okoro]

Ok, thank you. got it

* Example generated by -dataex-. To install: ssc install dataex
clear
input str18 make int(price mpg rep78) float headroom int(trunk weight length turn displacement) float gear_ratio byte foreign
"AMC Concord"        4099 22 3 2.5 11 2930 186 40 121 3.58 0
"AMC Pacer"          4749 17 3   3 11 3350 173 40 258 2.53 0
"AMC Spirit"         3799 22 .   3 12 2640 168 35 121 3.08 0
"Buick Century"      4816 20 3 4.5 16 3250 196 40 196 2.93 0
"Buick Electra"      7827 15 4   4 20 4080 222 43 350 2.41 0
"Buick LeSabre"      5788 18 3   4 21 3670 218 43 231 2.73 0
"Buick Opel"         4453 26 .   3 10 2230 170 34 304 2.87 0
"Buick Regal"        5189 20 3   2 16 3280 200 42 196 2.93 0
"Buick Riviera"     10372 16 3 3.5 17 3880 207 43 231 2.93 0
"Buick Skylark"      4082 19 3 3.5 13 3400 200 42 231 3.08 0
"Cad. Deville"      11385 14 3   4 20 4330 221 44 425 2.28 0
"Cad. Eldorado"     14500 14 2 3.5 16 3900 204 43 350 2.19 0
"Cad. Seville"      15906 21 3   3 13 4290 204 45 350 2.24 0
"Chev. Chevette"     3299 29 3 2.5  9 2110 163 34 231 2.93 0
"Chev. Impala"       5705 16 4   4 20 3690 212 43 250 2.56 0
"Chev. Malibu"       4504 22 3 3.5 17 3180 193 31 200 2.73 0
"Chev. Monte Carlo"  5104 22 2   2 16 3220 200 41 200 2.73 0
"Chev. Monza"        3667 24 2   2  7 2750 179 40 151 2.73 0
"Chev. Nova"         3955 19 3 3.5 13 3430 197 43 250 2.56 0
"Dodge Colt"         3984 30 5   2  8 2120 163 35  98 3.54 0
"Dodge Diplomat"     4010 18 2   4 17 3600 206 46 318 2.47 0
"Dodge Magnum"       5886 16 2   4 17 3600 206 46 318 2.47 0
"Dodge St. Regis"    6342 17 2 4.5 21 3740 220 46 225 2.94 0
"Ford Fiesta"        4389 28 4 1.5  9 1800 147 33  98 3.15 0
"Ford Mustang"       4187 21 3   2 10 2650 179 43 140 3.08 0
"Linc. Continental" 11497 12 3 3.5 22 4840 233 51 400 2.47 0
"Linc. Mark V"      13594 12 3 2.5 18 4720 230 48 400 2.47 0
"Linc. Versailles"  13466 14 3 3.5 15 3830 201 41 302 2.47 0
"Merc. Bobcat"       3829 22 4   3  9 2580 169 39 140 2.73 0
"Merc. Cougar"       5379 14 4 3.5 16 4060 221 48 302 2.75 0
"Merc. Marquis"      6165 15 3 3.5 23 3720 212 44 302 2.26 0
"Merc. Monarch"      4516 18 3   3 15 3370 198 41 250 2.43 0
"Merc. XR-7"         6303 14 4   3 16 4130 217 45 302 2.75 0
"Merc. Zephyr"       3291 20 3 3.5 17 2830 195 43 140 3.08 0
"Olds 98"            8814 21 4   4 20 4060 220 43 350 2.41 0
"Olds Cutl Supr"     5172 19 3   2 16 3310 198 42 231 2.93 0
"Olds Cutlass"       4733 19 3 4.5 16 3300 198 42 231 2.93 0
"Olds Delta 88"      4890 18 4   4 20 3690 218 42 231 2.73 0
"Olds Omega"         4181 19 3 4.5 14 3370 200 43 231 3.08 0
"Olds Starfire"      4195 24 1   2 10 2730 180 40 151 2.73 0
"Olds Toronado"     10371 16 3 3.5 17 4030 206 43 350 2.41 0
"Plym. Arrow"        4647 28 3   2 11 3260 170 37 156 3.05 0
"Plym. Champ"        4425 34 5 2.5 11 1800 157 37  86 2.97 0
"Plym. Horizon"      4482 25 3   4 17 2200 165 36 105 3.37 0
"Plym. Sapporo"      6486 26 . 1.5  8 2520 182 38 119 3.54 0
"Plym. Volare"       4060 18 2   5 16 3330 201 44 225 3.23 0
"Pont. Catalina"     5798 18 4   4 20 3700 214 42 231 2.73 0
"Pont. Firebird"     4934 18 1 1.5  7 3470 198 42 231 3.08 0
"Pont. Grand Prix"   5222 19 3   2 16 3210 201 45 231 2.93 0
"Pont. Le Mans"      4723 19 3 3.5 17 3200 199 40 231 2.93 0
"Pont. Phoenix"      4424 19 . 3.5 13 3420 203 43 231 3.08 0
"Pont. Sunbird"      4172 24 2   2  7 2690 179 41 151 2.73 0
"Audi 5000"          9690 17 5   3 15 2830 189 37 131  3.2 1
"Audi Fox"           6295 23 3 2.5 11 2070 174 36  97  3.7 1
"BMW 320i"           9735 25 4 2.5 12 2650 177 34 121 3.64 1
"Datsun 200"         6229 23 4 1.5  6 2370 170 35 119 3.89 1
"Datsun 210"         4589 35 5   2  8 2020 165 32  85  3.7 1
"Datsun 510"         5079 24 4 2.5  8 2280 170 34 119 3.54 1
"Datsun 810"         8129 21 4 2.5  8 2750 184 38 146 3.55 1
"Fiat Strada"        4296 21 3 2.5 16 2130 161 36 105 3.37 1
"Honda Accord"       5799 25 5   3 10 2240 172 36 107 3.05 1
"Honda Civic"        4499 28 4 2.5  5 1760 149 34  91  3.3 1
"Mazda GLC"          3995 30 4 3.5 11 1980 154 33  86 3.73 1
"Peugeot 604"       12990 14 . 3.5 14 3420 192 38 163 3.58 1
"Renault Le Car"     3895 26 3   3 10 1830 142 34  79 3.72 1
"Subaru"             3798 35 5 2.5 11 2050 164 36  97 3.81 1
"Toyota Celica"      5899 18 5 2.5 14 2410 174 36 134 3.06 1
"Toyota Corolla"     3748 31 5   3  9 2200 165 35  97 3.21 1
"Toyota Corona"      5719 18 5   2 11 2670 175 36 134 3.05 1
"VW Dasher"          7140 23 4 2.5 12 2160 172 36  97 3.74 1
"VW Diesel"          5397 41 5   3 15 2040 155 35  90 3.78 1
"VW Rabbit"          4697 25 4   3 15 1930 155 35  89 3.78 1
"VW Scirocco"        6850 25 4   2 16 1990 156 36  97 3.78 1
"Volvo 260"         11995 17 5 2.5 14 3170 193 37 163 2.98 1
end
label values foreign origin
label def origin 0 "Domestic", modify
label def origin 1 "Foreign", modify

[Clyde Schechter]

You posted the built-in Stata auto.dta. We need to see an example from your data set, the one you were talking about in #1.

[Michael Okoro]

ok

[Michael Okoro]

I want to say thank you @clyde, my supervisor made some changes. helped me. thank you, everyone

[Michael Okoro]

I want to say thank you @clyde, my supervisor made some changes that helped me. thank you, everyone