Not able to extract values from a r(table) matrix to add to a table as new columns

Kehinde Atoloye

Join Date: Nov 2021
Posts: 107

Not able to extract values from a r(table) matrix to add to a table as new columns

01 Apr 2024, 21:36

I want to extract coefficients and pvalues from the second table command and append to the first table command as columns by the right side of the tabulated values but my efforts have been to no avail. Extracting the values firstly is not working as only one value is being extracted instead. Your assistance is much appreciated. Thanks.

Code:

table (p800ind) (p203 studyphase), statistic(freq) statistic(percent, across(p800ind)) totals(p203#studyphase) nformat(%5.1f) sformat("(%s%%)" freq percent)
collect layout (p800ind) (p203#studyphase#result)

table (result) , command( exp(r(table)[1,2]) r(table)[4,2]:xtgee p800ind i.did i.p203 i.time, family(binomial 1) link(logit) corr(independent) robust eform) nformat(%5.2f)

Code:

* Example generated by -dataex-. For more info, type help dataex
clear
input float p800ind byte(did p203) float time byte studyphase
0 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
0 0 1 0 1
1 0 1 0 1
0 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
0 0 1 0 1
1 0 1 0 1
1 0 1 0 1
0 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
0 1 1 1 2
1 1 1 1 2
1 1 1 1 2
0 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
0 1 1 1 2
0 1 1 1 2
1 1 1 1 2
1 1 1 1 2
0 0 1 0 1
1 0 1 0 1
0 0 1 0 1
1 0 1 0 1
0 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
0 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
1 1 1 1 2
0 0 1 0 1
0 0 1 0 1
0 0 1 0 1
0 0 1 0 1
0 0 1 0 1
0 0 1 0 1
0 0 1 0 1
0 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
0 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
1 0 1 0 1
0 1 1 1 2
0 1 1 1 2
1 1 1 1 2
0 1 1 1 2
0 1 1 1 2
0 1 1 1 2
0 1 1 1 2
end
label values p800ind p800ind
label def p800ind 0 "No", modify
label def p800ind 1 "Yes", modify
label values p203 p203
label def p203 1 "Intervention", modify
label values time time
label def time 0 "Endline", modify
label def time 1 "Baseline", modify
label values studyphase studyphase
label def studyphase 1 "Baseline", modify
label def studyphase 2 "Endline", modify

Tags: None

Jeff Pitblado (StataCorp)

StataCorp Employee

Join Date: Mar 2014
Posts: 658

06 Apr 2024, 13:02

Thank you for the data and code example.

You do not identify the panel variable, I'm assuming it is studyphase.

1. You are better off using the collect: prefix with xtgee instead of calling table again. Using collect: prefix this way will put the collected results from xtgee into the collection created by your first table command. Otherwise, the second call to table replaces the collection created by the first call to table.

2. I'm having trouble understanding how you want the xtgee results to be arranged in your table. Please provide an example of the table you want using plain text. If you are trying to get something like the following

Code:

---------------------------------------------------------------------------
        |                      p203                             did
        |                  Intervention                          1
        |                   studyphase                  Odds ratio  p-value
        |        Baseline                Endline      
        |  Frequency    Percent   Frequency    Percent
--------+------------------------------------------------------------------
p800ind |                                             
  No    |       16.0    (29.1%)        12.0    (26.7%)
  Yes   |       39.0    (70.9%)        33.0    (73.3%)
  Total |       55.0   (100.0%)        45.0   (100.0%)         .12    <.001
---------------------------------------------------------------------------

then replace your second call to table with the following code:

Code:

xtset studyphase
collect levels p800ind
collect , tags(p800ind[.m]) : ///
xtgee p800ind i.did i.p203 i.time, ///
    family(binomial 1) ///
    link(logit) ///
    corr(independent) ///
    robust ///
    eform
collect label levels result _r_b "Odds ratio", modify
collect style cell result[_r_b], nformat(%9.2f)
collect style cell result[_r_p], nformat(%9.3f) minimum(.001)
collect layout (p800ind) (p203#studyphase#result colname[1.did]#result[_r_b _r_p])

Comment

Kehinde Atoloye

Join Date: Nov 2021
Posts: 107

07 Apr 2024, 10:33

Originally posted by Jeff Pitblado (StataCorp) View Post

Thank you. I am including a more comprehensive data

I would like the table be displayed this way if possible

Intervention		Control		DiD
Baseline	Endline	Baseline	Endline	Coef/odds	p-value
numerator/denominator (percent)	numerator/denominator (percent)	numerator/denominator (percent)	numerator/denominator (percent)

Code:

xtgee m i.did i.intervention i.time, family(binomial) link(logit) corr(exchangeable) i(participant_id) t(time) vce(robust) nolog level(95)

If you don't mind also assisting with determining what the best possible cluster variable should be. Although the unit of repeated measure is participant_id, I am thinking there is a possibility of lga, facility and intervention qualifying as a cluster variable too. Perhaps there is any test one can conduct to determine. The analysis is a DiD analysis of repeated measures, at baseline and endline. Thanks.

Code:

* Example generated by -dataex-. For more info, type help dataex
clear
input byte(lga intervention facility) long participant_id float(d e f g h i j k l m) byte did float time
1 1  1   1 0 6 0 1 0 0 1 1 0 1 0 0
1 1  1   1 1 6 1 1 2 1 1 0 0 1 1 1
1 1  1   2 0 6 1 3 0 1 1 1 0 1 0 0
1 1  1   2 0 6 0 5 3 1 1 0 0 1 1 1
1 1  1   3 0 6 1 1 0 1 1 1 0 1 0 0
1 1  1   3 1 6 0 5 6 1 1 0 0 1 1 1
1 1  1   4 0 6 1 2 0 1 1 1 0 1 0 0
1 1  1   4 1 6 1 5 6 1 1 0 0 1 1 1
1 1  1   5 0 6 0 1 0 1 1 1 0 0 0 0
1 1  1   5 1 6 1 1 5 1 1 0 0 0 1 1
1 1  1   6 0 5 0 3 0 1 1 1 0 0 0 0
1 1  1   6 1 6 1 5 8 0 1 0 1 1 1 1
1 1  1   7 0 6 0 2 1 1 1 1 1 1 0 0
1 1  1   7 1 6 0 3 1 1 1 0 0 1 1 1
1 1  1   8 0 6 0 3 0 1 1 1 0 0 0 0
1 1  1   8 0 6 0 2 0 0 0 0 . . 1 1
1 1  1   9 0 6 0 1 0 1 1 1 1 1 0 0
1 1  1   9 1 6 0 2 1 0 1 0 1 0 1 1
1 1  1  10 0 6 0 3 1 1 1 1 1 0 0 0
1 1  1  10 1 6 1 1 1 1 1 0 0 1 1 1
1 1  1  11 0 6 1 3 0 1 1 1 0 1 0 0
1 1  1  11 1 6 1 3 3 1 1 0 0 1 1 1
1 1  1  12 0 6 0 3 0 0 0 1 . . 0 0
1 1  1  12 0 6 1 3 2 0 1 0 0 1 1 1
1 1  1  13 0 6 1 2 0 0 0 1 . . 0 0
1 1  1  13 0 6 0 2 3 0 0 1 . . 1 1
1 1  1  14 0 6 0 2 2 1 1 1 0 1 0 0
1 1  1  14 1 6 1 5 3 1 0 1 . . 1 1
1 1  1  15 0 6 1 4 0 0 0 1 . . 0 0
1 1  1  15 0 6 0 1 1 1 1 1 0 0 1 1
1 1  1  16 0 6 1 3 0 0 0 0 . . 0 0
1 1  1  16 0 6 1 2 0 0 0 1 . . 1 1
1 1  1  17 0 6 0 3 0 0 0 1 . . 0 0
1 1  1  17 0 6 0 0 1 0 0 1 . . 1 1
1 1  1  18 0 6 0 2 0 1 1 1 0 1 0 0
1 1  1  18 1 6 1 4 2 1 1 1 0 1 1 1
1 1  1  19 0 6 0 2 0 1 1 1 0 1 0 0
1 1  1  19 1 6 1 2 0 1 0 1 . . 1 1
1 1  1  20 0 6 0 2 0 1 1 1 0 1 0 0
1 1  1  20 1 6 1 5 8 1 1 0 0 1 1 1
1 1  1  21 0 6 1 2 2 1 1 1 1 1 0 0
1 1  1  21 1 6 1 5 4 1 1 0 0 1 1 1
1 1  1  22 0 5 1 3 1 1 1 1 0 1 0 0
1 1  1  22 1 6 1 5 7 1 0 1 . . 1 1
1 1  2  23 0 6 1 2 0 0 1 1 0 1 0 0
1 1  1  23 1 6 1 5 4 1 1 0 0 1 1 1
1 1  2  24 0 6 1 1 0 1 1 1 1 1 0 0
1 1  2  24 1 6 1 5 8 1 1 0 0 1 1 1
1 1  2  25 0 5 1 1 0 1 1 1 0 1 0 0
1 1  2  25 1 6 1 3 2 1 1 0 0 1 1 1
1 1  2  26 0 6 0 2 2 1 1 1 0 1 0 0
1 1  2  26 0 6 1 3 4 1 1 0 1 1 1 1
1 1  2  27 0 6 0 3 0 1 1 1 0 1 0 0
1 1  2  27 0 6 1 5 3 1 1 0 0 1 1 1
1 1  2  28 0 6 1 4 2 1 1 1 0 1 0 0
1 1  2  28 1 6 1 5 7 1 0 1 . . 1 1
1 1  2  29 0 6 0 2 0 1 1 1 0 0 0 0
1 1  2  29 1 6 1 5 3 1 0 1 . . 1 1
1 1  2  30 0 6 0 2 0 0 0 1 . . 0 0
1 1  2  30 0 6 0 3 0 0 0 1 . . 1 1
1 1  2  31 0 6 0 2 0 1 1 1 0 0 0 0
1 1  2  31 1 6 1 5 5 1 1 0 0 1 1 1
1 1  2  32 0 6 0 2 0 1 1 1 0 1 0 0
1 1  2  32 1 6 1 5 0 0 1 1 0 1 1 1
1 1  2  33 0 6 0 3 0 1 1 1 0 0 0 0
1 1  2  33 1 6 1 4 4 1 1 0 0 0 1 1
1 1  2  34 0 5 0 0 0 0 0 1 . . 0 0
1 1  2  34 0 6 0 2 0 0 0 1 . . 1 1
1 1  2  35 0 6 1 3 0 1 1 1 0 1 0 0
1 1  2  35 0 6 1 5 4 1 1 0 0 0 1 1
1 1  2  36 0 6 0 2 0 0 0 0 . . 0 0
1 1  2  36 0 6 0 0 0 0 0 1 . . 1 1
1 1  2  37 0 6 0 2 3 0 0 0 . . 0 0
1 1  2  37 0 6 0 3 2 1 1 1 0 0 1 1
1 1  2  38 0 6 0 2 0 1 1 1 0 1 0 0
1 1  2  38 1 6 1 5 6 1 1 0 0 1 1 1
1 1  3  39 0 6 0 1 2 1 0 1 0 0 0 0
1 1  3  39 1 5 1 2 3 1 1 0 0 0 1 1
1 1  3  40 0 6 0 1 2 1 1 1 0 1 0 0
1 1  3  40 1 6 1 1 4 1 0 1 . . 1 1
1 1  3  41 0 1 0 1 2 1 1 1 0 0 0 0
1 1  3  41 1 6 1 3 2 1 0 1 . . 1 1
1 1  3  42 0 4 0 2 2 1 1 1 0 0 0 0
1 1  3  42 1 2 1 1 2 1 0 1 . . 1 1
1 1  3  43 0 1 1 1 3 1 1 1 0 1 0 0
1 1  3  43 1 5 1 1 2 1 1 1 0 1 1 1
1 1  3  44 0 3 1 1 3 1 1 1 0 0 0 0
1 1  3  44 0 6 0 1 2 1 0 1 . . 1 1
1 1  3  45 0 2 1 1 2 0 1 0 0 0 0 0
1 1  3  45 1 6 1 2 2 1 1 0 0 0 1 1
1 1  3  46 0 4 1 2 3 0 1 1 1 0 0 0
1 1  3  46 0 2 1 1 2 0 0 0 . . 1 1
1 1  3  47 0 5 0 1 2 1 1 1 0 0 0 0
1 1  3  47 1 6 0 1 2 1 0 1 . . 1 1
1 1  3  48 0 5 0 1 3 1 1 1 0 0 0 0
1 1  3  48 1 6 0 1 2 0 0 1 . . 1 1
1 1  3  49 0 4 1 3 2 0 0 1 . . 0 0
1 1  3  49 1 6 0 1 2 0 0 1 . . 1 1
1 1  3  50 0 2 1 2 3 1 1 1 0 0 0 0
1 1  3  50 1 6 1 2 2 1 0 1 . . 1 1
1 1  3  51 0 1 0 2 3 0 1 1 0 0 0 0
1 1  3  51 0 6 0 1 2 1 0 1 . . 1 1
1 1  3  52 0 4 1 2 3 1 1 0 0 0 0 0
1 1  3  52 1 6 0 1 2 1 1 0 0 0 1 1
1 1  3  53 0 1 1 1 2 1 0 0 0 0 0 0
1 1  3  53 1 6 1 1 2 1 1 1 0 0 1 1
1 1  3  54 0 0 0 2 2 0 1 1 0 0 0 0
1 1  3  54 1 6 0 1 2 1 1 0 0 0 1 1
1 1  3  55 0 1 1 2 2 0 1 0 1 0 0 0
1 1  3  55 0 6 0 1 2 1 1 1 0 0 1 1
1 1  4  56 0 5 0 1 2 0 0 1 . . . 0
1 1  4  57 0 6 0 1 1 0 1 0 0 0 0 0
1 1  4  57 1 6 0 3 2 1 1 0 0 1 1 1
1 1  4  58 0 6 0 1 0 1 1 1 0 1 0 0
1 1  4  58 1 5 1 2 2 1 1 0 0 1 1 1
1 1  4  59 0 6 1 2 1 1 1 1 0 0 0 0
1 1  4  59 1 6 0 2 2 1 0 1 . . 1 1
1 1  4  60 0 6 1 1 1 1 1 1 0 0 0 0
1 1  4  60 1 6 1 2 3 1 1 0 0 1 1 1
1 1  4  61 0 6 0 1 1 0 0 0 . . 0 0
1 1  4  61 0 4 1 2 3 0 0 1 . . 1 1
1 1  4  62 0 5 1 2 1 0 1 1 0 1 0 0
1 1  4  62 1 6 1 3 4 1 1 0 0 1 1 1
1 1  4  63 1 6 0 1 2 0 0 1 . . 0 0
1 1  4  63 1 6 0 3 3 1 1 0 0 1 1 1
1 1  4  64 0 5 1 2 2 1 1 0 0 0 0 0
1 1  4  64 0 4 1 3 2 1 1 0 0 1 1 1
1 1  4  65 0 6 1 3 2 1 1 1 0 1 0 0
1 1  4  65 1 6 1 3 1 1 0 1 . . 1 1
1 1  4  66 1 6 1 2 2 1 1 1 0 1 0 0
1 1  4  66 1 6 0 3 3 1 0 1 . . 1 1
1 1  4  67 1 5 0 2 0 1 1 0 0 0 0 0
1 1  4  67 1 5 1 4 3 0 0 1 . . 1 1
1 1  4  68 0 0 0 1 0 0 0 0 1 0 0 0
1 1  4  68 1 6 0 3 3 1 1 0 1 0 1 1
1 1  4  69 0 1 1 2 0 0 0 1 . . 0 0
1 1  4  69 1 6 1 3 1 0 0 1 . . 1 1
1 1  4  70 0 1 0 2 1 1 1 1 0 1 0 0
1 1  4  70 1 5 0 1 1 1 0 1 . . 1 1
1 1  4  71 0 6 0 1 0 0 0 1 . . 0 0
1 1  4  71 1 6 1 3 1 0 0 1 . . 1 1
1 1  4  72 0 5 0 1 0 0 1 0 0 0 0 0
1 1  4  72 0 4 0 2 1 1 1 0 0 1 1 1
1 1  4  73 1 6 0 1 0 0 1 1 0 0 0 0
1 1  4  73 1 6 1 1 1 1 1 0 0 1 1 1
1 1  4  74 1 5 0 1 2 1 1 1 0 1 0 0
1 1  4  74 1 6 0 3 1 1 1 0 0 1 1 1
1 1  4  75 0 5 0 1 0 1 1 1 0 1 0 0
1 1  4  75 1 6 1 3 1 1 0 1 . . 1 1
1 1  4  76 1 3 1 3 2 1 1 1 1 0 0 0
1 1  4  76 0 6 0 3 1 1 1 0 0 0 1 1
1 1  4  77 0 5 0 1 2 1 1 1 0 1 0 0
1 1  4  77 1 6 1 2 3 1 1 0 0 1 1 1
1 1  5  78 0 6 1 2 2 1 1 1 0 1 0 0
1 1  5  78 0 6 1 3 4 0 1 0 1 0 1 1
1 1  5  79 0 6 0 1 2 0 1 1 0 0 0 0
1 1  5  79 0 6 1 3 4 1 1 0 1 0 1 1
1 1  5  80 0 6 0 2 2 0 1 0 0 0 0 0
1 1  5  80 0 6 1 2 4 0 0 1 . . 1 1
1 1  5  81 0 6 0 2 3 0 0 1 . . 0 0
1 1  5  81 1 6 1 2 4 0 0 1 . . 1 1
1 1  5  82 0 5 0 2 1 0 1 0 1 0 0 0
1 1  5  82 0 6 1 2 4 0 1 1 0 0 1 1
1 1  5  83 0 6 0 2 2 0 0 1 . . 0 0
1 1  5  83 0 6 1 2 4 0 0 1 . . 1 1
1 1  5  84 0 6 0 2 3 0 1 0 1 0 0 0
1 1  5  84 0 6 1 2 3 0 1 0 0 0 1 1
1 1  5  85 0 5 0 2 2 0 1 0 1 0 0 0
1 1  5  85 0 6 1 2 3 0 1 0 0 0 1 1
1 1  5  86 0 6 1 3 3 0 1 0 1 0 0 0
1 1  5  86 0 6 1 2 2 0 1 0 0 1 1 1
1 1  5  87 1 5 1 1 2 0 1 0 1 0 0 0
1 1  5  87 0 6 1 2 4 0 0 1 . . 1 1
1 1  5  88 0 5 0 2 2 0 0 0 . . 0 0
1 1  5  88 0 6 1 2 1 0 0 1 . . 1 1
1 1  5  89 0 6 1 2 2 1 1 1 1 1 0 0
1 1  5  89 0 6 1 1 3 1 1 0 1 0 1 1
1 1  5  90 0 5 0 2 2 1 1 1 1 1 0 0
1 1  5  90 0 6 1 2 2 1 1 0 1 1 1 1
1 1  5  91 0 5 0 2 2 1 1 0 1 0 0 0
1 1  5  91 0 6 1 2 3 1 1 0 0 1 1 1
1 1  5  92 0 6 0 2 2 0 1 1 1 1 0 0
1 1  5  92 1 6 1 2 2 1 1 0 0 1 1 1
1 1  5  93 0 5 0 3 2 1 1 0 1 0 0 0
1 1  5  93 0 6 1 2 3 1 0 1 . . 1 1
1 1  5  94 0 6 0 2 2 0 1 0 1 1 0 0
1 1  5  94 0 6 1 3 3 1 0 1 . . 1 1
1 1  5  95 0 5 0 3 1 0 0 1 . . 0 0
1 1  5  95 0 6 1 1 3 0 0 1 . . 1 1
1 1  5  96 0 5 1 1 1 0 1 1 1 1 0 0
1 1  5  96 0 6 1 2 3 0 1 0 0 1 1 1
1 1  5  97 0 5 0 1 1 0 0 0 . . 0 0
1 1  5  97 0 6 1 1 2 0 0 1 . . 1 1
1 1  5  98 0 5 0 3 2 0 1 0 1 0 0 0
1 1  5  98 0 6 1 2 4 1 1 0 1 1 1 1
1 1  5  99 0 6 0 1 1 1 1 1 1 1 0 0
1 1  5  99 0 6 1 2 3 1 1 0 0 1 1 1
1 1  5 100 0 6 0 2 1 1 1 1 1 1 0 0
1 1  5 100 1 6 1 3 3 1 1 0 0 1 1 1
1 1  5 101 0 6 0 2 1 0 1 1 1 0 0 0
1 1  5 101 0 6 1 3 2 1 1 0 0 0 1 1
2 1  6 102 0 6 0 5 8 1 0 1 0 1 0 0
2 1  6 102 0 6 0 1 1 0 1 0 0 1 1 1
2 1  6 103 0 6 0 5 8 1 0 1 0 1 0 0
2 1  6 103 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 104 0 6 0 5 8 1 0 1 1 1 0 0
2 1  6 104 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 105 0 6 0 5 8 0 0 0 . . . 0
2 1  6 106 0 6 0 5 8 1 0 1 0 1 0 0
2 1  6 106 0 6 0 1 8 1 1 0 0 1 1 1
2 1  6 107 0 6 0 5 8 1 0 1 0 1 0 0
2 1  6 107 0 6 0 1 8 1 0 0 . . 1 1
2 1  6 108 0 6 0 5 8 1 0 1 1 1 0 0
2 1  6 108 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 109 0 6 0 5 7 0 0 0 . . 0 0
2 1  6 109 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 110 0 6 0 4 7 1 0 1 1 1 0 0
2 1  6 110 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 111 0 3 0 5 8 1 0 0 1 1 0 0
2 1  6 111 1 6 0 1 1 1 1 0 0 1 1 1
2 1  6 112 0 6 0 5 8 1 0 1 1 1 0 0
2 1  6 112 0 6 0 1 1 0 1 0 0 1 1 1
2 1  6 113 0 2 0 5 8 1 0 0 1 1 0 0
2 1  6 113 0 6 0 1 1 0 0 0 . . 1 1
2 1  6 114 0 3 0 5 8 1 0 0 1 1 0 0
2 1  6 114 0 6 0 1 8 0 1 0 0 1 1 1
2 1  6 115 0 6 0 5 7 1 0 1 0 1 0 0
2 1  6 115 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 116 0 6 0 5 6 1 0 1 1 1 0 0
2 1  6 116 0 6 0 1 8 0 1 0 0 1 1 1
2 1  6 117 0 6 0 5 8 1 0 1 1 0 0 0
2 1  6 117 0 6 0 2 8 0 1 0 0 1 1 1
2 1  6 118 0 3 0 5 8 1 0 1 1 1 0 0
2 1  6 118 0 6 0 1 1 0 1 0 0 1 1 1
2 1  6 119 0 6 0 5 8 1 0 1 0 1 0 0
2 1  6 119 0 6 0 1 1 0 1 0 0 1 1 1
2 1  6 120 0 6 0 4 8 1 0 0 0 1 0 0
2 1  6 120 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 121 0 6 0 5 8 1 0 1 0 1 0 0
2 1  6 121 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 122 0 6 0 5 7 1 0 1 0 1 0 0
2 1  6 122 0 6 0 1 1 1 1 0 0 1 1 1
2 1  6 123 0 6 0 5 8 1 0 0 0 1 0 0
2 1  6 123 0 6 0 1 1 1 1 0 0 1 1 1
2 1  7 124 0 6 1 1 2 0 0 1 . . 0 0
2 1  7 124 1 6 1 3 2 0 0 1 . . 1 1
2 1  7 125 0 6 1 3 3 1 1 1 0 1 0 0
2 1  7 125 0 6 1 1 2 1 1 0 0 1 1 1
2 1  7 126 0 5 1 1 1 1 1 1 0 1 0 0
2 1  7 126 0 6 1 2 2 1 0 1 . . 1 1
2 1  7 127 0 3 0 0 0 0 1 1 0 0 0 0
2 1  7 127 0 6 1 2 3 1 1 0 0 1 1 1
2 1  7 128 0 1 0 0 0 0 1 1 0 0 0 0
2 1  7 128 0 6 0 2 2 1 1 0 0 1 1 1
2 1  7 129 0 2 1 1 1 0 1 0 0 . 0 0
2 1  7 129 0 6 1 0 3 1 1 0 0 1 1 1
2 1  7 130 0 6 1 1 1 0 1 0 0 0 0 0
2 1  7 130 1 6 1 3 8 1 1 0 0 1 1 1
2 1  7 131 0 5 0 1 0 0 1 1 0 0 0 0
2 1  7 131 0 4 1 1 2 1 1 0 0 0 1 1
2 1  7 132 0 6 0 1 2 1 1 1 0 0 0 0
2 1  7 132 0 6 1 2 3 1 1 1 0 0 1 1
2 1  7 133 0 5 1 0 0 1 1 1 0 1 0 0
2 1  7 133 0 6 1 1 1 1 1 0 0 1 1 1
2 1  7 134 0 6 1 1 2 0 1 1 0 0 0 0
2 1  7 134 0 5 0 0 1 1 1 0 0 0 1 1
2 1  7 135 0 2 0 1 3 1 1 0 0 0 0 0
2 1  7 135 1 5 1 1 2 1 1 0 0 1 1 1
2 1  7 136 0 5 1 2 0 1 1 0 0 0 0 0
2 1  7 136 1 6 0 1 5 1 1 0 0 1 1 1
2 1  7 137 1 6 0 3 1 1 1 1 0 0 0 0
2 1  7 137 0 5 0 2 2 1 1 0 0 0 1 1
2 1  7 138 0 6 1 1 0 1 1 1 0 0 0 0
2 1  7 138 1 6 1 2 4 1 0 1 . . 1 1
2 1  7 139 0 6 0 1 4 0 1 1 0 0 0 0
2 1  7 139 1 6 1 2 1 1 1 0 0 1 1 1
2 1  7 140 0 5 0 1 0 0 0 1 . . 0 0
2 1  7 140 1 6 0 2 1 0 0 1 . . 1 1
2 1  7 141 0 1 0 2 1 0 1 1 0 1 0 0
2 1  7 141 0 6 1 1 1 0 1 0 0 0 1 1
2 1  7 142 0 3 0 1 0 0 0 1 . . 0 0
2 1  7 142 1 6 1 1 1 0 0 1 . . 1 1
2 1  7 143 0 5 1 1 1 0 0 1 . . 0 0
2 1  7 143 0 6 1 2 1 0 0 1 . . 1 1
2 1  7 144 0 4 1 1 1 1 1 1 0 0 0 0
2 1  7 144 1 6 1 4 3 1 1 0 0 1 1 1
2 1  7 145 0 5 1 1 2 1 1 1 0 0 0 0
2 1  7 145 1 6 1 1 4 0 1 1 0 1 1 1
2 1  7 146 0 1 0 1 0 0 0 1 . . 0 0
2 1  7 146 1 5 0 1 1 0 0 1 . . 1 1
2 1  8 147 0 3 0 2 0 0 0 1 . . 0 0
2 1  8 147 0 6 0 2 4 0 0 1 . . 1 1
2 1  8 148 0 4 0 1 0 1 1 1 0 0 0 0
2 1  8 148 1 4 1 1 2 1 1 0 0 0 1 1
2 1  8 149 0 6 0 3 0 0 0 0 . . 0 0
2 1  8 149 0 6 0 2 0 0 0 1 . . 1 1
2 1  8 150 0 5 0 1 0 1 0 0 1 0 0 0
2 1  8 150 0 5 0 1 1 1 1 0 0 0 1 1
2 1  8 151 0 6 0 1 0 1 1 1 1 0 0 0
2 1  8 151 0 4 0 2 5 1 1 0 0 0 1 1
2 1  8 152 0 3 0 1 0 0 0 1 1 0 0 0
2 1  8 152 0 5 0 2 0 1 1 0 0 0 1 1
2 1  8 153 0 5 0 1 0 1 0 1 0 0 0 0
2 1  8 153 0 6 0 2 4 1 1 0 0 1 1 1
2 1  8 154 0 1 0 1 0 0 0 0 1 0 0 0
2 1  8 154 0 4 0 3 0 0 0 0 0 0 1 1
2 1  8 155 0 1 0 1 0 0 0 0 1 0 0 0
2 1  8 155 0 5 0 2 3 1 0 0 0 1 1 1
2 1  8 156 0 5 0 2 1 0 0 0 0 0 0 0
2 1  8 156 0 5 1 1 1 1 1 0 0 1 1 1
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4 0 18 383 0 6 0 3 1 0 0 1 . . 0 1
4 0 18 384 0 5 0 1 2 1 1 1 0 1 0 0
4 0 18 384 0 6 0 1 1 1 1 0 0 1 0 1
4 0 18 385 0 6 0 3 1 1 1 1 0 0 0 0
4 0 18 385 0 6 0 2 1 1 1 0 0 0 0 1
4 0 18 386 0 6 0 1 1 0 0 1 . . 0 0
4 0 18 386 0 6 0 1 1 0 0 1 . . 0 1
4 0 18 387 0 6 0 1 0 1 1 1 0 1 0 0
4 0 18 387 0 6 0 1 1 1 1 0 0 1 0 1
4 0 18 388 0 6 0 1 1 0 0 1 . . 0 0
4 0 18 388 0 6 0 1 2 1 1 0 0 0 0 1
4 0 18 389 0 6 0 2 1 0 0 1 . . 0 0
4 0 18 389 0 6 0 1 1 0 0 1 . . 0 1
4 0 19 390 0 1 1 3 3 0 1 0 1 1 0 0
4 0 19 390 0 3 0 3 0 0 1 1 1 0 0 1
4 0 19 391 0 4 0 2 3 1 1 0 1 0 0 0
4 0 19 391 0 3 0 3 5 0 0 0 . . 0 1
4 0 19 392 0 5 1 3 0 0 0 1 . . 0 0
4 0 19 392 0 5 0 3 3 0 0 1 . . 0 1
4 0 19 393 0 4 0 2 2 0 1 1 1 1 0 0
4 0 19 393 0 3 0 2 3 0 0 1 . . 0 1
4 0 19 394 0 5 0 1 3 0 1 0 1 0 0 0
4 0 19 394 0 4 0 2 0 0 1 0 1 0 0 1
4 0 19 395 0 4 0 1 0 0 1 1 1 1 0 0
4 0 19 395 0 4 0 1 1 0 1 0 1 0 0 1
4 0 19 396 0 5 0 3 1 1 1 0 1 0 0 0
4 0 19 396 0 3 0 1 2 0 0 0 1 0 0 1
4 0 19 397 0 5 0 1 3 0 0 0 . . 0 0
4 0 19 397 0 4 0 3 3 0 0 0 . . 0 1
4 0 19 398 0 1 0 2 2 0 1 0 1 0 0 0
4 0 19 398 0 4 0 1 3 0 1 0 1 0 0 1
4 0 19 399 0 0 0 1 0 1 0 0 1 0 0 0
4 0 19 399 0 0 0 1 0 0 0 0 1 0 0 1
4 0 19 400 0 1 0 1 2 0 1 0 1 0 0 0
4 0 19 400 0 1 0 1 0 0 0 0 1 0 0 1
4 0 19 401 0 0 0 1 2 1 1 0 1 0 0 0
4 0 19 401 0 3 0 1 2 0 1 0 1 0 0 1
4 0 19 402 0 4 0 2 3 1 1 0 1 0 0 0
4 0 19 402 0 4 0 3 3 0 0 0 . . 0 1
4 0 19 403 0 1 0 1 0 0 0 0 1 0 0 0
4 0 19 403 0 3 0 1 0 0 1 0 1 0 0 1
4 0 19 404 0 0 0 2 0 0 1 0 1 0 0 0
4 0 19 404 0 1 0 2 0 0 0 0 . . 0 1
4 0 19 405 0 4 0 3 0 0 0 0 1 0 0 0
4 0 19 405 0 1 0 3 2 0 1 0 1 0 0 1
4 0 19 406 0 5 0 3 2 1 0 0 1 0 0 0
4 0 19 406 0 1 0 2 2 0 1 0 1 0 0 1
4 0 19 407 0 4 0 3 2 1 1 0 1 0 0 0
4 0 19 407 0 4 0 3 2 0 1 0 0 0 0 1
4 0 19 408 0 3 0 2 1 0 0 1 . . 0 0
4 0 19 408 0 0 0 2 4 0 0 0 . . 0 1
4 0 19 409 0 5 0 3 2 0 0 1 . . 0 0
4 0 19 409 0 3 0 3 2 0 0 0 . . 0 1
4 0 19 410 0 3 0 1 0 0 0 0 . . 0 0
4 0 19 410 0 2 0 3 0 0 1 1 0 0 0 1
4 0 19 411 0 4 0 1 1 0 0 0 1 0 0 0
4 0 19 411 0 1 0 2 1 0 0 0 . . 0 1
4 0 20 412 0 5 1 3 0 0 1 0 1 0 0 0
4 0 20 412 0 1 0 2 0 0 1 0 1 0 0 1
4 0 20 413 0 4 0 2 0 0 0 0 1 0 0 0
4 0 20 413 0 6 0 2 3 0 0 0 . . 0 1
4 0 20 414 0 5 0 3 2 0 1 0 1 0 0 0
4 0 20 414 0 5 1 3 3 0 1 0 1 0 0 1
4 0 20 415 0 4 0 1 3 0 0 0 1 0 0 0
4 0 20 415 0 3 0 3 3 0 0 0 . . 0 1
4 0 20 416 0 5 0 2 0 0 0 0 . . 0 0
4 0 20 416 0 3 0 2 3 1 1 0 1 0 0 1
4 0 20 417 0 5 0 3 3 0 0 0 . . 0 0
4 0 20 417 0 4 0 3 0 0 0 0 . . 0 1
4 0 20 418 0 2 0 0 0 0 0 0 . . 0 0
4 0 20 418 0 3 0 1 0 0 0 0 . . 0 1
4 0 20 419 0 5 0 1 1 0 0 0 . . 0 0
4 0 20 419 0 4 0 3 3 0 0 0 . . 0 1
4 0 20 420 0 4 0 3 4 0 0 0 . . 0 0
4 0 20 420 0 5 0 3 2 0 0 0 . . 0 1
4 0 20 421 0 6 1 3 1 1 1 1 0 0 0 0
4 0 20 421 0 5 0 3 4 1 1 0 0 0 0 1
4 0 20 422 0 5 0 2 1 0 0 0 . . 0 0
4 0 20 422 0 2 0 3 3 0 0 0 . . 0 1
4 0 20 423 0 1 0 1 1 0 0 0 1 0 0 0
4 0 20 423 0 2 0 3 3 0 0 0 . . 0 1
4 0 20 424 0 3 0 1 0 0 0 0 . . 0 0
4 0 20 424 0 4 0 1 0 0 0 0 . . 0 1
4 0 20 425 0 4 0 3 0 0 0 1 . . 0 0
4 0 20 425 0 5 0 2 3 0 0 1 . . 0 1
4 0 20 426 0 4 0 3 4 1 1 0 1 0 0 0
4 0 20 426 0 3 0 3 1 1 1 0 1 0 0 1
5 0 21 427 1 5 0 3 0 1 1 0 0 1 0 0
5 0 21 427 0 5 0 2 1 0 1 0 0 0 0 1
5 0 21 428 0 4 0 1 0 1 1 1 0 0 0 0
5 0 21 428 0 6 1 1 2 1 1 1 0 0 0 1
5 0 21 429 0 6 0 1 1 1 1 1 0 0 0 0
5 0 21 429 0 5 0 1 2 1 1 1 0 0 0 1
5 0 21 430 0 4 0 1 1 1 1 1 0 0 0 0
5 0 21 430 1 5 1 3 4 0 1 0 0 1 0 1
5 0 21 431 0 6 0 1 1 1 1 1 0 0 0 0
5 0 21 431 0 6 0 2 1 1 1 1 0 0 0 1
5 0 21 432 0 3 0 1 1 0 0 1 . . 0 0
5 0 21 432 0 4 0 1 1 0 0 1 . . 0 1
5 0 21 433 0 4 0 2 1 1 1 1 0 0 0 0
5 0 21 433 0 5 1 2 2 1 1 0 0 0 0 1
5 0 21 434 0 3 1 1 0 0 0 1 . . 0 0
5 0 21 434 1 6 0 1 2 0 0 1 . . 0 1
5 0 21 435 0 6 0 1 1 0 0 0 . . 0 0
5 0 21 435 0 6 1 3 0 0 0 1 . . 0 1
5 0 21 436 0 5 0 2 2 1 1 1 0 0 0 0
5 0 21 436 0 5 0 1 1 0 1 1 0 0 0 1
5 0 21 437 0 5 0 2 0 0 0 1 . . 0 0
5 0 21 437 0 6 1 1 1 0 0 1 . . 0 1
5 0 21 438 0 5 0 2 0 1 1 1 0 0 0 0
5 0 21 438 1 6 1 2 4 1 1 0 0 1 0 1
5 0 21 439 0 4 0 1 1 1 1 0 0 0 0 0
5 0 21 439 0 5 1 1 1 1 1 0 0 0 0 1
5 0 21 440 0 3 0 1 1 0 0 1 . . 0 0
5 0 21 440 0 6 0 1 2 0 0 1 . . 0 1
5 0 21 441 0 6 1 4 1 1 1 1 0 1 0 0
5 0 21 441 0 4 0 3 2 1 1 1 0 0 0 1
5 0 21 442 0 6 1 3 2 1 1 1 0 1 0 0
5 0 21 442 1 5 1 2 4 1 1 0 0 0 0 1
5 0 21 443 0 6 0 3 2 1 1 1 0 0 0 0
5 0 21 443 0 6 0 1 2 1 0 0 . . 0 1
5 0 21 444 0 5 0 1 2 1 1 1 0 0 0 0
5 0 21 444 0 6 1 1 2 0 1 0 0 0 0 1
5 0 21 445 0 4 0 2 1 1 1 1 0 0 0 0
5 0 21 445 1 4 1 2 5 1 1 1 0 0 0 1
5 0 21 446 0 6 1 1 4 0 0 1 . . 0 0
5 0 21 446 1 5 0 1 1 0 0 1 . . 0 1
5 0 21 447 0 5 1 1 1 1 1 0 0 0 0 0
5 0 21 447 1 5 1 2 1 1 1 1 0 0 0 1
5 0 21 448 0 6 0 1 3 1 1 0 0 0 0 0
5 0 21 448 0 6 0 3 1 1 1 0 0 0 0 1
5 0 21 449 0 6 0 1 1 1 1 1 0 0 0 0
5 0 21 449 0 6 1 1 5 1 1 0 0 1 0 1
5 0 21 450 0 6 0 2 2 1 1 1 0 0 0 0
5 0 21 450 1 5 1 2 3 1 1 0 0 0 0 1
5 0 22 451 0 5 0 2 4 1 0 0 0 0 0 0
5 0 22 451 0 6 0 1 0 1 1 0 1 0 0 1
5 0 22 452 0 4 0 3 4 1 1 0 0 0 0 0
5 0 22 452 0 6 0 1 0 0 1 0 0 0 0 1
5 0 22 453 0 5 0 2 4 1 1 0 0 0 0 0
5 0 22 453 0 5 0 1 0 0 0 0 . . 0 1
5 0 22 454 0 4 0 2 3 1 1 0 0 0 0 0
5 0 22 454 0 6 0 1 0 0 0 0 . . 0 1
5 0 22 455 0 3 0 1 5 1 1 0 0 0 0 0
5 0 22 455 0 6 0 1 0 1 1 0 1 0 0 1
5 0 22 456 0 3 0 2 5 0 1 0 0 0 0 0
5 0 22 456 0 6 0 1 0 0 1 0 0 1 0 1
5 0 22 457 0 3 0 1 0 1 1 0 0 0 0 0
5 0 22 457 0 6 0 1 0 1 1 0 1 0 0 1
5 0 22 458 0 3 0 1 0 1 1 0 0 0 0 0
5 0 22 458 0 6 0 1 0 0 1 0 1 0 0 1
5 0 22 459 0 4 0 1 0 1 1 0 0 0 0 0
5 0 22 459 0 6 0 1 0 0 1 0 1 0 0 1
5 0 22 460 0 5 0 1 0 1 1 0 0 0 0 0
5 0 22 460 0 6 0 1 0 1 1 0 0 0 0 1
5 0 22 461 0 4 0 0 0 0 0 0 . . 0 0
5 0 22 461 0 6 0 1 0 0 0 0 . . 0 1
5 0 22 462 0 5 0 1 0 0 0 0 . . 0 0
5 0 22 462 0 6 0 1 0 0 0 0 . . 0 1
5 0 22 463 0 4 0 0 0 0 0 0 . . 0 0
5 0 22 463 0 6 0 1 0 0 0 0 . . 0 1
5 0 22 464 0 3 0 1 0 0 0 0 . . 0 0
5 0 22 464 0 5 0 1 0 0 1 0 0 0 0 1
5 0 22 465 0 5 0 1 0 0 0 0 . . 0 0
5 0 22 465 0 5 0 1 0 0 0 0 . . 0 1
5 0 22 466 0 4 0 1 0 0 0 0 . . 0 0
5 0 22 466 0 6 1 1 0 1 1 0 0 0 0 1
5 0 22 467 0 3 0 2 3 1 1 0 0 0 0 0
5 0 22 467 0 6 0 1 0 1 1 0 1 0 0 1
5 0 22 468 0 4 0 2 4 1 1 0 0 0 0 0
5 0 22 468 0 6 0 1 0 0 0 0 . . 0 1
5 0 22 469 0 4 0 3 4 0 1 0 0 0 0 0
5 0 22 469 0 6 0 1 0 1 0 0 . . 0 1
5 0 22 470 0 4 0 3 4 1 1 0 0 0 0 0
5 0 22 470 0 6 0 1 0 1 1 0 0 1 0 1
5 0 22 471 0 5 0 1 4 1 1 0 0 0 0 0
5 0 22 471 0 6 0 1 0 1 1 0 0 0 0 1
5 0 22 472 0 4 0 1 0 0 1 0 0 0 0 0
5 0 22 472 0 6 0 1 0 0 1 0 1 0 0 1
5 0 22 473 0 5 0 0 0 1 1 0 0 0 0 0
5 0 22 473 0 6 0 1 0 1 1 0 1 0 0 1
5 0 22 474 0 5 0 1 0 1 1 0 0 0 0 0
5 0 22 474 0 6 0 1 0 1 1 0 1 0 0 1
5 0 23 475 0 4 0 1 0 0 0 0 . . 0 0
5 0 23 475 0 6 0 1 0 1 1 1 0 0 0 1
5 0 23 476 0 4 0 1 0 0 0 0 . . 0 0
5 0 23 476 0 5 0 1 0 0 0 1 . . 0 1
5 0 23 477 0 5 0 1 0 0 0 0 . . 0 0
5 0 23 477 0 5 0 1 0 0 0 0 . . 0 1
5 0 23 478 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 478 0 6 0 1 0 1 1 0 1 0 0 1
5 0 23 479 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 479 0 6 1 1 0 1 1 1 0 0 0 1
5 0 23 480 0 5 0 1 0 1 1 0 0 0 0 0
5 0 23 480 0 6 0 1 0 1 1 0 0 0 0 1
5 0 23 481 0 5 0 1 0 1 1 0 0 0 0 0
5 0 23 481 0 6 0 1 0 0 1 0 1 0 0 1
5 0 23 482 0 5 0 1 0 1 1 0 0 0 0 0
5 0 23 482 0 6 0 1 0 1 1 0 0 0 0 1
5 0 23 483 0 5 0 1 0 1 1 0 1 0 0 0
5 0 23 483 0 6 1 2 0 1 1 0 0 0 0 1
5 0 23 484 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 484 0 6 0 1 0 1 1 0 0 0 0 1
5 0 23 485 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 485 0 6 0 1 0 1 1 0 0 0 0 1
5 0 23 486 0 5 0 1 0 0 1 0 0 0 0 0
5 0 23 486 0 6 0 1 0 1 1 0 0 0 0 1
5 0 23 487 0 5 0 1 0 1 1 0 0 0 0 0
5 0 23 487 0 6 1 1 0 1 0 0 . . 0 1
5 0 23 488 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 488 0 6 0 1 0 1 1 0 0 0 0 1
5 0 23 489 0 6 0 1 0 1 1 1 0 0 0 0
5 0 23 489 0 6 0 1 0 1 1 1 0 0 0 1
5 0 23 490 0 6 0 1 0 0 0 0 . . 0 0
5 0 23 490 0 6 0 1 0 0 0 0 . . 0 1
5 0 23 491 0 6 0 1 0 1 1 0 0 . 0 0
5 0 23 491 0 6 0 1 0 0 1 0 0 0 0 1
5 0 23 492 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 492 0 6 1 1 0 0 0 0 . . 0 1
5 0 23 493 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 493 0 6 0 1 0 1 1 1 0 0 0 1
5 0 23 494 0 6 1 1 0 1 1 1 0 0 0 0
5 0 23 494 0 5 0 1 0 1 0 0 . . 0 1
5 0 23 495 0 6 0 1 0 0 0 0 . . 0 0
5 0 23 495 0 6 0 1 0 0 0 0 . . 0 1
5 0 23 496 0 5 0 1 0 0 0 0 . . 0 0
5 0 23 496 0 6 0 1 0 1 1 0 0 0 0 1
5 0 23 497 0 6 0 1 0 1 1 0 0 0 0 0
5 0 23 497 0 6 1 1 0 1 1 0 1 0 0 1
5 0 24 498 0 6 1 2 1 1 1 1 0 0 0 0
5 0 24 498 0 6 1 1 1 1 1 0 0 0 0 1
5 0 24 499 0 6 1 1 1 1 1 1 0 0 0 0
5 0 24 499 1 6 1 2 1 1 1 1 0 1 0 1
5 0 24 500 0 6 0 1 1 0 0 1 . . 0 0
5 0 24 500 1 6 1 2 1 0 0 1 . . 0 1
5 0 24 501 0 4 1 1 1 1 1 1 1 0 0 0
5 0 24 501 0 6 1 3 1 1 1 1 0 1 0 1
5 0 24 502 0 5 0 1 1 1 1 1 0 0 0 0
5 0 24 502 1 6 1 2 1 1 1 1 0 1 0 1
end
label values lga p204
label values intervention p203
label values facility p206
label values participant_id participant_id
label values d p500new
label def p500new 0 "Not Very Aware", modify
label def p500new 1 "Very aware", modify
label values f p700_0102ind
label def p700_0102ind 0 "No", modify
label def p700_0102ind 1 "Yes", modify
label values i p808adind
label def p808adind 0 "No change or decreased", modify
label def p808adind 1 "Increased", modify
label values j p902agind
label def p902agind 0 "Lower", modify
label def p902agind 1 "Higher", modify
label values k p1001_3ind
label def p1001_3ind 0 "No", modify
label def p1001_3ind 1 "Yes", modify
label values l p1101ind
label def p1101ind 0 "Not/somewhat satisfied", modify
label def p1101ind 1 "Quite/very satisfied", modify
label values m p1200ind
label def p1200ind 0 "No", modify
label def p1200ind 1 "Yes", modify
label values time time
label def time 0 "Endline", modify
label def time 1 "Baseline", modify

Last edited by Kehinde Atoloye; 07 Apr 2024, 10:35.

Comment

Kehinde Atoloye

Join Date: Nov 2021
Posts: 107

07 Apr 2024, 11:07

I tried it with newly sent data and below is what I have. PErhaps there is something I am not doing right.

Code:

xtset participant_id time
collect levels m
collect , tags(m[.m]) : xtgee m i.did i.intervention i.time, family(binomial) link(logit) i(participant_id) t(time) corr(independent) eform
collect label levels result _r_b "Odds ratio", modify
collect style cell result[_r_b], nformat(%9.2f)
collect style cell result[_r_p], nformat(%9.3f) minimum(.001)
collect layout (m) (intervention#time#result colname[1.did]#result[_r_b _r_p])

but got

Code:

. collect levels m

Collection: Table
 Dimension: m
    Levels: .m

. collect , tags(m[.m]) : xtgee m i.did i.intervention i.time, family(binomial 1) link(logit) i(participant_id) t(time) 
> corr(independent) eform

Iteration 1:  Tolerance = 1.631e-12

GEE population-averaged model                      Number of obs    =      692
Group variable: participan~d                       Number of groups =      401
Family: Binomial                                   Obs per group:  
Link:   Logit                                                   min =        1
Correlation: independent                                        avg =      1.7
                                                                max =        2
                                                   Wald chi2(3)     =    72.57
Scale parameter = 1                                Prob > chi2      =   0.0000

Pearson chi2(692)    = 692.00                      Deviance         =   876.01
Dispersion (Pearson) =      1                      Dispersion       = 1.265911

--------------------------------------------------------------------------------
             m | Odds ratio   Std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
         1.did |   2.119515   .7361946     2.16   0.031     1.072949    4.186911
1.intervention |   2.461538   .5782903     3.83   0.000     1.553222    3.901034
               |
          time |
     Baseline  |   1.245421   .3540234     0.77   0.440      .713434    2.174096
         _cons |        .35   .0687386    -5.35   0.000     .2381746    .5143286
--------------------------------------------------------------------------------
Note: _cons estimates baseline odds (conditional on zero random effects).
Note: collect is ignoring label "Coefficient" for level _r_b of dimension result.

. collect label levels result _r_b "Odds ratio", modify

. collect style cell result[_r_b], nformat(%9.2f)

. collect style cell result[_r_p], nformat(%9.3f) minimum(.001)

. collect layout (m) (intervention#time#result colname[1.did]#result[_r_b _r_p])

Collection: Table
      Rows: m
   Columns: intervention#time#result colname[1.did]#result[_r_b _r_p]

Your layout specification does not uniquely match any items. Dimension cmdset might help uniquely match items.

Announcement

Not able to extract values from a r(table) matrix to add to a table as new columns

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