Adding a variable to the variable's axis of a coefficients plot made with "coefplot"

[Juan Pablo Mesa]

Hello everybody.

Does anyone know how I can add the name of the variable corresponding to the omitted interaction (the interaction between a dummy variable and the reference category of a categorical variable) to the variable's axis of a coefficients plot made with command "coefplot"? In variable axis of the graph, I´m showing 5 variables (-3, -2, -1, 1 and 2). Each variable corresponds to the interaction between a dummy variable and a category (period) of a time variable. Because period 0 is the reference category, interaction between the dummy and period 0 is omitted. However, I need period 0 to be shown in the variable axis of the graph. Because there is no coefficient to period 0, “coefplot” don’t show me the variable. However, I need it.

coefplot (Homicidio_total), ///
yline (0) ///
vertical ///
keep (1.tratamiento#1.mes2 1.tratamiento#2.mes2 1.tratamiento#3.mes2 1.tratamiento#4.mes2 1.tratamiento#5.mes2) ///
coeflabels (1.tratamiento#1.mes2 = "-3" 1.tratamiento#2.mes2 = "-2" 1.tratamiento#3.mes2 = "-1" 1.tratamiento#4.mes2 = "1" 1.tratamiento#5.mes2 = "2") ///
xtitle(Months) ///
msymbol(o) ///
levels(99 95)

I need the graph that has a 0 in the x axis, but I currently have the grpah that doesn't have a 0 in the x axis (I need a 0 between -1 and 1).

[Andrew Musau]

coefplot is from SSC, authored by Ben Jann. You are asked to specify the source of user-written commands, see the FAQs. You have the baselevels option in coefplot to include the base level in the plot

coefplot, baselevels

You also have the addplot option to include additional plots as well as the possibility of extracting the regression results from r(table) and putting these into a matrix, then plotting as shown in #4 of this link. Having said all that, the base coefficient has a value of zero and no confidence interval. Including it in the plot serves no useful purpose and you could just add a note at the bottom of the graph stating "the base category is month=0".

[Juan Pablo Mesa]

Thank you very much Andrew. I was able to do what I wanted thanks to your advise. Now, I'm just wondering how to eliminate the dot corresponding to month 0. Is there a way to eliminate this dot only, or at least, a way to reduce its size? I tried the following 2 options but they didn't work:

msize (small small small tiny small small)

msize (2.3 2.3 2.3 1 2.3 2.3)

The full code of my graph is the following:

reg Homicidio_total i.tratamiento##i.mes2 i.hora i.fecha, vce(cluster fecha)

coefplot, ///
baselevel ///
drop (*tratamiento 0.tratamiento#0.mes2 1.mes2 2.mes2 3.mes2 4.mes2 5.mes2 _cons *hora_entera *fecha) ///
order (1.tratamiento#1.mes2 1.tratamiento#2.mes2 1.tratamiento#3.mes2) ///
coeflabels (0.mes2 = "0" 1.tratamiento#1.mes2 = "-3" 1.tratamiento#2.mes2 = "-2" 1.tratamiento#3.mes2 = "-1" 1.tratamiento#4.mes2 = "1" 1.tratamiento#5.mes2 = "2") ///
xtitle(Mes, size (vsmall)) ///
vertical ///
xline (4, lpattern(dash)) ///
yline (0) ///
msymbol(o) ///
levels(99 95 90) ///
ciopts(lwidth(*1 *2 *3)) ///
legend(title ("Niveles de confianza", size (vsmall)) order(1 "99%" 2 "95%" 3 "90%") row (1) size (vsmall)) ///
ylabel(,labsize(vsmall)) ///
xlabel(,labsize(vsmall)) ///
msize (2.3) ///
legend(symys(*.5) symxs(*.5) size(3) region(c(none)) bm(tiny))

This is the graph:

[Andrew Musau]

You do not provide a data example, but here is one way. The idea is to turn the coefficient of the base category from zero to missing. You will not be able to do this directly in the stored coefficients' matrix -e(b)-, but as stated in #2, coefplot allows you to use a matrix as the input.

sysuse auto
regress mpg i.rep78
*PUT REGRESSION RESULTS IN A MATRIX
mat r= r(table)
*WE JUST NEED THE FIRST 6 ROWS IN THIS INSTANCE.
 mat R= r[1..6,1..6]
*SAME PLOT AS RUNNING COEFPLOT AFTER REGRESSION
coefplot matrix(R), drop(_cons) se(2) baselevels
*BASE LEVEL 1b.rep78. TURN COEFFICIENT FROM 0 TO MISSING
mat list R
 mat R[1,1]=.
*PROCEED
coefplot matrix(R), drop(_cons) se(2) baselevels

[Juan Pablo Mesa]

Hi Andrew, thank you very much again. I was finally able to make the graph I wanted. I just had to add coefplot option "omitted" to your code.

*Regression
reg yvar i.tratamiento##i.mes2 i.hora i.fecha, vce(cluster fecha)

*Matrix
mat resultados = r(table)
mat grafico = resultados[1..6, 15..20]
mat grafico[1,1]=.        
    
*Graph
coefplot matrix(grafico), ///
se(2) ///
baselevels ///
omitted ///
order (1.tratamiento#1.mes2 1.tratamiento#2.mes2 1.tratamiento#3.mes2) ///
coeflabels (1.tratamiento#0.mes2 = "0" 1.tratamiento#1.mes2 = "-3" 1.tratamiento#2.mes2 = "-2" 1.tratamiento#3.mes2 = "-1" 1.tratamiento#4.mes2 = "1" 1.tratamiento#5.mes2 = "2") ///
xtitle(Meses, size (vsmall)) ///
vertical ///
xline (4, lpattern(dash) lcolor(black)) ///
yline (0, lwidth (medthin)) ///
msymbol(o) ///
levels(99 95 90) ///
ciopts(lwidth(*1 *2 *3)) ///
legend(title ("Niveles de confianza", size (vsmall)) order(1 "99%" 2 "95%" 3 "90%") row (1) size (vsmall)) ///
ylabel(,labsize(vsmall)) ///
xlabel(,labsize(vsmall)) ///
msize (2.3) ///
legend(symys(*.5) symxs(*.5) size(3) region(c(none)) bm(tiny))

[Jim Callegari]

Hi All,

I was not sure if I should post on my other question here but my current question relates more to this thread than the other. I want to be able to insert a "2016" entry since it is omitted in my regression and use it as a dotted line as seen above. I currently have it entirely omitted, so I tried to use "baseleves" and "omitted" as options in "coefplot," but it unfortunately does not change how the graph itself appears. Do you have any idea why this may be? Thank you so much for any and all advice!

Best,
Jim

   reg percent i.id_test5 ib2016.year i.year##soft_drink if country=="UK" & type==1, robust  //regression I run to see how percent breakdown of sugar changes over time. I want to see 
    //just doing 1 year lead/lag does not work becasue the former year is colinear with the following year
    
     matrix A = e(b)
local namecol "`: colnames A'"

tokenize `namecol'

forvalues i = 1 / `=colsof(matrix(A))' {
    local mv = A[1,`i']
    if `mv' != 0 & strmatch("``i''" , "*#*") {
        local coefinter `coefinter' ``i''
    }
}

coefplot, keep(`coefinter') vertical title("Ireland Ingredient Soft Drink * Time Interaction Coefficients") xtitle(Years relative to 2016) baselevels omitted yline(0, lpattern("-##")) mlabel(cond(@pval<.05, "***", cond(@pval<.1, "**", cond(@pval<.05, "", ""))))

* Example generated by -dataex-. To install: ssc install dataex
clear
input str14 country str43 prod int year byte type float percent long id_test5 float(new_id soft_drink) str11 newprod long product
"Ireland" "Baby Food"         2014 0    88.76344 2  1 0 "Others"      1
"Ireland" "Baby Food"         2015 0    88.85345 2  1 0 "Others"      1
"Ireland" "Baby Food"         2016 0    88.91432 2  1 0 "Others"      1
"Ireland" "Baby Food"         2017 0    88.97702 2  1 0 "Others"      1
"Ireland" "Baby Food"         2018 0    89.05828 2  1 0 "Others"      1
"Ireland" "Baby Food"         2019 0    89.15837 2  1 0 "Others"      1
"Ireland" "Baby Food"         2020 0    89.23181 2  1 0 "Others"      1
"Ireland" "Baby Food"         2014 1    11.23656 2  2 0 "Others"      1
"Ireland" "Baby Food"         2015 1   11.146546 2  2 0 "Others"      1
"Ireland" "Baby Food"         2016 1   11.085676 2  2 0 "Others"      1
"Ireland" "Baby Food"         2017 1   11.022975 2  2 0 "Others"      1
"Ireland" "Baby Food"         2018 1   10.941716 2  2 0 "Others"      1
"Ireland" "Baby Food"         2019 1    10.84163 2  2 0 "Others"      1
"Ireland" "Baby Food"         2020 1   10.768182 2  2 0 "Others"      1
"Ireland" "Baked Goods"       2014 0    96.40752 3  3 0 "Others"      1
"Ireland" "Baked Goods"       2015 0    96.42551 3  3 0 "Others"      1
"Ireland" "Baked Goods"       2016 0    96.42312 3  3 0 "Others"      1
"Ireland" "Baked Goods"       2017 0    96.40865 3  3 0 "Others"      1
"Ireland" "Baked Goods"       2018 0     96.3924 3  3 0 "Others"      1
"Ireland" "Baked Goods"       2019 0    96.44029 3  3 0 "Others"      1
"Ireland" "Baked Goods"       2020 0    96.34309 3  3 0 "Others"      1
"Ireland" "Baked Goods"       2014 1     3.59136 3  4 0 "Others"      1
"Ireland" "Baked Goods"       2015 1   3.5733924 3  4 0 "Others"      1
"Ireland" "Baked Goods"       2016 1    3.575801 3  4 0 "Others"      1
"Ireland" "Baked Goods"       2017 1    3.590291 3  4 0 "Others"      1
"Ireland" "Baked Goods"       2018 1   3.6065545 3  4 0 "Others"      1
"Ireland" "Baked Goods"       2019 1    3.558706 3  4 0 "Others"      1
"Ireland" "Baked Goods"       2020 1    3.655866 3  4 0 "Others"      1
"Ireland" "Baked Goods"       2014 2 .0011187661 3  5 0 "Others"      1
"Ireland" "Baked Goods"       2015 2 .0011032855 3  5 0 "Others"      1
"Ireland" "Baked Goods"       2016 2 .0010741096 3  5 0 "Others"      1
"Ireland" "Baked Goods"       2017 2 .0010554073 3  5 0 "Others"      1
"Ireland" "Baked Goods"       2018 2 .0010359032 3  5 0 "Others"      1
"Ireland" "Baked Goods"       2019 2 .0010000111 3  5 0 "Others"      1
"Ireland" "Baked Goods"       2020 2 .0010402437 3  5 0 "Others"      1
"Ireland" "Bottled Water"     2014 0    99.74924 4  6 0 "Others"      1
"Ireland" "Bottled Water"     2015 0    99.75259 4  6 0 "Others"      1
"Ireland" "Bottled Water"     2016 0     99.7564 4  6 0 "Others"      1
"Ireland" "Bottled Water"     2017 0    99.76518 4  6 0 "Others"      1
"Ireland" "Bottled Water"     2018 0    99.76906 4  6 0 "Others"      1
"Ireland" "Bottled Water"     2019 0     99.7739 4  6 0 "Others"      1
"Ireland" "Bottled Water"     2020 0    99.78398 4  6 0 "Others"      1
"Ireland" "Bottled Water"     2014 1   .24856895 4  7 0 "Others"      1
"Ireland" "Bottled Water"     2015 1       .2453 4  7 0 "Others"      1
"Ireland" "Bottled Water"     2016 1    .2415012 4  7 0 "Others"      1
"Ireland" "Bottled Water"     2017 1   .23278593 4  7 0 "Others"      1
"Ireland" "Bottled Water"     2018 1    .2289391 4  7 0 "Others"      1
"Ireland" "Bottled Water"     2019 1    .2241746 4  7 0 "Others"      1
"Ireland" "Bottled Water"     2020 1    .2141973 4  7 0 "Others"      1
"Ireland" "Bottled Water"     2014 2 .0021854744 4  8 0 "Others"      1
"Ireland" "Bottled Water"     2015 2    .0021185 4  8 0 "Others"      1
"Ireland" "Bottled Water"     2016 2 .0020976719 4  8 0 "Others"      1
"Ireland" "Bottled Water"     2017 2 .0020299621 4  8 0 "Others"      1
"Ireland" "Bottled Water"     2018 2 .0019979784 4  8 0 "Others"      1
"Ireland" "Bottled Water"     2019 2 .0019317467 4  8 0 "Others"      1
"Ireland" "Bottled Water"     2020 2 .0018214793 4  8 0 "Others"      1
"Ireland" "Breakfast Cereals" 2014 0    88.36055 5  9 0 "Others"      1
"Ireland" "Breakfast Cereals" 2015 0    88.84888 5  9 0 "Others"      1
"Ireland" "Breakfast Cereals" 2016 0    89.38763 5  9 0 "Others"      1
"Ireland" "Breakfast Cereals" 2017 0    89.83304 5  9 0 "Others"      1
"Ireland" "Breakfast Cereals" 2018 0    89.93861 5  9 0 "Others"      1
"Ireland" "Breakfast Cereals" 2019 0    90.04226 5  9 0 "Others"      1
"Ireland" "Breakfast Cereals" 2020 0    90.45103 5  9 0 "Others"      1
"Ireland" "Breakfast Cereals" 2014 1   11.639448 5 10 0 "Others"      1
"Ireland" "Breakfast Cereals" 2015 1   11.151115 5 10 0 "Others"      1
"Ireland" "Breakfast Cereals" 2016 1    10.61238 5 10 0 "Others"      1
"Ireland" "Breakfast Cereals" 2017 1    10.16696 5 10 0 "Others"      1
"Ireland" "Breakfast Cereals" 2018 1   10.061397 5 10 0 "Others"      1
"Ireland" "Breakfast Cereals" 2019 1     9.95774 5 10 0 "Others"      1
"Ireland" "Breakfast Cereals" 2020 1    9.548981 5 10 0 "Others"      1
"Ireland" "Carbonates"        2014 0    93.63103 6 11 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2015 0    93.62852 6 11 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2016 0    93.62292 6 11 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2017 0    93.60877 6 11 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2018 0    93.58997 6 11 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2019 0    93.56568 6 11 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2020 0    93.57092 6 11 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2014 1    6.348202 6 12 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2015 1    6.350781 6 12 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2016 1     6.35649 6 12 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2017 1    6.370764 6 12 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2018 1    6.389736 6 12 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2019 1    6.414242 6 12 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2020 1    6.408684 6 12 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2014 2    .0207635 6 13 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2015 2   .02070044 6 13 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2016 2  .020589443 6 13 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2017 2   .02046161 6 13 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2018 2   .02030443 6 13 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2019 2  .020074073 6 13 0 "Soft Drinks" 2
"Ireland" "Carbonates"        2020 2   .02039159 6 13 0 "Soft Drinks" 2
"Ireland" "Coffee"            2014 0         100 7 14 0 "Others"      1
"Ireland" "Coffee"            2015 0         100 7 14 0 "Others"      1
"Ireland" "Coffee"            2016 0         100 7 14 0 "Others"      1
"Ireland" "Coffee"            2017 0         100 7 14 0 "Others"      1
"Ireland" "Coffee"            2018 0         100 7 14 0 "Others"      1
"Ireland" "Coffee"            2019 0         100 7 14 0 "Others"      1
"Ireland" "Coffee"            2020 0         100 7 14 0 "Others"      1
"Ireland" "Concentrates"      2014 0    64.63536 8 15 0 "Soft Drinks" 2
"Ireland" "Concentrates"      2015 0    64.63651 8 15 0 "Soft Drinks" 2
end
label values type Type
label def Type 0 "Control", modify
label def Type 1 "Sugars", modify
label def Type 2 "Artificial sugars", modify
label values id_test5 id_test5
label def id_test5 2 "Baby Food", modify
label def id_test5 3 "Baked Goods", modify
label def id_test5 4 "Bottled Water", modify
label def id_test5 5 "Breakfast Cereals", modify
label def id_test5 6 "Carbonates", modify
label def id_test5 7 "Coffee", modify
label def id_test5 8 "Concentrates", modify
label values product product
label def product 1 "Others", modify
label def product 2 "Soft Drinks", modify

[Andrew Musau]

In the future, whenever you provide example data, make sure that it is usable. There is no United Kingdom in your data example and all values of your variable take the value 0. I can replace the UK with Ireland and introduce variation in your variable, which is what you needed to create a reproducible example. The advice remains the same, i.e., to hide the marker in your coefplot graph, you need to transform its coefficient from 0 to missing and plot using a matrix. Alternatively, add another marker to hide the marker, but this will entail changes to the xline crossing y=0 as it is interrupted by anything in the foreground. I illustrate the latter. You do not need a loop to select the interaction coefficients as they have common patterns in their names that make them identifiable and thus enable you to use wildcards.

set seed 02072021
replace soft_drink=runiformint(0,1)
reg percent i.id_test5 ib2016.year i.year##soft_drink if country=="Ireland" & type==1, robust
coefplot, keep(20*#1*) xlab(1 "2014" 2 "2015" 3 "2016" 4 "2017" 5 "2018" 6 "2019" 7"2020") ///
vertical title("Ireland Ingredient Soft Drink * Time Interaction Coefficients") ///
xtitle(Years relative to 2016) baselevels omitted xline(3, lpattern("-##") lcolor(red)) ///
mlabel(cond(@pval<.05, "***", cond(@pval<.1, "**", cond(@pval<.05, "", "")))) ///
addplot((scatteri 0 3, msize(medlarge) mcolor(white)) (scatteri -0.25 3 0.25 3, recast(line) ///
lcolor(red) lpattern("-##") mcolor(red))) scheme(s1color)

[Konstantina Boutsioukou]

Hi all,

I am trying to replicate something similar with my data. In particular, my base year is 2007 (yeard1=13) and i would like to make the coefficient points disappear and instead have only the dotted line available for 2007. The code I am using is the following:

coefplot (est1, mcolor(gs10) ciopts(color(gs10)) keep(1.edu_f#*.yeard1)) ///
(est1, mcolor(gs10) ciopts(color(gs10)) keep(2.edu_f#*.yeard1)) ///
(est1, mcolor(gs6) ciopts(color(gs6))keep(3.edu_f#*.yeard1)) ///
(est1, mcolor(gs4) ciopts(color(gs4)) keep(4.edu_f#*.yeard1 )) ///
(est2, msymbol(O) mcolor(gs10) mfcolor(white) ciopts(color(gs10)) keep(1.edu_f#*.yeard1)) ///
(est2, msymbol(O) mcolor(gs10) mfcolor(white) ciopts(color(gs10)) keep(2.edu_f#*.yeard1)) ///
(est2, msymbol(O) mcolor(gs6) mfcolor(white) ciopts(color(gs6))keep(3.edu_f#*.yeard1)) ///
(est2, msymbol(O) mcolor(gs4) mfcolor(white) ciopts(color(gs4)) keep(4.edu_f#*.yeard1 ) omitted baselevels), vertical ///
yline(0) ///
groups(1.edu_f#*.yeard1="{bf:Secondary education}" ///
2.edu_f#*.yeard1="{bf:Secondary education}" ///
3.edu_f#*.yeard1="{bf:Vocational education}" ///
4.edu_f#*.yeard1="{bf:Tertiary education}") ///
baselevels omitted xline(4 18 32, lpattern("-##") lcolor(black)) ///
coeflabels(1.edu_f#10.yeard1="2004" 1.edu_f#11.yeard1="2005" 1.edu_f#12.yeard1="2006" 1.edu_f#13.yeard1="2007" 1.edu_f#14.yeard1="2008" 1.edu_f#15.yeard1="2009" 1.edu_f#16.yeard1="2010" 1.edu_f#17.yeard1="2011" 1.edu_f#18.yeard1="2012" 1.edu_f#19.yeard1="2013" 1.edu_f#20.yeard1="2014" 1.edu_f#21.yeard1="2015" 1.edu_f#22.yeard1="2016" 2.edu_f#10.yeard1="2004" 2.edu_f#11.yeard1="2005" 2.edu_f#12.yeard1="2006" 2.edu_f#13.yeard1="2007" 2.edu_f#14.yeard1="2008" 2.edu_f#15.yeard1="2009" 2.edu_f#16.yeard1="2010" 2.edu_f#17.yeard1="2011" 2.edu_f#18.yeard1="2012" 2.edu_f#19.yeard1="2013" 2.edu_f#20.yeard1="2014" 2.edu_f#21.yeard1="2015" 2.edu_f#22.yeard1="2016" 3.edu_f#10.yeard1="2004" 3.edu_f#11.yeard1="2005" 3.edu_f#12.yeard1="2006" 3.edu_f#13.yeard1="2007" 3.edu_f#14.yeard1="2008" 3.edu_f#15.yeard1="2009" 3.edu_f#16.yeard1="2010" 3.edu_f#17.yeard1="2011" 3.edu_f#18.yeard1="2012" 3.edu_f#19.yeard1="2013" 3.edu_f#20.yeard1="2014" 3.edu_f#21.yeard1="2015" 3.edu_f#22.yeard1="2016" 4.edu_f#10.yeard1="2004" 4.edu_f#11.yeard1="2005" 4.edu_f#12.yeard1="2006" 4.edu_f#13.yeard1="2007" 4.edu_f#14.yeard1="2008" 4.edu_f#15.yeard1="2009" 4.edu_f#16.yeard1="2010" 4.edu_f#17.yeard1="2011" 4.edu_f#18.yeard1="2012" 4.edu_f#19.yeard1="2013" 4.edu_f#20.yeard1="2014" 4.edu_f#21.yeard1="2015" 4.edu_f#22.yeard1="2016", notick labgap(2) angle(vertical)) ///
graphregion(fcolor(white)) ///
note("Base category: Primary education or below") ///
legend(order(2 "Linear Probability Model" 8 "Linear Probability Model with Heckman correction") span)
graph export "$coefplots/edu_father_Heckunemp_nuts2.png", width(1000) height(800) replace


Here you can find an example of my data produced using dataex.

* Example generated by -dataex-. To install: ssc install dataex
clear
input double elite float edu_f double yeard1 float occup_f byte sex_stud float nationality double lambda_1
0 1 12 1 2 1  1.043254525784166
0 1 22 2 2 1  .8807243479288559
0 1 10 3 1 0                  .
0 1 13 1 2 1 1.0431385033157994
0 1 20 1 2 1 1.1431363900854488
0 1 16 2 2 1  .7741537470970734
0 1 19 1 2 1   .866481366070403
0 1 10 1 1 1                  .
0 1 12 1 2 1 1.0767255785213885
0 1 14 2 2 1  .5560497434888139
0 1 16 1 1 1  1.316855959395752
1 1 19 2 2 1  .6619491476386039
0 1 10 2 1 1                  .
0 1 17 1 1 1 1.0833344553048916
0 1 20 1 2 1 1.1097649815619703
0 1 10 2 2 1   .747476765415923
0 1 17 1 2 1  .8711009486511126
0 1 15 1 2 1 1.1336815287270272
0 1 15 2 2 1  .8407630887083043
0 1 11 1 2 1 1.1132139174236917
0 1 18 1 2 1  .8777161723953205
0 1 16 1 2 1 1.0569616273737161
0 1 22 1 2 1 1.2364493747774472
0 1 19 1 2 1  .8551861492225585
0 1 20 1 2 1 1.0989943315008082
0 1 21 1 1 1 1.1512451960347456
0 1 16 1 2 1 1.0733181421527542
0 1 10 1 2 0                  .
0 1 21 1 2 1  .9259909494988807
1 1 11 3 1 1 1.0914367251015615
1 1 13 2 2 1  .7526962805615407
0 1 12 1 1 1                  .
0 1 17 3 1 1  .8363555582228038
0 1 13 1 1 1 1.2740352952515248
0 1 18 1 2 1  .8777161723953205
0 1 21 4 2 1  .8032719085856194
0 1 14 1 2 0                  .
0 1 15 1 2 1  1.117001251812017
0 1 17 3 2 1   .647197852893997
0 1 11 1 2 1   1.10740332761512
0 1 22 2 1 1 1.1529680189202738
0 1 10 2 2 1  .7695939420794652
0 1 16 2 1 1  1.007585485227331
0 1 20 4 2 1 1.0023564172157033
0 1 10 2 1 0                  .
0 1 18 2 2 1   .641209403277852
0 1 22 1 1 1 1.4750794374420984
0 1 15 1 1 1  1.363573755766611
0 1 20 1 2 1 1.0989943315008082
0 1 10 1 2 1                  .
1 1 10 1 2 0                  .
0 1 22 4 2 0  1.690857828103253
0 1 10 1 2 1                  .
0 1 20 2 2 1  .8342793927697049
0 1 10 1 2 1 1.0234433584499347
0 1 10 2 2 1  .7624283697123582
0 1 10 2 2 1  .7624283697123582
1 1 10 2 1 0                  .
0 1 18 3 1 1  .8533380817019487
0 1 18 1 1 1 1.1320407746133925
0 1 10 1 1 1                  .
0 1 12 1 1 0                  .
0 1 13 2 2 1  .7619649639682952
0 1 14 1 2 1   .762401348619022
0 1 14 3 1 1  .7852146924647669
0 1 10 1 2 1                  .
0 1 10 1 2 1 1.0387981590177038
0 1 14 2 2 1  .5456288354752798
0 1 10 1 2 1                  .
0 1 18 1 1 1 1.0880519985668655
0 1 11 3 2 1  .8120536652138782
0 1 18 1 2 1  .8746821619169416
0 1 15 4 1 1 1.2671639176645504
0 1 11 1 2 1 1.1307097492582032
0 1 11 1 1 1  1.353322825342117
0 1 20 4 2 1 1.0361518763063329
0 1 10 1 2 0                  .
0 1 10 2 1 1                  .
0 1 10 2 2 1                  .
0 1 13 1 2 1 1.0326071598347946
0 1 13 2 2 1  .7526962805615407
1 1 13 2 2 1  .7505644665615663
0 1 22 1 1 1 1.4905547818044682
0 1 22 2 1 1 1.0962716998100286
1 1 10 2 2 1                  .
0 1 22 4 2 1 1.1130851549330898
0 1 21 1 2 1  .8829686352828467
0 1 18 1 1 1 1.1387336465821154
0 1 22 1 2 1 1.1766650821898716
0 1 20 2 1 1 1.0578580054556213
0 1 10 1 2 0                  .
0 1 15 3 2 1  .8820169177619769
0 1 16 4 2 1  .9688468671808836
0 1 14 1 2 1                  .
0 1 20 2 2 1  .8506983030599027
0 1 14 2 2 1  .5572822979732545
0 1 17 4 2 0                  .
0 1 22 1 2 1 1.2954481786808116
0 1 18 1 2 1  .9013741162536791
0 1 11 2 1 1 1.0604316360786075
end
label values elite elite
label def elite 0 "Non-elite", modify
label def elite 1 "Elite", modify
label values edu_f edu_f
label def edu_f 1 "Primary education or below", modify
label values occup_f occup_f
label def occup_f 1 "Blue collar", modify
label def occup_f 2 "Low skilled white collar", modify
label def occup_f 3 "High skilled white collar", modify
label def occup_f 4 "Unemployed", modify
label values sex_stud sex_stud
label def sex_stud 1 "Male", modify
label def sex_stud 2 "Female", modify
label values nationality nationality
label def nationality 0 "Non-Greek", modify
label def nationality 1 "Greek", modify

Any help would be greatly appreciated.

Best,
Konstantina

[Andrew Musau]

You need to include the regression commands to create a reproducible example.

[Konstantina Boutsioukou]

I forgot to paste the most important thing, really sorry.
Here they are:
eststo: reg elite i.edu_f#ib13.yeard1 i.edu_f i.occup_f i.sex_stud i.nationality
eststo: reg elite i.edu_f#ib13.yeard1 i.edu_f i.occup_f i.sex_stud i.nationality c.lambda_1#ib13.yeard1

[Andrew Musau]

Thanks. We can exploit the property that the log of 0 is undefined and use the -transform()- option. Taking the log of the base coefficients will result in missing values for these coefficients, which is exactly what we want.

* Example generated by -dataex-. To install: ssc install dataex
clear
input double elite float edu_f double yeard1 float occup_f byte sex_stud float nationality double lambda_1
0 1 12 1 2 1  1.043254525784166
0 1 22 2 2 1  .8807243479288559
0 1 10 3 1 0                  .
0 1 13 1 2 1 1.0431385033157994
0 1 20 1 2 1 1.1431363900854488
0 1 16 2 2 1  .7741537470970734
0 1 19 1 2 1   .866481366070403
0 1 10 1 1 1                  .
0 1 12 1 2 1 1.0767255785213885
0 1 14 2 2 1  .5560497434888139
0 1 16 1 1 1  1.316855959395752
1 1 19 2 2 1  .6619491476386039
0 1 10 2 1 1                  .
0 1 17 1 1 1 1.0833344553048916
0 1 20 1 2 1 1.1097649815619703
0 1 10 2 2 1   .747476765415923
0 1 17 1 2 1  .8711009486511126
0 1 15 1 2 1 1.1336815287270272
0 1 15 2 2 1  .8407630887083043
0 1 11 1 2 1 1.1132139174236917
0 1 18 1 2 1  .8777161723953205
0 1 16 1 2 1 1.0569616273737161
0 1 22 1 2 1 1.2364493747774472
0 1 19 1 2 1  .8551861492225585
0 1 20 1 2 1 1.0989943315008082
0 1 21 1 1 1 1.1512451960347456
0 1 16 1 2 1 1.0733181421527542
0 1 10 1 2 0                  .
0 1 21 1 2 1  .9259909494988807
1 1 11 3 1 1 1.0914367251015615
1 1 13 2 2 1  .7526962805615407
0 1 12 1 1 1                  .
0 1 17 3 1 1  .8363555582228038
0 1 13 1 1 1 1.2740352952515248
0 1 18 1 2 1  .8777161723953205
0 1 21 4 2 1  .8032719085856194
0 1 14 1 2 0                  .
0 1 15 1 2 1  1.117001251812017
0 1 17 3 2 1   .647197852893997
0 1 11 1 2 1   1.10740332761512
0 1 22 2 1 1 1.1529680189202738
0 1 10 2 2 1  .7695939420794652
0 1 16 2 1 1  1.007585485227331
0 1 20 4 2 1 1.0023564172157033
0 1 10 2 1 0                  .
0 1 18 2 2 1   .641209403277852
0 1 22 1 1 1 1.4750794374420984
0 1 15 1 1 1  1.363573755766611
0 1 20 1 2 1 1.0989943315008082
0 1 10 1 2 1                  .
1 1 10 1 2 0                  .
0 1 22 4 2 0  1.690857828103253
0 1 10 1 2 1                  .
0 1 20 2 2 1  .8342793927697049
0 1 10 1 2 1 1.0234433584499347
0 1 10 2 2 1  .7624283697123582
0 1 10 2 2 1  .7624283697123582
1 1 10 2 1 0                  .
0 1 18 3 1 1  .8533380817019487
0 1 18 1 1 1 1.1320407746133925
0 1 10 1 1 1                  .
0 1 12 1 1 0                  .
0 1 13 2 2 1  .7619649639682952
0 1 14 1 2 1   .762401348619022
0 1 14 3 1 1  .7852146924647669
0 1 10 1 2 1                  .
0 1 10 1 2 1 1.0387981590177038
0 1 14 2 2 1  .5456288354752798
0 1 10 1 2 1                  .
0 1 18 1 1 1 1.0880519985668655
0 1 11 3 2 1  .8120536652138782
0 1 18 1 2 1  .8746821619169416
0 1 15 4 1 1 1.2671639176645504
0 1 11 1 2 1 1.1307097492582032
0 1 11 1 1 1  1.353322825342117
0 1 20 4 2 1 1.0361518763063329
0 1 10 1 2 0                  .
0 1 10 2 1 1                  .
0 1 10 2 2 1                  .
0 1 13 1 2 1 1.0326071598347946
0 1 13 2 2 1  .7526962805615407
1 1 13 2 2 1  .7505644665615663
0 1 22 1 1 1 1.4905547818044682
0 1 22 2 1 1 1.0962716998100286
1 1 10 2 2 1                  .
0 1 22 4 2 1 1.1130851549330898
0 1 21 1 2 1  .8829686352828467
0 1 18 1 1 1 1.1387336465821154
0 1 22 1 2 1 1.1766650821898716
0 1 20 2 1 1 1.0578580054556213
0 1 10 1 2 0                  .
0 1 15 3 2 1  .8820169177619769
0 1 16 4 2 1  .9688468671808836
0 1 14 1 2 1                  .
0 1 20 2 2 1  .8506983030599027
0 1 14 2 2 1  .5572822979732545
0 1 17 4 2 0                  .
0 1 22 1 2 1 1.2954481786808116
0 1 18 1 2 1  .9013741162536791
0 1 11 2 1 1 1.0604316360786075
end
label values elite elite
label def elite 0 "Non-elite", modify
label def elite 1 "Elite", modify
label values edu_f edu_f
label def edu_f 1 "Primary education or below", modify
label values occup_f occup_f
label def occup_f 1 "Blue collar", modify
label def occup_f 2 "Low skilled white collar", modify
label def occup_f 3 "High skilled white collar", modify
label def occup_f 4 "Unemployed", modify
label values sex_stud sex_stud
label def sex_stud 1 "Male", modify
label def sex_stud 2 "Female", modify
label values nationality nationality
label def nationality 0 "Non-Greek", modify
label def nationality 1 "Greek", modify

estimates clear
eststo: reg elite i.edu_f#ib13.yeard1 i.edu_f i.occup_f i.sex_stud i.nationality
eststo: reg elite i.edu_f#ib13.yeard1 i.edu_f i.occup_f i.sex_stud i.nationality c.lambda_1#ib13.yeard1


coefplot (est1, mcolor(gs10) ciopts(color(gs10)) keep(1.edu_f#*.yeard1)) ///
(est1, mcolor(gs10) ciopts(color(gs10)) keep(2.edu_f#*.yeard1)) ///
(est1, mcolor(gs6) ciopts(color(gs6))keep(3.edu_f#*.yeard1)) ///
(est1, mcolor(gs4) ciopts(color(gs4)) keep(4.edu_f#*.yeard1 )) ///
(est2, msymbol(O) mcolor(gs10) mfcolor(white) ciopts(color(gs10)) keep(1.edu_f#*.yeard1)) ///
(est2, msymbol(O) mcolor(gs10) mfcolor(white) ciopts(color(gs10)) keep(2.edu_f#*.yeard1)) ///
(est2, msymbol(O) mcolor(gs6) mfcolor(white) ciopts(color(gs6))keep(3.edu_f#*.yeard1)) ///
(est2, msymbol(O) mcolor(gs4) mfcolor(white) ciopts(color(gs4)) keep(4.edu_f#*.yeard1 )), vertical ///
yline(0) ///
groups(1.edu_f#*.yeard1="{bf:Secondary education}" ///
2.edu_f#*.yeard1="{bf:Secondary education}" ///
3.edu_f#*.yeard1="{bf:Vocational education}" ///
4.edu_f#*.yeard1="{bf:Tertiary education}") ///
baselevels omitted xline(4 18 32, lpattern("-##") lcolor(black)) ///
coeflabels(1.edu_f#10.yeard1="2004" 1.edu_f#11.yeard1="2005" 1.edu_f#12.yeard1="2006" 1.edu_f#13.yeard1="2007" 1.edu_f#14.yeard1="2008" 1.edu_f#15.yeard1="2009" 1.edu_f#16.yeard1="2010" 1.edu_f#17.yeard1="2011" 1.edu_f#18.yeard1="2012" 1.edu_f#19.yeard1="2013" 1.edu_f#20.yeard1="2014" 1.edu_f#21.yeard1="2015" 1.edu_f#22.yeard1="2016" 2.edu_f#10.yeard1="2004" 2.edu_f#11.yeard1="2005" 2.edu_f#12.yeard1="2006" 2.edu_f#13.yeard1="2007" 2.edu_f#14.yeard1="2008" 2.edu_f#15.yeard1="2009" 2.edu_f#16.yeard1="2010" 2.edu_f#17.yeard1="2011" 2.edu_f#18.yeard1="2012" 2.edu_f#19.yeard1="2013" 2.edu_f#20.yeard1="2014" 2.edu_f#21.yeard1="2015" 2.edu_f#22.yeard1="2016" 3.edu_f#10.yeard1="2004" 3.edu_f#11.yeard1="2005" 3.edu_f#12.yeard1="2006" 3.edu_f#13.yeard1="2007" 3.edu_f#14.yeard1="2008" 3.edu_f#15.yeard1="2009" 3.edu_f#16.yeard1="2010" 3.edu_f#17.yeard1="2011" 3.edu_f#18.yeard1="2012" 3.edu_f#19.yeard1="2013" 3.edu_f#20.yeard1="2014" 3.edu_f#21.yeard1="2015" 3.edu_f#22.yeard1="2016" 4.edu_f#10.yeard1="2004" 4.edu_f#11.yeard1="2005" 4.edu_f#12.yeard1="2006" 4.edu_f#13.yeard1="2007" 4.edu_f#14.yeard1="2008" 4.edu_f#15.yeard1="2009" 4.edu_f#16.yeard1="2010" 4.edu_f#17.yeard1="2011" 4.edu_f#18.yeard1="2012" 4.edu_f#19.yeard1="2013" 4.edu_f#20.yeard1="2014" 4.edu_f#21.yeard1="2015" 4.edu_f#22.yeard1="2016", notick labgap(2) angle(vertical)) ///
graphregion(fcolor(white)) ///
note("Base category: Primary education or below") ///
transform(*13.*= log(@)) ///
legend(order(2 "Linear Probability Model" 8 "Linear Probability Model with Heckman correction") span)
*graph export "$coefplots/edu_father_Heckunemp_nuts2.png", width(1000) height(800) replace

Res.:

[Konstantina Boutsioukou]

Thank you so much Andrew! That is great