Random graphs (148): Dynamite plot

sysuse auto, clear

qui regress mpg i.foreign
local df = e(df_r)
local t = round(((_b[1.foreign]/_se[1.foreign])), .01)
local p = round((2 * ttail(`df', abs(`t'))), .001)
margins, at(foreign = (0 1))
marginsplot, recast(bar) ytitle("Mileage") xtitle("Car type") ///
             plotopts(fcolor(gs14) lcolor(black)) ///
             title("{bf:A} Mileage", justification(left) bexpand span) ///
             addplot(scatteri 28.95 0 28.95 1, recast(line) lwidth(medium) lpattern(solid) ///
                     text(29.5 .5 "{it:t}(`df') = `t', {it:p} = `p'") ///
                     xlabel(0 1) ///
                    || scatteri 28.95 0 28.05 0, recast(line) lwidth(medium) lpattern(solid) ///
                    || scatteri 28.95 1 28.05 1, recast(line) lwidth(medium) lpattern(solid)) ///
             legend(off) ///
             name(figureA, replace)
			 
qui regress weight i.foreign
local df = e(df_r)
local t = round(((_b[1.foreign]/_se[1.foreign])), .01)
local double p = round((2 * ttail(`df', abs(`t'))), .0000001)
margins, at(foreign = (0 1))
marginsplot, recast(bar) ytitle("Weight") xtitle("Car type") ///
             plotopts(fcolor(gs14) lcolor(black)) ///
             title("{bf:B} Weight", justification(left) bexpand span) ///
             addplot(scatteri 3700 0 3700 1, recast(line) lwidth(medium) lpattern(solid) ///
                     text(3760 .5 "{it:t}(`df') = `t', {it:p} = `p'") ///
                     xlabel(0 1) ///
                   || scatteri 3700 0 3600 0, recast(line) lwidth(medium) lpattern(solid) ///
                   || scatteri 3700 1 3600 1, recast(line) lwidth(medium) lpattern(solid)) ///
             legend(off) ///
             name(figureB, replace)
			 
graph combine figureA figureB, col(2) name(figure, replace)

Random graphs (18): Simple interaction plots with -marginsplot- for categorical and continuous variables

sysuse auto, clear

reg mpg c.weight##i.foreign

qui sum weight

local minsd = r(mean) - r(sd)
local mean  = r(mean)
local plusd = r(mean) + r(sd)

margins foreign, at(weight = (`minsd' `mean' `plusd'))

marginsplot, recast(line) noci scheme(lean1) ///
             name(first, replace)
gr export first.png, as(png) replace
// Impossible to tell the difference between
// domestic and foreign lines because of the lean scheme

marginsplot, recast(line) noci ///
             plot1opts(lpattern(dash)) ///
             scheme(lean1) ///
             name(second, replace)
gr export second.png, as(png) replace
// That fixes the problem

marginsplot, recast(line) recastci(rarea) ciopts(color(gs10)) ///
             plot1opts(lpattern(dash)) ///
             scheme(lean1) ///
             name(third, replace)
gr export third.png, as(png) replace
// That's a nice way of plotting CI's

marginsplot, recast(line) recastci(rarea) ciopts(color(gs10)) ///
             plot1opts(lpattern(dash)) ///
             ytitle("Predicted mileage") ///
             title("Car type moderates the weight{char 0150}mileage association") ///
             xlabel(`minsd' "-1 SD" `mean' "Average weight (lbs.)" `plusd' "+1 SD") ///
             xtitle("") ///
             scheme(lean1) ///
             name(fourth, replace)
gr export fourth.png, as(png) replace
// That labels the graph nicely

Random graphs (13): Interaction plots using -marginsplot-

 
estimates use m2   // Open estimates saved via -estimates save-

xtcloglog  // Show results
 
// Set esample() as explained in the help-file to -estimates save-:
mark miss_mod1
markout miss_mod1 fail2 female age1519 age2529 age3034 /// 
                  hatlev1d loggdp hatfield_main
estimates esample:  if miss_mod1
 
// Calculate predicted probabilities:
margins female#hatlev1d, pred(pu0) 

  
// Simple default marginsplot:
marginsplot, xtitle("Gender") ytitle("Predicted probability") /// 
             caption("{it:Source:} EU-LFS 2009 AHM", span) /// 
             title("") name(sexXeduc1, replace) 

  
// As a bar graph:
marginsplot, xtitle("") ytitle("Predicted probability") title("") /// 
             recast(bar) /// // Draw bar graph instead of default 
             xdimension(female hatlev1d) ///  
             /* Get both interacting variables onto x-axis */ ///
             caption("{it:Source:} EU-LFS 2009 AHM", span) /// 
             name(sexXeduc2, replace) 

 
// As a bar graph that better separates the different variable combinations:
marginsplot, xtitle("") ytitle("Predicted probability") ///  
             recast(bar)  ///
             xdimension(female) ///  
             bydimension(hatlev1d, ///
                         elabels(1 "Low education" ///
                                 2 "Medium education" /// 
                                 3 "High education")) /// 
      /* Use -bydimension- to better structure the x-axis */ ///
             name(sexXeduc3, replace) 

 
// As a proper-looking graph:
marginsplot, xtitle("") ytitle("Predicted probability") /// 
             recast(bar)  /// 
       xdimension(female) /// 
             bydimension(hatlev1d, elabels(1 "Low education" ///
                                           2 "Medium education" /// 
                                           3 "High education")) /// 
             byopts(row(1) /// All plots in one row
                    noiyaxes /// // Don't display individual y-axes
      imargin(zero) /// // No margin between by() dimensions  
                    title("") /// // Suppress title 
      caption("{it:Note:} Error bars are 95 % CI's" ///
      "{it:Source:} EU-LFS 2009 AHM", span)) /// 
                    /* Caption here to suppress displaying it three times */ ///
             plotregion(lwidth(none)) /// 
             /* Removes lines around separate by dimension plots */
      subtitle(, pos(6)) /// // Place label of by dimensions below plot 
      plotopts(fcolor(gs14) lcolor(b)) /// 
             /* Options of the recast() type can go here as well */ ///
      name(sexXeduc4, replace)