qq Plots

[Tony Josipovic]

Hello everbody,

I have estimated parameter alpha for each participant with six different methods. I wanted to plot each alpha of one method with the alpha of another method. When I get the graphs they look weird. Have someone had some qqplots like mine? Or what else could be a good solution for the visualiation of the data?

Thanks.

[Nick Cox]

It is indeed hard to see what is going on here. One simple point is that ADM has an outlier at about 200. Whether it is otherwise similar to any of the other variables is something I can't guess from these graphs.

WIlliam S. Cleveland makes good use of pairwise quantile plots, but I find that superimposing quantiles is usually more instructive. qplot from the Stata Journal is one possibility.

Why not post the data?

[Tony Josipovic]

It is indeed hard to see what is going on here. One simple point is that ADM has an outlier at about 200. Whether it is otherwise similar to any of the other variables is something I can't guess from these graphs.

WIlliam S. Cleveland makes good use of pairwise quantile plots, but I find that superimposing quantiles is usually more instructive. qplot from the Stata Journal is one possibility.

Why not post the data?

You mean, I should post a data example with dataex?

[Nick Cox]

Indeed.

[Tony Josipovic]

Indeed.

Hi Nick,

this is my dataex:

[Nick Cox]

Sorry, but I can't use that. The point of dataex is to give a dataset defined by Stata code that people can run. I am not going to try to scrape that for an example. .

[Tony Josipovic]

Sorry, but I can't use that. The point of dataex is to give a dataset defined by Stata code that people can run. I am not going to try to scrape that for an example. .

Hi Nick,

I am sorry that my dataex is unusable, but I do not have experience with it, except that I run it a few times. Do you want my whole code from stata? Did you mean this?


import excel using Data, firstrow clear
destring, replace

generate method_nr=.

replace method_nr = 1 if method == "10 Alpha-Delta-1"
replace method_nr = 2 if method == "10 Alpha-Delta-2"
replace method_nr = 3 if method == "Holt-Laury"
replace method_nr = 4 if method == "Teubner"
replace method_nr = 5 if method == "5 Alpha-Delta-1"
replace method_nr = 6 if method == "Alpha-Delta-Maren"

label define method_nr 1 "10 AD-1" 2 "10 AD-2" 3 "HL" 4 "T" 5 "5 AD-1" 6 "ADM"
label value method_nr method_nr


encode method, gen(m)
tab m
keep m mean_alpha mean_delta user_id

reshape wide mean_alpha mean_delta, i(user_id) j(m)

qqplot mean_alpha1 mean_alpha2, aspectratio(1) ytitle("10 Alpha-1") xtitle("10 Alpha-2") title("10 AD-1/10 AD-2") yline(0) xline(0) saving ("....qq10.gph",replace) //xscale(r(-20 60)) yscale(r(-20 60))
qqplot mean_alpha1 mean_alpha3, aspectratio(1) ytitle("10 Alpha-1") xtitle("5 Alpha-1") title("10 AD-1/5 AD-1") yline(0) xline(0) saving ("...\qq20.gph",replace) //xscale(r(-20 60)) yscale(r(-20 60))
qqplot mean_alpha1 mean_alpha4, aspectratio(1) ytitle("10 Alpha-1") xtitle("ADM") title("10 AD-1/ADM") yline(0) xline(0) saving ("..\qq30.gph",replace) //xscale(r(-20 60)) yscale(r(-20 60))
qqplot mean_alpha1 mean_alpha5, aspectratio(1) ytitle("10 Alpha-1") xtitle("HL") title("10 AD-1/HL") yline(0) xline(0) saving ("...\qq40.gph",replace) //xscale(r(-20 60)) yscale(r(-20 60))
qqplot mean_alpha1 mean_alpha6, aspectratio(1) ytitle("10 Alpha-1") xtitle("T") title("10 AD-1/T") yline(0) xline(0) saving ("...\qq50.gph",replace) //xscale(r(-20 60)) yscale(r(-20 60))
graph combine "...\qq10.gph" "...\qq20.gph" "...\qq30.gph" "..\qq40.gph" "...\qq50.gph"


If you have meant something else, please let me know what exactly you need from me. Thank you.

[Nick Cox]

It's not difficult. See

help dataex 

and if that fails see

https://www.statalist.org/forums/help#stata

dataex mean_alpha?

should yield the output you copy and paste into a post in this thread.

[Tony Josipovic]

It's not difficult. See

help dataex 

and if that fails see

https://www.statalist.org/forums/help#stata

dataex mean_alpha?

should yield the output you copy and paste into a post in this thread.

Hi Nick,

thanks for the helpful information about dataex. Here is my dataex for mean_alpha1:


. dataex mean_alpha1

----------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. To install: ssc install dataex
clear
input double mean_alpha1
              1.05
              .755
               .69
             1.025
               .69
             1.025
.41500000000000004
             1.025
 .8200000000000001
 .6950000000000001
             1.025
 .7150000000000001
             1.025
             1.025
              .755
             1.025
 .8600000000000001
               .99
 .6499999999999999
              1.49
               .81
              .775
              .355
               .76
              .355
 .8600000000000001
             1.025
              1.05
.41500000000000004
             1.025
             1.025
             1.025
 .6950000000000001
             1.025
               .81
             1.025
             1.025
             1.575
              .675
.42000000000000004
             1.025
              .165
 .5449999999999999
.30000000000000004
              1.47
              .775
              .485
             1.025
.46499999999999997
 .6950000000000001
               .52
              .565
1.5899999999999999
             1.025
             1.025
              .775
               .76
              .535
               .48
 .6000000000000001
              .165
              .575
               .17
               .93
              .675
 .7150000000000001
 .8400000000000001
             1.605
             1.025
              .775
               .99
.41500000000000004
 .5449999999999999
               .44
1.5899999999999999
              1.47
             1.605
 .6950000000000001
              .775
.41500000000000004
             1.055
 .8600000000000001
.42000000000000004
               .99
              .595
              .505
             1.025
               .48
1.3599999999999999
             1.025
             1.025
               .81
              .855
               .47
                .3
 .7549999999999999
             1.025
               .69
              .535
              .485
end

------------------ copy up to and including the previous line ------------------

Listed 100 out of 302 observations


Is that now OK? Thanks.

[Nick Cox]

Thanks, but please look again. I typed

dataex mean_alpha?

where the ? is a wildcard capturing all six variables mean_alpha1 to mean_alpha6: there would be little point to our seeing just one variable. As the implication of #1 is that you have at least one outlier I would add an option so that we see everything.

dataex mean_alpha? , count(302)

I will be away from my computer for about 3 hours now. Nothing stops anyone else answering more quickly.

[Tony Josipovic]

Thanks, but please look again. I typed

dataex mean_alpha?

where the ? is a wildcard capturing all six variables mean_alpha1 to mean_alpha6: there would be little point to our seeing just one variable. As the implication of #1 is that you have at least one outlier I would add an option so that we see everything.

dataex mean_alpha? , count(302)

I will be away from my computer for about 3 hours now. Nothing stops anyone else answering more quickly.

I am so sorry, I thought ? should stand for 1 or 2, ect.
How can I paste the data nicer? If I just copy-paste it from stata it lokks like this:

dataex mean_alpha?, count (302)

----------------------- copy starting from the next line -----------------------
[CODE]
* Example generated by -dataex-. To install: ssc install dataex
clear
input double(mean_alpha1 mean_alpha2 mean_alpha3 mean_alpha4 mean_alpha5 mean_alpha6)
1.05 1.605 1.605 .8503207637023151 1.3199999999999998 1.7105000000000001
.755 .915 1.055 1.16206143196799 1.72 .24335
.69 .755 .905 1.39775942274163 1 .7755000000000001
1.025 1.075 1.04 1.02263660438861 1 1
.69 .7 .655 1.35943045124946 1 .7755000000000001
1.025 1.075 1.04 .919694150401523 1 1
.41500000000000004 .75 .655 .691850894549782 .455 .5793
1.025 1.075 1.04 .977672030862242 1 1



I also get the information that Maximum number of characters exceeded. It cannot be more than 30000 characters. The current number of characters is 35834. What is the easiest and best way to provide the complete dataex? Thanks.

[Nick Cox]

This is a test of dataex with 302 observations and 6 numeric variables.

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(whatever1 whatever2 whatever3 whatever4 whatever5 whatever6)
  .3488717   .6306959       .36064    .956012    .5972152    .4186606
  .2668857  .07612886    .03673937   .3384307    .3533689    .7969238
  .1366463   .3457762    .57537925   .6057844    .4596219    .5201019
.028556867  .09631826      .221964   .6617531    .4068561   .02220209
  .8689333  .54500425     .7979596   .4743504    .4077098    .6578994
  .3508549   .8078102    .16497013   .2992962    .7577423   .16301645
 .07110509   .1186969     .8966501   .4654182    .5807178    .6571357
 .32336795   .2723139     .8639233 .034121275    .5116685    .8350152
  .5551032    .431756     .4085775     .30816    .2047537    .0570736
   .875991   .7970607     .1949142 .003487901    .6093534    .4007674
 .20470947   .5824112     .7840244  .28833413    .0387973    .7719912
  .8927587   .1161198      .070528   .6364796    .7746329    .8089946
  .5844658    .742251     .2428684  .08675048    .9013634    .6000561
  .3697791  .26177752      .204897   .7966116    .9806134    .7996946
  .8506309   .8069862     .2065663   .4795855     .378227    .3199501
  .3913819    .667398     .4900448  .04386123    .3226037    .8986002
 .11966132   .2427153    .26690826   .9899774     .782653     .177872
  .7542434    .629151    .01006614   .8475135     .566009    .8063351
  .6950234  .53290236     .8361933   .9417136    .2736457    .3188445
  .6866152   .7275901     .4765315   .3003805    .7823756    .5817582
  .9319346   .6358732     .4714345   .9400132   .29409716   .10019553
  .4548882  .30897725     .3801901   .9475428    .9107049   .28940704
  .0674011 .031594325      .816859   .3450306     .474516    .9443634
  .3379889   .7675205      .412824  .55524206    .3359306    .7993056
  .9748848   .7826504    .12924023   .6854833   .26345026    .4864607
  .7264384   .4032832    .21030535  .11279255   .17222953    .7479802
 .04541512   .2183805     .7137254   .8242237    .7375143    .8986604
  .7459667     .65438     .3503907   .7657151    .3477812    .5754572
  .4961259   .6249384    .07086408    .231456    .9681915    .4173038
  .7167162   .6531338     .6881661  .19181708      .89627    .8418096
   .859742   .8717756     .4603092   .9720517    .2050382    .5001324
 .13407555   .3228789    .23016937   .6437678    .6921436    .8519058
 .48844185   .7929984     .6171731   .4974545  .020211987    .9920368
  .8712187    .890541     .6949196   .1538196    .3855711    .7027063
  .7664683   .5850651     .3374159   .6828631    .7440512    .4003244
 .25125554  .16509147      .387021    .312333    .5049479 .0026621534
 .16636477  .21370257    .09764836   .9198263     .584623    .4821162
  .7437958   .7540336     .9711645   .1923872    .8685638    .9690186
  .9805113   .4879022   .012112502   .9760643    .4546707    .4347669
  .7295772    .784761      .629522   .8125142    .7748037    .6414735
  .9011049   .3591984      .225161    .564901    .7298304    .5468456
 .26436493  .27472454     .9587393   .8040974    .4911503     .987264
  .8856509   .3598655     .3209424   .5962987    .9107035    .9547378
   .882112   .4099021     .9041464   .8943648   .12460504    .4911027
   .748933   .7452902    .19612606   .4545577   .01231986    .7514483
  .9196262   .6489046     .9146875  .56784004   .03717232    .9765408
  .6934533 .024716996   .016822837   .8573105   .15307076    .4727278
  .2154026   .8951959     .5806194    .489548    .8117028   .10404762
  .8285888   .3025671     .9434988   .5545882    .5564202   .09841928
 .04421536   .9094009     .8424475   .2717406    .8797395   .09568615
  .8630378   .1980502     .9254969  .08052149    .7954421    .8467412
  .3526046   .9396281     .5135824    .748333    .4057344    .3358724
  .7720399   .9796002     .7684242   .8791647   .27344185   .53188473
  .5861199 .019446915    .02592992   .2358954    .9534792    .9798947
  .3227766   .6482321     .6911973   .5186448     .786071    .8436455
 .17293066   .4484187     .6122161 .021258455    .3541959    .8225987
  .8053644   .6840329     .6287054   .7259853   .08869617    .8964981
  .3060019  .17468606    .56436235   .3906652    .0834552    .7955093
 .21909967  .07517652     .6144451  .22270304   .09636933     .626348
   .724731  .25289148      .269866 .036899354    .7419433    .0230035
  .6964867  .09834082    .03489263   .0928724   .02070321    .9717283
  .9119344  .51919395     .8602025  .10996235    .9231768  .031069605
  .6795634  .24395096     .3574097   .9473996   .14382604    .8445307
  .3549416   .7611632     .2017341   .5506343   .04782375     .204635
    .73897  .48342595     .8017713   .0749511    .4608969    .4259032
 .18740167   .4842453    .15975595   .2016104    .6494145   .05278226
  .3146128  .47314665     .7445277   .8244188    .4167082    .6901507
  .1375693   .7108122    .52616996   .8156186   .07828436   .38342625
  .6537739  .14951538     .8194776   .4771375   .24280035    .7068417
 .27013195   .9263857     .3154643   .6336106   .04420254     .712261
  .8998394   .3883542    .03566065  .09545588   .55603766    .6639749
  .5734232   .3072758    .20502636   .2637803    .7387324    .6276135
 .11147037   .8733901      .149372  .30216765    .3170609    .7924258
  .4145227   .9820003  .0028598644   .8662144   .23082066    .8182809
.003052204   .7621314     .4283178  .09442192   .54064256    .5479002
  .6659978   .7449422     .7184824  .11306874    .7299951   .11392886
  .3462876   .3134476     .8381081   .3430918   .25665858    .8139769
  .0780235   .7424038    .18588196   .1831292    .8870668   .56942606
 .12758136   .3216974     .8848767  .45623535    .1747041    .9314196
  .2297006  .48313835     .5469507   .9616036    .9416567    .3587537
  .3295547    .950884     .6376462   .4237315   .33033755   .31586125
  .4144089   .5302431     .4897037  .14638567    .5929443    .4912878
.036084738 .026787916    .21063723   .5354874    .8717136    .6446795
 .08438109   .4755277     .7363223   .7323775     .571244    .9518121
.009876247  .09654304     .8317598  .08710776     .702037    .9726177
  .3200437   .2699718     .9492906   .5474224    .6800585    .6053908
.005196966   .9286809     .7394671  .28013048    .5203593   .49292415
 .22754347   .8276468     .7860632   .0905889    .4472686   .04216485
   .851468  .08834159     .3663346  .31138664   .25451902    .4472602
  .9820066  .20203817     .4763205   .5252051    .3480951    .4714447
.032479186  .21422483     .4325287   .7635252   .51026404      .54993
  .9874847   .6291219     .6459439  .55356884     .370028    .3449739
   .894106   .4517648     .2420242  .10169004  .014994144    .3022764
  .9684734   .8285236     .8896527  .05767901    .3986437   .24677886
 .23922028   .1816306    .12809317  .04408112    .2137471    .2016013
  .6927336   .5259434    .14697766   .3154627    .3315967   .29457477
  .4884359  .04015534     .4884153  .10591135    .9117172    .8023747
  .4376452   .8173847    .49010965  .06671948    .9994622    .2518314
  .5858005 .018189823     .5205599   .5924248    .9832386   .29457036
  .3787092   .8708924      .887952   .7020757    .6696112   .07300241
  .6880603   .7101489     .4173595   .9121656   .51240337    .2082022
  .9794578   .2058177    .03257617   .6120089    .3573781    .6746453
  .6701937  .13346358    .37046635  .02825442   .17014106    .7250822
  .5948808  .27601084     .3774713   .7517885    .3221159   .11333132
  .7970893   .6522317     .1952358   .9564982    .8010535    .9182087
  .7835853   .3502681     .4517798   .4900662    .2972489    .7494639
  .6546342   .9921945   .020191964   .5663874    .7478074   .29600614
 .09688907  .02088869     .6246378   .4321739   .05084893    .3883448
  .6885059    .368774    .28260428   .7812381    .9961022    .7303737
   .872496    .454854     .6127123   .4410577   .26265275    .4329889
 .52963525    .137625     .7618679   .1747132  .004531789   .58621436
  .8302209   .5867818     .8497505   .9614761   .17406245    .3069796
  .9339853   .5984893   .015242445   .8956713    .7957382   .58824503
  .1749891  .02060423     .5396008   .4247924    .6507624    .9639818
  .5536171   .6255214    .16114493   .8250874   .07776628    .9909449
  .5346152    .243554    .04438921   .5413929   .17324743    .7918458
  .7767794   .5891765    .07501002   .7456304   .15216297    .4282079
  .1288747    .361389     .5790233  .21453594   .14170407    .4014561
 .27751842   .3213269     .5892495   .7955645     .497656    .8514773
  .4242016   .9684579     .2459801  .05296029    .9393871    .4823096
 .13590056   .8063638     .3860658   .7071615    .8469824    .4882186
  .3325624   .6920744     .5415648   .6967921   .11126234    .5170875
  .4675523   .9056482     .4649227  .14784238 .0088835955    .6275546
 .51608807   .8847663     .1909824   .8872471   .34531385     .611358
 .06694305   .7955388    .25016406   .6141953    .3385353    .9077195
 .07229638    .373362    .59258175   .6547466    .4958427    .5743615
  .6817465   .4137706    .53520966   .3250723    .3397309    .4735439
 .08804953   .9794121    .58649206   .8780633    .8877727    .8971185
 .13270818   .4671331    .16810808   .4415368    .5405418   .01095399
  .8745816   .3582671     .6040714   .8101014    .9249117    .4433249
  .2468877   .7049199     .8727009   .9376529    .3603184    .7320505
   .043255  .54907256     .9903682  .18651846    .8818375    .7426032
  .3764437   .4666853     .0454078  .54218066    .7588792    .7933023
  .7677861  .55963516    .14914225     .33339    .4431816    .9372609
  .7551366  .13266039     .4489954   .8242743    .1721991    .6428731
  .4476188  .09640402    .52572006 .018307567    .1811879    .8056636
  .4087105   .6034411    .04099891   .3039657    .3865013   .23977904
 .29777426  .04256091      .608234  .55991685    .7256564    .3524112
  .6794177  .10262416     .4821762   .7606178   .53889024   .18912596
  .7124024   .9817198     .1584521   .3225134    .8809248   .10794725
 .56622654   .5422223      .241483    .611941    .8686107   .52614844
  .1778325   .5469667     .7688082 .022553703    .7899613    .7189465
 .11399896   .9074407    .17088246  .19591025    .6893945   .29601723
  .5955869   .1058121     .9300933   .2950318   .06823365    .3911087
  .6251604  .51389873     .6191499   .4658934    .4131948    .9225978
   .634899   .3569477    .21185684   .8054574    .9448974  .066453874
  .9944572 .018503504    .45555985   .9891512    .9961277    .8199414
  .7497677   .8677372     .8277618   .7127118   .11056665    .9081864
  .1736788  .14396448     .9246075   .3349668   .22512646     .963169
  .6107705   .3736859     .5091284   .2556627    .8319395   .06331687
  .5754215   .9615316     .4970234  .08372691   .10592862    .3055841
  .3678161 .003440836     .7606224   .6228576   .20543014    .4334986
  .3005246   .9452119      .945023    .362564    .6773011    .4928864
.007538023   .9075785     .9342079  .21443237   .57071394    .3284452
  .6701369  .23994325      .494971   .5233325    .4025198    .4394713
  .4241406  .51792276     .3523825    .583494      .77022    .8411205
  .9537622    .821936    .45202985   .9469459    .4638748    .8633803
 .08674778   .6596439     .8108512   .2367406    .3459106    .5895734
  .8949648    .622691      .922225   .4694958     .455443    .0994138
  .5890286  .18438303    .51293933  .02652248    .9845372    .9409589
  .4005832   .8392156     .6424342  .15395723    .9962279     .922036
  .6654902   .4844661    .22544003   .3703323     .187684    .0386864
  .4198386   .6447397     .9688056   .9556926    .8899175   .51446646
  .7472054 .034635708    .13827966   .5654693   .17611444    .7296869
  .7190143   .7482488     .6464592   .8430785    .4723534     .486661
  .8464647  .08959591      .399232   .3220623    .8238188    .9494621
  .7908313   .3612543     .4699738   .9606591    .8751434   .09354497
  .1900222   .6126024    .23529056  .03021939    .6615545    .4190526
  .3869604   .4969319      .827109  .08598463    .6423526   .45468375
 .23871335 .015301493     .6982278   .3326673     .802131    .8172774
  .3447002   .5427198     .9373755   .5281806    .7128311    .6595466
  .7795682  .04541227     .4626662   .4105174    .3944169    .5319368
  .7484396   .4769426     .1621351   .9002901    .7592371    .2145773
 .23037836   .6809173    .05452213   .6947856    .2679208    .7408234
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  .9180508   .7387761   .004177726    .368501  .017220348   .51005036
  .3138996   .6884711     .8446351   .2898439   .52598906     .341276
  .9019141   .3824532     .3641508  .11311357   .58922577   .12327544
 .07740517   .7957672     .8107548  .07457202    .8970551    .1297689
  .6341382   .4585904    .27155378  .07720169   .18128037    .7900795
  .8147295   .8765864    .51708394  .10544161   .24185154    .6424836
  .8788922   .5313176     .5123527   .9287012   .36507475    .0412038
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    .17993   .7752083     .3558787   .5297512   .53843826    .9119475
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  .4081415   .7538502     .5841006  .40153345   .27444264    .3449988
  .6155495   .1852177    .43261304   .9703205    .7197577    .9586726
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  .3617646  .15807964     .6718097   .1093132    .3823916    .6955702
  .1338996   .9231839    .09571288   .6204874    .7143542    .4312744
.001363096   .3984752     .1957708  .49248815    .2132959    .7336563
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  .6517417   .8115618    .02832463     .81045    .6292137   .51556784
  .9252081   .1763781     .4662072   .7778153    .9338232    .4049557
  .8233367 .032954417     .9976588  .57461554    .9976581   .25611997
  .9229402   .2032501      .423397   .7488256    .6721607   .08473209
  .7480426    .210147     .7435132   .5581618    .4290972   .05891459
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  .4022151   .3270695    .27168334    .318136    .8457566    .8362991
  .8681989  .28943086     .6866202   .7115257   .49334815   .27831855
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  .7239472  .08471239    .05472944   .8985527    .7408876   .06167734
  .7955464   .1648882     .8512263    .669644    .3008768     .347909
  .8925074   .6785589     .2997819    .975374   .03904126    .4998046
  .7078791 .007334597      .812538   .9155825    .6255479   .14790824
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  .9310499  .29345974    .04661484   .6782503    .7429258   .21646224
  .6216809   .3446431     .9957619     .93239    .7184782   .16914473
  .8004354   .7827835    .19743223    .445779    .8115859    .6194289
  .4798372   .2831923     .8255965   .5659708    .8107751   .17600995
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  .8343448  .08946785    .47970185 .022759806    .3438286   .07823905
  .3431251   .5563994     .2772788   .7600296    .6425273    .4487384
  .5906867  .07463773      .650822   .6965978   .22591817    .8682244
    .32953   .3121633    .10572556  .13708863    .9980771    .7168093
  .6996295   .1926827   .014234847   .6906198   .59434885    .4833126
  .8142969   .9333811     .4481005   .6081647    .6074141    .6146165
  .3429726   .8438337    .28715575   .4973137   .24736454    .5438059
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  .9671743   .4501373     .7944884  .27169845    .1882574    .8664822
  .1285523  .11123316     .9680328 .012431592   .29272494    .6356224
  .7578536   .6879476   .018150784   .5209538    .2155348    .5409249
 .25002995   .3111916    .25817555   .7989563     .949012    .5717357
  .9269138   .5012202    .13310464   .4741453    .2973817    .8082242
  .7118431   .7705238      .766541   .6700493   .47059485    .7678562
 .17967154   .3593072   .008671947   .7950285   .55922335     .622166
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  .1946068   .6775658      .316877  .02360998    .6796544   .50032836
  .7135741  .53953046    .14289537   .6820623   .17909065   .09140427
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  .7672456   .9327974     .8884382  .56511956    .2953885   .27577865
  .3653557   .8530564     .7917896   .8397618     .592105    .7877125
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  .9911318   .2385955     .3595372   .6136002    .8485704   .10366658
  .6851298   .6944832     .4265456    .113841    .6873636    .8188233
  .5027668   .2454206     .0848408   .9305902    .6081801   .03889452
  .6903818   .4833633    .36439085   .5189458    .6901931   .47005755
  .8636012   .9445977     .4623141  .55161387    .7071512     .989675
  .0404633   .9037415      .580438   .6180993    .8017052    .7291228
  .1842219   .2430261     .7822571    .452817     .435684    .7263665
  .4198807   .7557979    .06603088 .013168757   .20787387    .3661197
  .6475499   .5309251     .2589655  .28314304    .9112108    .8851424
  .9103145   .7679258     .5666703  .04499651   .56090105   .02538471
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  .8568827  .59377366     .4267464   .6608714     .822699    .5801665
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  .8390664   .5462847     .8855875    .350483    .5038062  .017672615
  .6208202   .9335977    .02498507   .9537742   .05502835    .4036626
  .4041756   .7594104    .12259632   .9591237   .26036248    .9567434
  .9786366   .4732795     .5383945   .3923207    .6325456     .347509
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  .7853414  .51688135     .2178382   .7152806    .1040603    .4665728
  .4717338   .6203774     .3880392   .3228117    .6070312    .7049699
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  .7976828   .6339769    .08250254   .4846864    .3102676    .3859075
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  .8570072  .34738955     .8524442   .8017744     .516189    .6754093
  .9322746   .7714663     .8806648    .525175   .09305243    .4889876
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  .1637711   .8551438     .9159781   .6731707     .814846   .38445985
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  .2363827    .314258     .3255852   .9348271    .0690686    .4989507
  .6764786   .8031017    .51877147     .18833   .05346531    .8144791
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  .8219558   .3396957    .11855964 .009321873    .6532404   .26924905
  .3213928   .2645637    .58366823   .9852205   .26314273  .034031596
  .4164997   .8069639     .5004264   .3644912    .5838737    .4542049
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  .3125404   .6935425     .1663715  .13172364    .6833867    .6189552
  .9322619    .203871     .4234573   .8697823    .6760067    .8005169
  .0502046  .20104358    .50436085   .8130028    .9369418    .3658558
  .6221892   .4646199     .6142209   .4326884    .6128471   .29242727
  .6189114   .8933898     .4479784  .17562613    .2026468    .6190419
  .9028944  .03824018    .14944874   .6412218   .11630663   .12664571
  .3830579  .13844115     .8706222  .32641065    .4409166    .1747644
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  .6978495   .6943715    .15362944   .3748066    .6516842   .29713863
  .7828125  .53279144     .9386509    .958773    .8308372   .08099458
  .8312564   .7187118     .9343078   .9749437  .065225475     .999561
  .8498105   .2219616     .7008432   .7444841    .7968004    .3448386
  .9997507   .2199756     .8792776   .8447949   .39491475   .50958353
 .09139451  .17882447     .9075318  .16970707    .8994266    .6582274
end

[Nick Cox]

The previous post shows that I can't reproduce your problem.

Meanwhile #1 suggests you have an outlier on Alpha-Delta-Maren which is mean_alpha6 in your terms. Showing us the result of

summarize mean_alpha6. detail

would be interesting.

[Tony Josipovic]

The previous post shows that I can't reproduce your problem.

Meanwhile #1 suggests you have an outlier on Alpha-Delta-Maren which is mean_alpha6 in your terms. Showing us the result of

summarize mean_alpha6. detail

would be interesting.

Hi Nick,

here is mean_alpha6.

[Nick Cox]

Gee, what is it about you and attachments? Please, please read https://www.statalist.org/forums/help#stata to understand our requests on attachments.

The whole business of attachments can be re-written here: One, and only one, kind of attachment is welcome on Statalist, the use of .,png to show Stata graphs. All other kinds of attachment are not nearly as helpful as you hope and indeed even at best a distraction from better ways to show anything textual or to do with data.

Still, as those opening the file will see the four highest values, on this variable, are (rounding a little)

4.433 
6.244
7.377
191.169

The outlier at 191.169 is the main reason why the graphs in #1 look odd, which is where you started.

Now you have to tell us, unless someone here recognises the method and can make informed comment. What is going on with this variable?

As I understand it, you have six methods of measuring something and the distributions of five of them seem similar in ranging from about 0.2 to 1.6 (unstated units) -- except that Holt-Laury sometimes produces negative values and this one sometimes is really wild.

That's it, so far. A little Googling suggests risk elicitation as the territory and that is way outside my expertise any way, so I stop there.

Sorry, but I won't encourage work-arounds like sending me the data or posting the data as an attachment. Your data should be small enough to be posted here as dataex output;