log type: text
opened on: 17 Oct 2005, 11:04:29
. edit
(3 vars, 25 obs pasted into editor)
- preserve
. tabulate husb wife [fweight=count]
| wife
husb | 1 2 3 4 5 | Total
-----------+-------------------------------------------------------+----------
1 | 4,074 63 32 42 215 | 4,426
2 | 25 3,947 143 95 1,009 | 5,219
3 | 16 132 239 18 304 | 709
4 | 19 78 18 1,022 360 | 1,497
5 | 103 1,156 373 492 28,453 | 30,577
-----------+-------------------------------------------------------+----------
Total | 4,237 5,376 805 1,669 30,341 | 42,428
. describe
Contains data
obs: 25
vars: 3
size: 200 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
count int %8.0g
wife byte %8.0g
husb byte %8.0g
-------------------------------------------------------------------------------
Sorted by:
Note: dataset has changed since last saved
. label define race 1 "black" 2 "Mexican" 3 "Other Hisp" 4 "all others" 5 "White"
. label val wife race
. label val husb race
. *This is associated the race label with these two numeric variables
. tabulate husb wife [fweight=count]
| wife
husb | black Mexican Other His all other White | Total
-----------+-------------------------------------------------------+----------
black | 4,074 63 32 42 215 | 4,426
Mexican | 25 3,947 143 95 1,009 | 5,219
Other Hisp | 16 132 239 18 304 | 709
all others | 19 78 18 1,022 360 | 1,497
White | 103 1,156 373 492 28,453 | 30,577
-----------+-------------------------------------------------------+----------
Total | 4,237 5,376 805 1,669 30,341 | 42,428
. *let's say we want to create an off-diagonal association between white and Mexican
. gen byte assoc=0
. replace assoc=1 if wife=="White" & husb=="Mexican"
type mismatch
r(109);
. replace assoc=1 if (wife==5 & husb==2)| (wife==2 & husb==5)
(2 real changes made)
. table husb wife, contents(sum assoc)
-----------------------------------------------------------------------
| wife
husb | black Mexican Other Hisp all others White
-----------+-----------------------------------------------------------
black | 0 0 0 0 0
Mexican | 0 0 0 0 1
Other Hisp | 0 0 0 0 0
all others | 0 0 0 0 0
White | 0 1 0 0 0
-----------------------------------------------------------------------
. clear all
. edit
(3 vars, 25 obs pasted into editor)
- preserve
. describe
Contains data
obs: 25
vars: 3
size: 650 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
wife str10 %10s
husb str10 %10s
count int %8.0g
-------------------------------------------------------------------------------
Sorted by:
Note: dataset has changed since last saved
. gen byte assoc=0
. replace assoc=1 if (wife=="White" & husb=="Mexican")|(wife=="Mexican" & husb=="White")
(2 real changes made)
. *This is the equivalent way of dealing with string variables
. table husb wife, contents(sum assoc)
-----------------------------------------------------------------------
| wife
husb | All Others Black Mexican Oth Hisp White
-----------+-----------------------------------------------------------
All Others | 0 0 0 0 0
Black | 0 0 0 0 0
Mexican | 0 0 0 0 1
Oth Hisp | 0 0 0 0 0
White | 0 0 1 0 0
-----------------------------------------------------------------------
. clear all
. set mem 50m
* to make room in memory for a bigger dataset
Current memory allocation
current memory usage
settable value description (1M = 1024k)
--------------------------------------------------------------------
set maxvar 5000 max. variables allowed 1.733M
set memory 50M max. data space 50.000M
set matsize 400 max. RHS vars in models 1.254M
-----------
52.987M
. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta", clear
. *On my website (class Soc 180) is a link to a dataset of CPS data from 2000
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 133,710
vars: 42 16 May 2004 11:38
size: 9,894,540 (81.1% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
phseq str5 %9s household sequence number p2
pernum byte %8.0g
age byte %8.0g p15
maritl byte %26.0g marlbl Marital Status p17
sex byte %8.0g sexnm p20
vet byte %22.0g vetnm veteran status p21
hga byte %8.0g Educational Attainment p22
race byte %11.0g racenm p25
reorigin byte %25.0g hisplbl Hispanic Origin p27
hrs1 byte %8.0g hours worked last week p76
clswkr byte %32.0g cwrknm sector of worker p109
grswk int %9.0g gross weekly wages p135
unmem byte %13.0g unnm labor union member p139
lfsr byte %28.0g lfsrnm labor force status p145
ernval float %9.0g main job last year earnings p228
ssval long %12.0g last year soc security payments
p291
pawval int %12.0g last year welfare payments p305
wgt2 int %9.0g rounded weight based on p50
ernval2 float %9.0g main job earnings, losses
recoded to zero
htype byte %37.0g htpnm household type h25
state byte %8.0g HG-ST60, or simply state of
residence h40
hpmsasz byte %16.0g hpmsasz metropolitan area size h56
hcccr byte %12.0g hccrlbl residence in central city h58
frelu18 byte %8.0g number of kids in fam under 18
f29
povll byte %9.0g povll_label
ratio of fam income to poverty
level f38
fwsval float %9.0g family income f48
famwgt2 int %8.0g adjusted family weight f233
yrsed float %9.0g years of education, from hga
citizen byte %33.0g citnm citizenship p733
health byte %11.0g hlthnm self reported health status p800
occ int %8.0g occupation P 106
ptotr byte %8.0g total person income categories
P466
penatvty int %8.0g country of birth P 722,
Appendix H
pemntvty int %8.0g Mother's country of birth,
P725, appendix H
pefntvty int %8.0g Father's country of birth,
P728, appendix H
peinusyr byte %8.0g time of immigration, P 731
pxnatvty byte %8.0g allocation flag for country of
birth P 734
hgmsac int %8.0g metropolitan area code, h44,
appendix E
pppos2 byte %8.0g family sequence number within
each household p46
edlvl byte %16.0g edlabel 4 categories ed attainment
hispanic byte %12.0g smhisplbl
dichotomoy hispanic yes/no
new_race byte %18.0g new_race race and Hispanic combined
-------------------------------------------------------------------------------
Sorted by:
. *let's say we want to make a collapsed dataset of counts using marital status, race, and sex
. tabulate maritl
Marital Status p17 | Freq. Percent Cum.
---------------------------+-----------------------------------
married, spouse present | 55,585 41.57 41.57
married, AF spouse present | 351 0.26 41.83
married, spouse absent | 1,355 1.01 42.85
widowed | 6,561 4.91 47.75
divorced | 9,523 7.12 54.88
separated | 2,097 1.57 56.44
never married | 58,238 43.56 100.00
---------------------------+-----------------------------------
Total | 133,710 100.00
. tabulate maritl, nolab
Marital |
Status p17 | Freq. Percent Cum.
------------+-----------------------------------
1 | 55,585 41.57 41.57
2 | 351 0.26 41.83
3 | 1,355 1.01 42.85
4 | 6,561 4.91 47.75
5 | 9,523 7.12 54.88
6 | 2,097 1.57 56.44
7 | 58,238 43.56 100.00
------------+-----------------------------------
Total | 133,710 100.00
. tabulate race
p25 | Freq. Percent Cum.
------------+-----------------------------------
White | 113,475 84.87 84.87
Black | 13,626 10.19 95.06
Amer Indian | 1,894 1.42 96.47
Asian | 4,715 3.53 100.00
------------+-----------------------------------
Total | 133,710 100.00
. tabulate sex
p20 | Freq. Percent Cum.
------------+-----------------------------------
male | 64,791 48.46 48.46
female | 68,919 51.54 100.00
------------+-----------------------------------
Total | 133,710 100.00
. contract maritl race sex
* we are expecting 56 cells
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 55
vars: 4 16 May 2004 11:38
size: 495 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
maritl byte %26.0g marlbl Marital Status p17
sex byte %8.0g sexnm p20
race byte %11.0g racenm p25
_freq int %12.0g Frequency
-------------------------------------------------------------------------------
Sorted by: maritl race sex
Note: dataset has changed since last saved
. *Note: if you do contract without the appropriate zero option, you will get a dataset that does not include any of the cells that would have had zero count, and you need those cells.
. clear all
. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta", clear
. contract maritl race sex, zero
*The zero option preserves the zero cells in your dataset
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 56
vars: 4 16 May 2004 11:38
size: 504 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
maritl byte %26.0g marlbl Marital Status p17
sex byte %8.0g sexnm p20
race byte %11.0g racenm p25
_freq int %12.0g Frequency
-------------------------------------------------------------------------------
Sorted by: maritl race sex
Note: dataset has changed since last saved
. rename _freq count
. tabulate count
Frequency | Freq. Percent Cum.
------------+-----------------------------------
0 | 1 1.79 1.79
1 | 1 1.79 3.57
2 | 1 1.79 5.36
10 | 2 3.57 8.93
12 | 1 1.79 10.71
14 | 1 1.79 12.50
19 | 1 1.79 14.29
20 | 1 1.79 16.07
22 | 3 5.36 21.43
23 | 1 1.79 23.21
33 | 1 1.79 25.00
35 | 1 1.79 26.79
42 | 1 1.79 28.57
52 | 1 1.79 30.36
58 | 1 1.79 32.14
67 | 1 1.79 33.93
68 | 2 3.57 37.50
83 | 1 1.79 39.29
88 | 1 1.79 41.07
109 | 1 1.79 42.86
136 | 1 1.79 44.64
144 | 1 1.79 46.43
176 | 1 1.79 48.21
264 | 1 1.79 50.00
279 | 1 1.79 51.79
289 | 1 1.79 53.57
331 | 1 1.79 55.36
404 | 1 1.79 57.14
511 | 1 1.79 58.93
527 | 1 1.79 60.71
528 | 1 1.79 62.50
542 | 1 1.79 64.29
582 | 1 1.79 66.07
651 | 1 1.79 67.86
664 | 1 1.79 69.64
916 | 1 1.79 71.43
930 | 1 1.79 73.21
1019 | 1 1.79 75.00
1034 | 1 1.79 76.79
1071 | 1 1.79 78.57
1158 | 1 1.79 80.36
1636 | 1 1.79 82.14
1765 | 1 1.79 83.93
3498 | 1 1.79 85.71
3578 | 1 1.79 87.50
4078 | 1 1.79 89.29
4527 | 1 1.79 91.07
4640 | 1 1.79 92.86
22453 | 1 1.79 94.64
24691 | 1 1.79 96.43
24847 | 1 1.79 98.21
24961 | 1 1.79 100.00
------------+-----------------------------------
Total | 56 100.00
. *the distribution of counts into cells here is not at all bad, only 3 cells with small counts.
. tabulate maritl [fweight=count]
Marital Status p17 | Freq. Percent Cum.
---------------------------+-----------------------------------
married, spouse present | 55,585 41.57 41.57
married, AF spouse present | 351 0.26 41.83
married, spouse absent | 1,355 1.01 42.85
widowed | 6,561 4.91 47.75
divorced | 9,523 7.12 54.88
separated | 2,097 1.57 56.44
never married | 58,238 43.56 100.00
---------------------------+-----------------------------------
Total | 133,710 100.00
. *this is a cross tabulation of marital status, sex, and race for all ages which may not be appropriate.
. clear all
. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta", clear
. contract maritl race sex if age>19 & age<40, zero
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 56
vars: 4 16 May 2004 11:38
size: 504 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
maritl byte %26.0g marlbl Marital Status p17
sex byte %8.0g sexnm p20
race byte %11.0g racenm p25
_freq int %12.0g Frequency
-------------------------------------------------------------------------------
Sorted by: maritl race sex
Note: dataset has changed since last saved
. rename _freq count
. tabulate maritl [fweight=count]
Marital Status p17 | Freq. Percent Cum.
---------------------------+-----------------------------------
married, spouse present | 18,746 49.97 49.97
married, AF spouse present | 265 0.71 50.67
married, spouse absent | 529 1.41 52.08
widowed | 140 0.37 52.46
divorced | 2,665 7.10 59.56
separated | 892 2.38 61.94
never married | 14,280 38.06 100.00
---------------------------+-----------------------------------
Total | 37,517 100.00
. *let's say we were working with this dataset and we decided after a while to collapse the marital status variable further.
. tabulate maritl [fweight=count], nolab
Marital |
Status p17 | Freq. Percent Cum.
------------+-----------------------------------
1 | 18,746 49.97 49.97
2 | 265 0.71 50.67
3 | 529 1.41 52.08
4 | 140 0.37 52.46
5 | 2,665 7.10 59.56
6 | 892 2.38 61.94
7 | 14,280 38.06 100.00
------------+-----------------------------------
Total | 37,517 100.00
. gen byte marital2=maritl
. replace marital2=1 if maritl<4
(16 real changes made)
. tabulate maritl marital2
| marital2
Marital Status p17 | 1 4 5 6 7 | Total
----------------------+-------------------------------------------------------+----------
married, spouse prese | 8 0 0 0 0 | 8
married, AF spouse pr | 8 0 0 0 0 | 8
married, spouse absen | 8 0 0 0 0 | 8
widowed | 0 8 0 0 0 | 8
divorced | 0 0 8 0 0 | 8
separated | 0 0 0 8 0 | 8
never married | 0 0 0 0 8 | 8
----------------------+-------------------------------------------------------+----------
Total | 24 8 8 8 8 | 56
. label define new_mar 1 "married" 4 "widowed" 5 "divorced" 6 "separated" 7 "never married"
. label val marital2 new_mar
. tabulate marital2 [fweight=count]
marital2 | Freq. Percent Cum.
--------------+-----------------------------------
married | 19,540 52.08 52.08
widowed | 140 0.37 52.46
divorced | 2,665 7.10 59.56
separated | 892 2.38 61.94
never married | 14,280 38.06 100.00
--------------+-----------------------------------
Total | 37,517 100.00
. contract marital2 sex race [fweight=count], zero
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta
obs: 40
vars: 4 16 May 2004 11:38
size: 360 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
sex byte %8.0g sexnm p20
race byte %11.0g racenm p25
marital2 byte %13.0g new_mar
_freq int %12.0g Frequency
-------------------------------------------------------------------------------
Sorted by: marital2 sex race
Note: dataset has changed since last saved
. *OK, so that's a twice contracted dataset of marital status, sex and race.
. clear all
. *now a quick look at the HW3 dataset.
. use "C:\AAA Miker Files\newer web pages\soc_388_notes\70-80-90 MR intermar.dta", clear
. *This dataset has 5 variables plus count
. tabulate meth
meth | Freq. Percent Cum.
------------+-----------------------------------
Blk_NH | 45 20.00 20.00
Mex_Am | 45 20.00 40.00
Oth_H | 45 20.00 60.00
Oth_NH | 45 20.00 80.00
Wht_NH | 45 20.00 100.00
------------+-----------------------------------
Total | 225 100.00
. tabulate meth feth [fweight=count]
| feth
meth | Blk_NH Mex_Am Oth_H Oth_NH Wht_NH | Total
-----------+-------------------------------------------------------+----------
Blk_NH | 42,521 291 412 393 2,064 | 45,681
Mex_Am | 94 18,088 612 433 6,067 | 25,294
Oth_H | 310 633 5,901 258 4,507 | 11,609
Oth_NH | 101 317 214 3,509 3,959 | 8,100
Wht_NH | 615 5,338 4,403 5,505 543,276 | 559,137
-----------+-------------------------------------------------------+----------
Total | 43,641 24,667 11,542 10,098 559,873 | 649,821
. tabulate fgen mgen [fweight=count]
| mgen
fgen | 1 2 | Total
-----------+----------------------+----------
1 | 0 19,166 | 19,166
2 | 19,825 610,830 | 630,655
-----------+----------------------+----------
Total | 19,825 629,996 | 649,821
. tabulate year [fweight=count]
year | Freq. Percent Cum.
------------+-----------------------------------
70 | 64,903 9.99 9.99
80 | 348,247 53.59 63.58
90 | 236,671 36.42 100.00
------------+-----------------------------------
Total | 649,821 100.00
. *Three years, 5 racial groups, and 2 levels of nativity for each person but, there is a big structural zero.
. display 5*5*2*2*3
300
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\70-80-90 MR intermar.d
> ta
obs: 225
vars: 8 27 Oct 2003 10:53
size: 6,750 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
meth str8 %9s
feth str9 %9s
mgen byte %8.0g
fgen byte %8.0g
year byte %8.0g
count long %12.0g Frequency
meth_num byte %8.0g racelbl
feth_num byte %8.0g racelbl
-------------------------------------------------------------------------------
Sorted by: year mgen fgen
. display 5*5*3*3
225
. *a brief look at the HW3 dataset.
. clear all
. use "C:\AAA Miker Files\newer web pages\soc_388_notes\Qian 80-90 intermar.dta", clear
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\Qian 80-90 intermar.dt
> a
obs: 512
vars: 6 16 Oct 2001 11:12
size: 10,752 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
mfulleth str5 %9s
med4 byte %8.0g
ffulleth str5 %9s
fed4 byte %8.0g
count long %12.0g COUNT
year byte %8.0g
-------------------------------------------------------------------------------
Sorted by: year med4 fed4
. tabulate mfulleth ffulleth [fweight=count]
| ffulleth
mfulleth | Asian Hisp black white | Total
-----------+--------------------------------------------+----------
Asian | 372 43 7 320 | 742
Hisp | 40 15,469 227 7,189 | 22,925
black | 11 459 34,334 1,625 | 36,429
white | 447 6,744 458 455,797 | 463,446
-----------+--------------------------------------------+----------
Total | 870 22,715 35,026 464,931 | 523,542
. tabulate med4 fed4 [fweight=count]
| fed4
med4 | 1 2 3 4 | Total
-----------+--------------------------------------------+----------
1 | 32,016 33,374 8,407 988 | 74,785
2 | 28,370 137,876 43,783 8,446 | 218,475
3 | 7,051 48,766 61,633 18,195 | 135,645
4 | 984 13,794 28,635 51,224 | 94,637
-----------+--------------------------------------------+----------
Total | 68,421 233,810 142,458 78,853 | 523,542
. tabulate year [fweight=count]
year | Freq. Percent Cum.
------------+-----------------------------------
80 | 315,266 60.22 60.22
90 | 208,276 39.78 100.00
------------+-----------------------------------
Total | 523,542 100.00
. display 4*4*4*4*2
512
. tabulate count
COUNT | Freq. Percent Cum.
------------+-----------------------------------
0 | 102 19.92 19.92
1 | 49 9.57 29.49
2 | 23 4.49 33.98
3 | 27 5.27 39.26
4 | 12 2.34 41.60
5 | 15 2.93 44.53
6 | 11 2.15 46.68
7 | 5 0.98 47.66
8 | 5 0.98 48.63
9 | 2 0.39 49.02
10 | 10 1.95 50.98
11 | 6 1.17 52.15
12 | 8 1.56 53.71
13 | 5 0.98 54.69
14 | 3 0.59 55.27
15 | 3 0.59 55.86
16 | 2 0.39 56.25
17 | 4 0.78 57.03
18 | 3 0.59 57.62
19 | 3 0.59 58.20
21 | 8 1.56 59.77
22 | 2 0.39 60.16
23 | 4 0.78 60.94
24 | 4 0.78 61.72
25 | 2 0.39 62.11
26 | 2 0.39 62.50
27 | 3 0.59 63.09
29 | 1 0.20 63.28
30 | 1 0.20 63.48
31 | 2 0.39 63.87
32 | 4 0.78 64.65
34 | 2 0.39 65.04
35 | 1 0.20 65.23
36 | 3 0.59 65.82
37 | 4 0.78 66.60
38 | 2 0.39 66.99
39 | 2 0.39 67.38
41 | 1 0.20 67.58
42 | 3 0.59 68.16
43 | 3 0.59 68.75
44 | 1 0.20 68.95
45 | 1 0.20 69.14
46 | 1 0.20 69.34
48 | 2 0.39 69.73
50 | 2 0.39 70.12
51 | 1 0.20 70.31
53 | 2 0.39 70.70
59 | 2 0.39 71.09
62 | 2 0.39 71.48
65 | 1 0.20 71.68
71 | 3 0.59 72.27
73 | 1 0.20 72.46
75 | 1 0.20 72.66
76 | 1 0.20 72.85
77 | 1 0.20 73.05
78 | 1 0.20 73.24
85 | 1 0.20 73.44
87 | 1 0.20 73.63
88 | 1 0.20 73.83
89 | 2 0.39 74.22
90 | 1 0.20 74.41
93 | 1 0.20 74.61
94 | 1 0.20 74.80
97 | 1 0.20 75.00
100 | 1 0.20 75.20
102 | 1 0.20 75.39
108 | 1 0.20 75.59
112 | 1 0.20 75.78
115 | 1 0.20 75.98
118 | 1 0.20 76.17
128 | 1 0.20 76.37
130 | 1 0.20 76.56
132 | 1 0.20 76.76
140 | 1 0.20 76.95
141 | 1 0.20 77.15
145 | 1 0.20 77.34
146 | 1 0.20 77.54
148 | 1 0.20 77.73
149 | 1 0.20 77.93
156 | 1 0.20 78.13
157 | 1 0.20 78.32
160 | 1 0.20 78.52
161 | 1 0.20 78.71
163 | 1 0.20 78.91
164 | 1 0.20 79.10
165 | 1 0.20 79.30
187 | 1 0.20 79.49
192 | 1 0.20 79.69
198 | 1 0.20 79.88
204 | 1 0.20 80.08
205 | 1 0.20 80.27
210 | 1 0.20 80.47
216 | 1 0.20 80.66
217 | 1 0.20 80.86
220 | 1 0.20 81.05
222 | 1 0.20 81.25
227 | 1 0.20 81.45
234 | 1 0.20 81.64
235 | 1 0.20 81.84
257 | 2 0.39 82.23
264 | 1 0.20 82.42
283 | 1 0.20 82.62
295 | 1 0.20 82.81
297 | 1 0.20 83.01
303 | 1 0.20 83.20
311 | 1 0.20 83.40
313 | 1 0.20 83.59
324 | 1 0.20 83.79
334 | 1 0.20 83.98
341 | 1 0.20 84.18
343 | 1 0.20 84.38
351 | 1 0.20 84.57
354 | 1 0.20 84.77
367 | 1 0.20 84.96
374 | 1 0.20 85.16
390 | 2 0.39 85.55
394 | 1 0.20 85.74
404 | 1 0.20 85.94
405 | 1 0.20 86.13
419 | 1 0.20 86.33
423 | 1 0.20 86.52
454 | 1 0.20 86.72
468 | 1 0.20 86.91
473 | 1 0.20 87.11
477 | 1 0.20 87.30
497 | 1 0.20 87.50
501 | 1 0.20 87.70
519 | 1 0.20 87.89
547 | 1 0.20 88.09
557 | 1 0.20 88.28
565 | 1 0.20 88.48
572 | 1 0.20 88.67
598 | 1 0.20 88.87
607 | 1 0.20 89.06
624 | 1 0.20 89.26
633 | 1 0.20 89.45
636 | 1 0.20 89.65
660 | 1 0.20 89.84
667 | 1 0.20 90.04
713 | 1 0.20 90.23
782 | 1 0.20 90.43
783 | 1 0.20 90.63
870 | 2 0.39 91.02
1045 | 1 0.20 91.21
1082 | 1 0.20 91.41
1119 | 1 0.20 91.60
1129 | 1 0.20 91.80
1132 | 1 0.20 91.99
1227 | 1 0.20 92.19
1513 | 1 0.20 92.38
1716 | 1 0.20 92.58
1911 | 1 0.20 92.77
2039 | 1 0.20 92.97
2054 | 1 0.20 93.16
2119 | 1 0.20 93.36
2162 | 1 0.20 93.55
2173 | 1 0.20 93.75
2180 | 1 0.20 93.95
2383 | 1 0.20 94.14
2509 | 1 0.20 94.34
2643 | 1 0.20 94.53
2657 | 1 0.20 94.73
2939 | 1 0.20 94.92
2982 | 1 0.20 95.12
3381 | 1 0.20 95.31
4122 | 1 0.20 95.51
4161 | 1 0.20 95.70
6734 | 1 0.20 95.90
7868 | 2 0.39 96.29
8301 | 1 0.20 96.48
9380 | 1 0.20 96.68
9821 | 1 0.20 96.88
10095 | 1 0.20 97.07
10142 | 1 0.20 97.27
15539 | 1 0.20 97.46
15601 | 1 0.20 97.66
15801 | 1 0.20 97.85
16340 | 1 0.20 98.05
17604 | 1 0.20 98.24
18173 | 1 0.20 98.44
19526 | 1 0.20 98.63
20444 | 1 0.20 98.83
24167 | 1 0.20 99.02
27314 | 1 0.20 99.22
27573 | 1 0.20 99.41
29109 | 1 0.20 99.61
39467 | 1 0.20 99.80
81301 | 1 0.20 100.00
------------+-----------------------------------
Total | 512 100.00
. *so 102 of the 512 cells are zero. Is that a problem, and if so how would we find out
. * one way to think about this dataset is that it is 32 separate 4x4 ethnicity tables
. *what if we tried to fit the marginals of all 32 of those racexrace tables.
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*med4*fed4*year, verbose
Desmat generated the following design matrix:
nr Variables Term Parameterization
First Last
1 _x_1 _x_3 mfulleth ind(1)
2 _x_4 _x_6 med4 ind(1)
3 _x_7 _x_15 mfulleth.med4 ind(1).ind(1)
4 _x_16 _x_18 fed4 ind(1)
5 _x_19 _x_27 mfulleth.fed4 ind(1).ind(1)
6 _x_28 _x_36 med4.fed4 ind(1).ind(1)
7 _x_37 _x_63 mfulleth.med4.fed4 ind(1).ind(1).ind(1)
8 _x_64 year ind(80)
9 _x_65 _x_67 mfulleth.year ind(1).ind(80)
10 _x_68 _x_70 med4.year ind(1).ind(80)
11 _x_71 _x_79 mfulleth.med4.year ind(1).ind(1).ind(80)
12 _x_80 _x_82 fed4.year ind(1).ind(80)
13 _x_83 _x_91 mfulleth.fed4.year ind(1).ind(1).ind(80)
14 _x_92 _x_99 med4.fed4.year ind(1).ind(1).ind(80)
15 _x_100 _x_126 mfulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80)
16 _x_127 _x_129 ffulleth ind(1)
17 _x_130 _x_138 ffulleth.med4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.fed4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.med4.fed4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.med4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.fed4.year ind(1).ind(1).ind(80)
23 _x_196 med4.fed4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80)
Iteration 0: log likelihood = -10182718 (not concave)
Iteration 1: log likelihood = -9775409 (not concave)
Iteration 2: log likelihood = -8993376.6 (not concave)
Iteration 3: log likelihood = -8091654.2 (not concave)
Iteration 4: log likelihood = -7444357.8 (not concave)
Iteration 5: log likelihood = -6491615.2 (not concave)
Iteration 6: log likelihood = -5455862.1 (not concave)
Iteration 7: log likelihood = -4592233.3 (not concave)
Iteration 8: log likelihood = -3909283.7 (not concave)
Iteration 9: log likelihood = -3417118.4 (not concave)
Iteration 10: log likelihood = -2973957 (not concave)
Iteration 11: log likelihood = -2783195 (not concave)
Iteration 12: log likelihood = -2592428.7 (not concave)
Iteration 13: log likelihood = -2221113.9 (not concave)
Iteration 14: log likelihood = -2052404.7 (not concave)
Iteration 15: log likelihood = -1754366.1 (not concave)
Iteration 16: log likelihood = -1604776.6 (not concave)
Iteration 17: log likelihood = -1483631.8 (not concave)
Iteration 18: log likelihood = -1343375 (not concave)
Iteration 19: log likelihood = -1247741.1 (not concave)
Iteration 20: log likelihood = -1161997 (not concave)
Iteration 21: log likelihood = -1072588.2 (not concave)
Iteration 22: log likelihood = -970211.93 (not concave)
Iteration 23: log likelihood = -924405.15 (not concave)
Iteration 24: log likelihood = -870858.9 (not concave)
Iteration 25: log likelihood = -808624.74 (not concave)
Iteration 26: log likelihood = -777813.6 (not concave)
Iteration 27: log likelihood = -736661.37
Iteration 28: log likelihood = -714424.98 (backed up)
Iteration 29: log likelihood = -585481.63 (backed up)
Iteration 30: log likelihood = -271160.25 (backed up)
Iteration 31: log likelihood = -191025.55
Iteration 32: log likelihood = -160267.66
Iteration 33: log likelihood = -155976.88
Iteration 34: log likelihood = -155783.44
Iteration 35: log likelihood = -155780.43 (not concave)
Iteration 36: log likelihood = -155780.43 (not concave)
Iteration 37: log likelihood = -155780.43 (not concave)
Iteration 38: log likelihood = -155780.43 (not concave)
Iteration 39: log likelihood = -155780.43 (not concave)
Iteration 40: log likelihood = -155780.42 (not concave)
Iteration 41: log likelihood = -155780.42 (not concave)
Iteration 42: log likelihood = -155780.42 (not concave)
Iteration 43: log likelihood = -155780.42 (not concave)
Iteration 44: log likelihood = -155780.42 (not concave)
Iteration 45: log likelihood = -155780.42 (not concave)
Iteration 46: log likelihood = -155780.42 (not concave)
Iteration 47: log likelihood = -155780.42 (not concave)
Iteration 48: log likelihood = -155780.42 (not concave)
Iteration 49: log likelihood = -155780.42 (not concave)
Iteration 50: log likelihood = -155780.42 (not concave)
Iteration 51: log likelihood = -155780.42 (not concave)
Iteration 52: log likelihood = -155780.42 (not concave)
Iteration 53: log likelihood = -155780.42 (not concave)
Iteration 54: log likelihood = -155780.42 (not concave)
Iteration 55: log likelihood = -155780.42 (not concave)
Iteration 56: log likelihood = -155780.42 (not concave)
Iteration 57: log likelihood = -155780.42 (not concave)
Iteration 58: log likelihood = -155780.42 (not concave)
Iteration 59: log likelihood = -155780.42 (not concave)
Iteration 60: log likelihood = -155780.42 (not concave)
Iteration 61: log likelihood = -155780.42 (not concave)
Iteration 62: log likelihood = -155780.42 (not concave)
Iteration 63: log likelihood = -155780.42 (not concave)
Iteration 64: log likelihood = -155780.42 (not concave)
Iteration 65: log likelihood = -155780.42 (not concave)
Iteration 66: log likelihood = -155780.42 (not concave)
Iteration 67: log likelihood = -155780.42 (not concave)
Iteration 68: log likelihood = -155780.42 (not concave)
Iteration 69: log likelihood = -155780.42 (not concave)
Iteration 70: log likelihood = -155780.42 (not concave)
Iteration 71: log likelihood = -155780.42 (not concave)
Hessian has become unstable or asymmetric
--Break--
r(1);
. *That didn't seem to want to converge
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*med4*fed4*year, difficult verbose
Desmat generated the following design matrix:
nr Variables Term Parameterization
First Last
1 _x_1 _x_3 mfulleth ind(1)
2 _x_4 _x_6 med4 ind(1)
3 _x_7 _x_15 mfulleth.med4 ind(1).ind(1)
4 _x_16 _x_18 fed4 ind(1)
5 _x_19 _x_27 mfulleth.fed4 ind(1).ind(1)
6 _x_28 _x_36 med4.fed4 ind(1).ind(1)
7 _x_37 _x_63 mfulleth.med4.fed4 ind(1).ind(1).ind(1)
8 _x_64 year ind(80)
9 _x_65 _x_67 mfulleth.year ind(1).ind(80)
10 _x_68 _x_70 med4.year ind(1).ind(80)
11 _x_71 _x_79 mfulleth.med4.year ind(1).ind(1).ind(80)
12 _x_80 _x_82 fed4.year ind(1).ind(80)
13 _x_83 _x_91 mfulleth.fed4.year ind(1).ind(1).ind(80)
14 _x_92 _x_99 med4.fed4.year ind(1).ind(1).ind(80)
15 _x_100 _x_126 mfulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80)
16 _x_127 _x_129 ffulleth ind(1)
17 _x_130 _x_138 ffulleth.med4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.fed4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.med4.fed4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.med4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.fed4.year ind(1).ind(1).ind(80)
23 _x_196 med4.fed4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80)
Iteration 0: log likelihood = -10182718 (not concave)
Iteration 1: log likelihood = -6841124.2 (not concave)
Iteration 2: log likelihood = -3459967.5 (not concave)
Iteration 3: log likelihood = -2547632.2
Iteration 4: log likelihood = -2460816.8 (backed up)
Iteration 5: log likelihood = -1708263.2 (backed up)
Iteration 6: log likelihood = -709252.76
Iteration 7: log likelihood = -321262.59
Iteration 8: log likelihood = -186966.3
Iteration 9: log likelihood = -158834.18
Iteration 10: log likelihood = -156262
Iteration 11: log likelihood = -155810.93
Iteration 12: log likelihood = -155780.82 (not concave)
Iteration 13: log likelihood = -155780.42 (not concave)
Iteration 14: log likelihood = -155780.42 (not concave)
Iteration 15: log likelihood = -155780.42 (not concave)
Iteration 16: log likelihood = -155780.42 (not concave)
Iteration 17: log likelihood = -155780.42 (not concave)
Iteration 18: log likelihood = -155780.42 (not concave)
Iteration 19: log likelihood = -155780.42 (not concave)
Iteration 20: log likelihood = -155780.42 (not concave)
Iteration 21: log likelihood = -155780.42 (not concave)
Iteration 22: log likelihood = -155780.42 (not concave)
Iteration 23: log likelihood = -155780.42 (not concave)
Iteration 24: log likelihood = -155780.42 (not concave)
Iteration 25: log likelihood = -155780.42 (not concave)
Iteration 26: log likelihood = -155780.42 (not concave)
Iteration 27: log likelihood = -155780.42 (not concave)
Iteration 28: log likelihood = -155780.42 (not concave)
Iteration 29: log likelihood = -155780.42 (not concave)
Iteration 30: log likelihood = -155780.42 (not concave)
Iteration 31: log likelihood = -155780.42 (not concave)
Iteration 32: log likelihood = -155780.42 (not concave)
Iteration 33: log likelihood = -155780.42 (not concave)
Iteration 34: log likelihood = -155780.42 (not concave)
Iteration 35: log likelihood = -155780.42 (not concave)
Hessian has become unstable or asymmetric (D2)
--Break--
r(1);
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*med4*fed4*year, difficult verbose
> iterate(30)
Desmat generated the following design matrix:
nr Variables Term Parameterization
First Last
1 _x_1 _x_3 mfulleth ind(1)
2 _x_4 _x_6 med4 ind(1)
3 _x_7 _x_15 mfulleth.med4 ind(1).ind(1)
4 _x_16 _x_18 fed4 ind(1)
5 _x_19 _x_27 mfulleth.fed4 ind(1).ind(1)
6 _x_28 _x_36 med4.fed4 ind(1).ind(1)
7 _x_37 _x_63 mfulleth.med4.fed4 ind(1).ind(1).ind(1)
8 _x_64 year ind(80)
9 _x_65 _x_67 mfulleth.year ind(1).ind(80)
10 _x_68 _x_70 med4.year ind(1).ind(80)
11 _x_71 _x_79 mfulleth.med4.year ind(1).ind(1).ind(80)
12 _x_80 _x_82 fed4.year ind(1).ind(80)
13 _x_83 _x_91 mfulleth.fed4.year ind(1).ind(1).ind(80)
14 _x_92 _x_99 med4.fed4.year ind(1).ind(1).ind(80)
15 _x_100 _x_126 mfulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80)
16 _x_127 _x_129 ffulleth ind(1)
17 _x_130 _x_138 ffulleth.med4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.fed4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.med4.fed4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.med4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.fed4.year ind(1).ind(1).ind(80)
23 _x_196 med4.fed4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.med4.fed4.year ind(1).ind(1).ind(1).ind(80)
Iteration 0: log likelihood = -10182718 (not concave)
Iteration 1: log likelihood = -6841124.2 (not concave)
Iteration 2: log likelihood = -3459967.5 (not concave)
Iteration 3: log likelihood = -2547632.2
Iteration 4: log likelihood = -2460816.8 (backed up)
Iteration 5: log likelihood = -1708263.2 (backed up)
Iteration 6: log likelihood = -709252.76
Iteration 7: log likelihood = -321262.59
Iteration 8: log likelihood = -186966.3
Iteration 9: log likelihood = -158834.18
Iteration 10: log likelihood = -156262
Iteration 11: log likelihood = -155810.93
Iteration 12: log likelihood = -155780.82 (not concave)
Iteration 13: log likelihood = -155780.42 (not concave)
Iteration 14: log likelihood = -155780.42 (not concave)
Iteration 15: log likelihood = -155780.42 (not concave)
Iteration 16: log likelihood = -155780.42 (not concave)
Iteration 17: log likelihood = -155780.42 (not concave)
Iteration 18: log likelihood = -155780.42 (not concave)
Iteration 19: log likelihood = -155780.42 (not concave)
Iteration 20: log likelihood = -155780.42 (not concave)
Iteration 21: log likelihood = -155780.42 (not concave)
Iteration 22: log likelihood = -155780.42 (not concave)
Iteration 23: log likelihood = -155780.42 (not concave)
Iteration 24: log likelihood = -155780.42 (not concave)
Iteration 25: log likelihood = -155780.42 (not concave)
Iteration 26: log likelihood = -155780.42 (not concave)
Iteration 27: log likelihood = -155780.42 (not concave)
Iteration 28: log likelihood = -155780.42 (not concave)
Iteration 29: log likelihood = -155780.42 (not concave)
Iteration 30: log likelihood = -155780.42 (not concave)
convergence not achieved
Poisson regression Number of obs = 512
LR chi2(223) = 2493255.73
Prob > chi2 = 0.0000
Log likelihood = -155780.42 Pseudo R2 = 0.8889
------------------------------------------------------------------------------
count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_x_1 | 6.739214 .5776554 11.67 0.000 5.60703 7.871397
_x_2 | 6.557602 .5777234 11.35 0.000 5.425285 7.689919
_x_3 | 8.580608 .577368 14.86 0.000 7.448988 9.712229
_x_4 | .6735293 .8543125 0.79 0.430 -1.000892 2.347951
_x_5 | 1.433788 .9593588 1.49 0.135 -.4465206 3.314097
_x_6 | .2001297 1.562649 0.13 0.898 -2.862606 3.262865
_x_7 | -1.410074 .6907378 -2.04 0.041 -2.763895 -.0562526
_x_8 | -2.812612 .6923978 -4.06 0.000 -4.169687 -1.455537
_x_9 | -3.075547 1.165945 -2.64 0.008 -5.360757 -.790337
_x_10 | -1.00214 .6906932 -1.45 0.147 -2.355874 .3515938
_x_11 | -2.496765 .692199 -3.61 0.000 -3.853451 -1.14008
_x_12 | -2.568513 1.16292 -2.21 0.027 -4.847795 -.2892316
_x_13 | -.8567228 .6900419 -1.24 0.214 -2.20918 .4957345
_x_14 | -2.501591 .6903574 -3.62 0.000 -3.854667 -1.148516
_x_15 | -2.303862 1.155567 -1.99 0.046 -4.56873 -.0389928
_x_16 | 1.157131 .8096192 1.43 0.153 -.4296939 2.743955
_x_17 | -12.74375 275.1082 -0.05 0.963 -551.9459 526.4584
_x_18 | 1.540977 1.332387 1.16 0.247 -1.070453 4.152407
_x_19 | -1.741824 .6590744 -2.64 0.008 -3.033587 -.4500624
_x_20 | 10.11187 275.1062 0.04 0.971 -529.0864 549.3101
_x_21 | -3.184302 1.166967 -2.73 0.006 -5.471515 -.8970888
_x_22 | -1.142659 .658959 -1.73 0.083 -2.434195 .1488771
_x_23 | 11.10965 275.1062 0.04 0.968 -528.0886 550.3079
_x_24 | -2.176009 1.160094 -1.88 0.061 -4.449752 .0977343
_x_25 | -1.092474 .6583521 -1.66 0.097 -2.38282 .1978727
_x_26 | 10.4999 275.1062 0.04 0.970 -528.6983 549.6981
_x_27 | -2.654114 1.155671 -2.30 0.022 -4.919187 -.389041
_x_28 | 1.931469 .9843847 1.96 0.050 .0021106 3.860828
_x_29 | 15.64481 275.1088 0.06 0.955 -523.5586 554.8482
_x_30 | -.704067 1.566435 -0.45 0.653 -3.774223 2.366089
_x_31 | 1.220087 1.085462 1.12 0.261 -.9073803 3.347554
_x_32 | 16.61625 275.1091 0.06 0.952 -522.5877 555.8202
_x_33 | 1.813136 1.517946 1.19 0.232 -1.161984 4.788256
_x_34 | 1.15224 1.678289 0.69 0.492 -2.137146 4.441627
_x_35 | 17.7352 275.1119 0.06 0.949 -521.4742 556.9446
_x_36 | 4.670058 1.945732 2.40 0.016 .8564931 8.483622
_x_37 | .47736 .7710298 0.62 0.536 -1.033831 1.988551
_x_38 | -12.11675 275.1065 -0.04 0.965 -551.3156 527.0821
_x_39 | .9126505 1.329394 0.69 0.492 -1.692914 3.518215
_x_40 | 1.273097 .7783926 1.64 0.102 -.2525245 2.798718
_x_41 | -11.53684 275.1065 -0.04 0.967 -550.7356 527.6619
_x_42 | 1.225637 1.242237 0.99 0.324 -1.209103 3.660377
_x_43 | .671039 1.231539 0.54 0.586 -1.742733 3.084811
_x_44 | -12.15059 275.1081 -0.04 0.965 -551.3525 527.0514
_x_45 | .5949681 1.548291 0.38 0.701 -2.439626 3.629562
_x_46 | .3336975 .77075 0.43 0.665 -1.176945 1.84434
_x_47 | -12.2891 275.1065 -0.04 0.964 -551.488 526.9098
_x_48 | 1.282746 1.319756 0.97 0.331 -1.303928 3.869419
_x_49 | 1.019007 .7778749 1.31 0.190 -.5055999 2.543614
_x_50 | -11.86587 275.1065 -0.04 0.966 -551.0646 527.3329
_x_51 | 1.1933 1.234455 0.97 0.334 -1.226187 3.612786
_x_52 | .1778249 1.228021 0.14 0.885 -2.229051 2.584701
_x_53 | -12.67975 275.1081 -0.05 0.963 -551.8817 526.5221
_x_54 | .2472903 1.540308 0.16 0.872 -2.771658 3.266239
_x_55 | .5781241 .7698293 0.75 0.453 -.9307136 2.086962
_x_56 | -11.69581 275.1065 -0.04 0.966 -550.8947 527.503
_x_57 | 1.88932 1.314875 1.44 0.151 -.6877877 4.466428
_x_58 | 1.555406 .7756949 2.01 0.045 .0350717 3.07574
_x_59 | -10.89851 275.1065 -0.04 0.968 -550.0973 528.3003
_x_60 | 2.146847 1.228893 1.75 0.081 -.2617398 4.555434
_x_61 | 1.077707 1.219893 0.88 0.377 -1.313239 3.468653
_x_62 | -11.21994 275.108 -0.04 0.967 -550.4218 527.9819
_x_63 | 1.67762 1.531269 1.10 0.273 -1.323613 4.678852
_x_64 | .2965018 .9454412 0.31 0.754 -1.556529 2.149532
_x_65 | -1.15531 .7645192 -1.51 0.131 -2.65374 .3431204
_x_66 | -1.31111 .764817 -1.71 0.086 -2.810123 .1879041
_x_67 | -.803033 .7637561 -1.05 0.293 -2.299968 .6939015
_x_68 | .088824 1.233853 0.07 0.943 -2.329484 2.507132
_x_69 | -1.092008 1.455841 -0.75 0.453 -3.945404 1.761387
_x_70 | -25.32289 . . . . .
_x_71 | .3107625 .9340794 0.33 0.739 -1.519999 2.141524
_x_72 | 2.015666 1.110905 1.81 0.070 -.1616687 4.193
_x_73 | 12.8992 423.6387 0.03 0.976 -817.4173 843.2157
_x_74 | .2272532 .9342594 0.24 0.808 -1.603862 2.058368
_x_75 | 2.016177 1.110991 1.81 0.070 -.161325 4.193679
_x_76 | 13.53192 423.6386 0.03 0.975 -816.7845 843.8483
_x_77 | .1239621 .9323202 0.13 0.894 -1.703352 1.951276
_x_78 | 1.950056 1.107543 1.76 0.078 -.2206882 4.120801
_x_79 | 13.12464 423.6385 0.03 0.975 -817.1916 843.4409
_x_80 | -2.801759 1.450748 -1.93 0.053 -5.645173 .0416545
_x_81 | 12.37076 275.1111 0.04 0.964 -526.8372 551.5787
_x_82 | -23.42365 1.172158 -19.98 0.000 -25.72104 -21.12626
_x_83 | 2.137456 1.089391 1.96 0.050 .0022883 4.272624
_x_84 | -9.910379 275.1085 -0.04 0.971 -549.1131 529.2923
_x_85 | 11.77379 173.7214 0.07 0.946 -328.7139 352.2615
_x_86 | 1.727232 1.089587 1.59 0.113 -.4083206 3.862784
_x_87 | -10.31977 275.1085 -0.04 0.970 -549.5224 528.8829
_x_88 | 11.70905 173.7213 0.07 0.946 -328.7785 352.1966
_x_89 | 1.8623 1.088034 1.71 0.087 -.2702085 3.994808
_x_90 | -9.934236 275.1085 -0.04 0.971 -549.1369 529.2684
_x_91 | 11.93334 173.7212 0.07 0.945 -328.554 352.4207
_x_92 | .3142696 1.696488 0.19 0.853 -3.010786 3.639326
_x_93 | 16.05559 15.70191 1.02 0.307 -14.71958 46.83077
_x_94 | 1.721069 1.881338 0.91 0.360 -1.966285 5.408424
_x_95 | -13.15201 275.1135 -0.05 0.962 -552.3645 526.0605
_x_96 | 23.0936 . . . . .
_x_97 | 27.19157 1.348593 20.16 0.000 24.54838 29.83476
_x_98 | 10.88125 275.1097 0.04 0.968 -528.3239 550.0864
_x_99 | 47.94669 1.516743 31.61 0.000 44.97392 50.91945
_x_100 | -.5044849 1.250378 -0.40 0.687 -2.955181 1.946211
_x_101 | 11.61097 275.1092 0.04 0.966 -527.5932 550.8151
_x_102 | -4.133055 174.4238 -0.02 0.981 -345.9973 337.7312
_x_103 | -2.620291 1.387815 -1.89 0.059 -5.340358 .099776
_x_104 | 9.965784 275.1097 0.04 0.971 -529.2393 549.1709
_x_105 | -11.96693 173.7215 -0.07 0.945 -352.4548 328.5209
_x_106 | -14.40882 423.6396 -0.03 0.973 -844.7271 815.9095
_x_107 | -.8814283 505.1277 -0.00 0.999 -990.9135 989.1506
_x_108 | -23.34396 457.874 -0.05 0.959 -920.7605 874.0725
_x_109 | -.0777722 1.250364 -0.06 0.950 -2.52844 2.372896
_x_110 | 11.84729 275.1092 0.04 0.966 -527.3568 551.0514
_x_111 | -4.387734 174.4236 -0.03 0.980 -346.2517 337.4763
_x_112 | -2.257602 1.387648 -1.63 0.104 -4.977342 .462137
_x_113 | 10.20885 275.1097 0.04 0.970 -528.9963 549.414
_x_114 | -12.07444 173.7214 -0.07 0.945 -352.5621 328.4132
_x_115 | -14.53484 423.6395 -0.03 0.973 -844.853 815.7834
_x_116 | -1.372811 505.1276 -0.00 0.998 -991.4048 988.6591
_x_117 | -24.28447 457.8739 -0.05 0.958 -921.7008 873.1318
_x_118 | -.4327116 1.248027 -0.35 0.729 -2.8788 2.013377
_x_119 | 11.41972 275.1092 0.04 0.967 -527.7844 550.6238
_x_120 | -4.576643 174.4235 -0.03 0.979 -346.4404 337.2871
_x_121 | -2.648232 1.383743 -1.91 0.056 -5.360318 .0638544
_x_122 | 9.559564 275.1097 0.03 0.972 -529.6455 548.7646
_x_123 | -12.55421 173.7213 -0.07 0.942 -353.0416 327.9332
_x_124 | -14.64146 423.6394 -0.03 0.972 -844.9595 815.6766
_x_125 | -1.646477 505.1276 -0.00 0.997 -991.6783 988.3854
_x_126 | -24.12218 457.8738 -0.05 0.958 -921.5383 873.294
_x_127 | 5.572793 .3339315 16.69 0.000 4.918299 6.227287
_x_128 | 5.434971 .3340249 16.27 0.000 4.780294 6.089648
_x_129 | 7.494966 .3333915 22.48 0.000 6.841531 8.148401
_x_130 | -.249966 .505013 -0.49 0.621 -1.239773 .7398412
_x_131 | -.8247876 .6691725 -1.23 0.218 -2.136342 .4867665
_x_132 | -2.018409 1.067298 -1.89 0.059 -4.110274 .0734564
_x_133 | .0800015 .5049541 0.16 0.874 -.9096904 1.069693
_x_134 | -.5985101 .6690363 -0.89 0.371 -1.909797 .712777
_x_135 | -1.39288 1.062148 -1.31 0.190 -3.474653 .6888921
_x_136 | .2347746 .5040274 0.47 0.641 -.753101 1.22265
_x_137 | -.5555331 .6669815 -0.83 0.405 -1.862793 .7517266
_x_138 | -1.217401 1.054547 -1.15 0.248 -3.284275 .8494722
_x_139 | -.5460862 .4726746 -1.16 0.248 -1.472511 .380339
_x_140 | -.0502673 1.056681 -0.05 0.962 -2.121324 2.020789
_x_141 | -3.08763 .6875339 -4.49 0.000 -4.435172 -1.740088
_x_142 | .0651069 .4724496 0.14 0.890 -.8608773 .9910911
_x_143 | .974622 1.055597 0.92 0.356 -1.094309 3.043553
_x_144 | -2.163883 .6764953 -3.20 0.001 -3.489789 -.8379762
_x_145 | .1076816 .4715793 0.23 0.819 -.8165968 1.03196
_x_146 | .3674395 1.054798 0.35 0.728 -1.699927 2.434806
_x_147 | -2.671749 .6687645 -4.00 0.000 -3.982504 -1.360995
_x_148 | -1.061764 .6155161 -1.72 0.085 -2.268154 .1446252
_x_149 | -2.254117 1.135099 -1.99 0.047 -4.478871 -.0293638
_x_150 | .2733737 .8779151 0.31 0.756 -1.447308 1.994056
_x_151 | -.9355232 .7630666 -1.23 0.220 -2.431106 .5600598
_x_152 | -2.235824 1.21045 -1.85 0.065 -4.608262 .1366142
_x_153 | -.0773814 .9138849 -0.08 0.933 -1.868563 1.7138
_x_154 | .4468043 1.168605 0.38 0.702 -1.843619 2.737228
_x_155 | -1.764996 1.470468 -1.20 0.230 -4.64706 1.117068
_x_156 | .3005016 1.228406 0.24 0.807 -2.10713 2.708133
_x_157 | -1.206091 .6151332 -1.96 0.050 -2.41173 -.0004525
_x_158 | -2.456553 1.133665 -2.17 0.030 -4.678495 -.2346109
_x_159 | .7265817 .8639152 0.84 0.400 -.966661 2.419824
_x_160 | -1.159277 .762561 -1.52 0.128 -2.65387 .3353146
_x_161 | -2.592664 1.20919 -2.14 0.032 -4.962632 -.2226963
_x_162 | .0000595 .9038369 0.00 1.000 -1.771428 1.771547
_x_163 | -.4212615 1.163475 -0.36 0.717 -2.70163 1.859107
_x_164 | -2.661676 1.465508 -1.82 0.069 -5.53402 .2106681
_x_165 | -.0392556 1.217 -0.03 0.974 -2.424533 2.346022
_x_166 | -.9689765 .6139386 -1.58 0.114 -2.172274 .2343211
_x_167 | -1.882035 1.132508 -1.66 0.097 -4.101709 .3376401
_x_168 | 1.341926 .8563021 1.57 0.117 -.3363958 3.020247
_x_169 | -.6388991 .7601814 -0.84 0.401 -2.128827 .8510291
_x_170 | -1.669815 1.20736 -1.38 0.167 -4.036197 .6965664
_x_171 | .9435724 .8959998 1.05 0.292 -.812555 2.6997
_x_172 | .6249045 1.155205 0.54 0.589 -1.639256 2.889065
_x_173 | -1.10452 1.459097 -0.76 0.449 -3.964297 1.755257
_x_174 | 1.500489 1.205868 1.24 0.213 -.8629686 3.863947
_x_175 | -.2393688 .5587747 -0.43 0.668 -1.334547 .8558094
_x_176 | -.4512211 .5591916 -0.81 0.420 -1.547216 .6447742
_x_177 | .0646195 .5576814 0.12 0.908 -1.028416 1.157655
_x_178 | -.2575769 .8108739 -0.32 0.751 -1.846861 1.331707
_x_179 | -.3687733 .9500873 -0.39 0.698 -2.23091 1.493364
_x_180 | 12.60478 423.6386 0.03 0.976 -817.7117 842.9212
_x_181 | -.319787 .8111706 -0.39 0.693 -1.909652 1.270078
_x_182 | -.3955505 .9505434 -0.42 0.677 -2.258581 1.46748
_x_183 | 12.44671 423.6386 0.03 0.977 -817.8697 842.7631
_x_184 | -.4084816 .8088144 -0.51 0.614 -1.993729 1.176765
_x_185 | -.4677467 .9461801 -0.49 0.621 -2.322226 1.386732
_x_186 | 12.12641 423.6385 0.03 0.977 -818.1899 842.4427
_x_187 | 1.105899 .9617356 1.15 0.250 -.7790681 2.990866
_x_188 | -1.380755 1.219589 -1.13 0.258 -3.771106 1.009596
_x_189 | 11.66544 173.7215 0.07 0.946 -328.8224 352.1533
_x_190 | .7307209 .9619211 0.76 0.447 -1.15461 2.616052
_x_191 | -1.74089 1.218442 -1.43 0.153 -4.128992 .6472133
_x_192 | 11.98519 173.7214 0.07 0.945 -328.5024 352.4728
_x_193 | .8876934 .9600832 0.92 0.355 -.9940351 2.769422
_x_194 | -1.365096 1.216319 -1.12 0.262 -3.749037 1.018845
_x_195 | 12.20837 173.7213 0.07 0.944 -328.2791 352.6958
_x_196 | -14.14774 275.1126 -0.05 0.959 -553.3586 525.0631
_x_197 | .1074339 1.152445 0.09 0.926 -2.151317 2.366184
_x_198 | 2.625051 1.382819 1.90 0.058 -.0852245 5.335326
_x_199 | -10.85594 173.7236 -0.06 0.950 -351.3478 329.636
_x_200 | .5331404 1.280352 0.42 0.677 -1.976303 3.042584
_x_201 | 3.152323 1.454816 2.17 0.030 .3009352 6.003711
_x_202 | -10.44753 173.7215 -0.06 0.952 -350.9355 330.0404
_x_203 | -13.60577 423.6399 -0.03 0.974 -843.9247 816.7131
_x_204 | -10.02741 423.6401 -0.02 0.981 -840.3467 820.2919
_x_205 | -23.55659 457.8742 -0.05 0.959 -920.9736 873.8604
_x_206 | .4777504 1.152473 0.41 0.678 -1.781055 2.736556
_x_207 | 2.838718 1.381321 2.06 0.040 .1313791 5.546056
_x_208 | -11.51247 173.7234 -0.07 0.947 -352.004 328.9791
_x_209 | .8746885 1.280427 0.68 0.495 -1.634903 3.38428
_x_210 | 3.400048 1.453738 2.34 0.019 .5507746 6.249321
_x_211 | -10.78576 173.7214 -0.06 0.950 -351.2734 329.7019
_x_212 | -12.91522 423.6399 -0.03 0.976 -843.2341 817.4037
_x_213 | -9.931023 423.6401 -0.02 0.981 -840.2503 820.3882
_x_214 | -24.38869 457.8742 -0.05 0.958 -921.8056 873.0282
_x_215 | .097637 1.149805 0.08 0.932 -2.155939 2.351213
_x_216 | 2.398616 1.378308 1.74 0.082 -.3028174 5.10005
_x_217 | -11.73673 173.7232 -0.07 0.946 -352.228 328.7546
_x_218 | .4857974 1.27587 0.38 0.703 -2.014863 2.986458
_x_219 | 2.773828 1.449399 1.91 0.056 -.0669417 5.614598
_x_220 | -11.24046 173.7213 -0.06 0.948 -351.7279 329.2469
_x_221 | -13.18605 423.6398 -0.03 0.975 -843.5048 817.1327
_x_222 | -10.26651 423.64 -0.02 0.981 -840.5856 820.0526
_x_223 | -24.31086 457.8741 -0.05 0.958 -921.7276 873.1058
_cons | -6.638627 .666581 -9.96 0.000 -7.945102 -5.332152
------------------------------------------------------------------------------
------------------------------------------------------------------------------------------
Poisson regression
------------------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -155780.421
LR chi square: 2493255.730
Model degrees of freedom: 223
Pseudo R-squared: 0.889
Prob: 0.000
------------------------------------------------------------------------------------------
nr Effect Coeff s.e.
------------------------------------------------------------------------------------------
count
mfulleth
1 Hisp 6.739** 0.578
2 black 6.558** 0.578
3 white 8.581** 0.577
med4
4 2 0.674 0.854
5 3 1.434 0.959
6 4 0.200 1.563
mfulleth.med4
7 Hisp.2 -1.410* 0.691
8 Hisp.3 -2.813** 0.692
9 Hisp.4 -3.076** 1.166
10 black.2 -1.002 0.691
11 black.3 -2.497** 0.692
12 black.4 -2.569* 1.163
13 white.2 -0.857 0.690
14 white.3 -2.502** 0.690
15 white.4 -2.304* 1.156
fed4
16 2 1.157 0.810
17 3 -12.744 275.108
18 4 1.541 1.332
mfulleth.fed4
19 Hisp.2 -1.742** 0.659
20 Hisp.3 10.112 275.106
21 Hisp.4 -3.184** 1.167
22 black.2 -1.143 0.659
23 black.3 11.110 275.106
24 black.4 -2.176 1.160
25 white.2 -1.092 0.658
26 white.3 10.500 275.106
27 white.4 -2.654* 1.156
med4.fed4
28 2.2 1.931* 0.984
29 2.3 15.645 275.109
30 2.4 -0.704 1.566
31 3.2 1.220 1.085
32 3.3 16.616 275.109
33 3.4 1.813 1.518
34 4.2 1.152 1.678
35 4.3 17.735 275.112
36 4.4 4.670* 1.946
mfulleth.med4.fed4
37 Hisp.2.2 0.477 0.771
38 Hisp.2.3 -12.117 275.107
39 Hisp.2.4 0.913 1.329
40 Hisp.3.2 1.273 0.778
41 Hisp.3.3 -11.537 275.106
42 Hisp.3.4 1.226 1.242
43 Hisp.4.2 0.671 1.232
44 Hisp.4.3 -12.151 275.108
45 Hisp.4.4 0.595 1.548
46 black.2.2 0.334 0.771
47 black.2.3 -12.289 275.107
48 black.2.4 1.283 1.320
49 black.3.2 1.019 0.778
50 black.3.3 -11.866 275.106
51 black.3.4 1.193 1.234
52 black.4.2 0.178 1.228
53 black.4.3 -12.680 275.108
54 black.4.4 0.247 1.540
55 white.2.2 0.578 0.770
56 white.2.3 -11.696 275.107
57 white.2.4 1.889 1.315
58 white.3.2 1.555* 0.776
59 white.3.3 -10.899 275.106
60 white.3.4 2.147 1.229
61 white.4.2 1.078 1.220
62 white.4.3 -11.220 275.108
63 white.4.4 1.678 1.531
year
64 90 0.297 0.945
mfulleth.year
65 Hisp.90 -1.155 0.765
66 black.90 -1.311 0.765
67 white.90 -0.803 0.764
med4.year
68 2.90 0.089 1.234
69 3.90 -1.092 1.456
70 4.90 -25.323 .
mfulleth.med4.year
71 Hisp.2.90 0.311 0.934
72 Hisp.3.90 2.016 1.111
73 Hisp.4.90 12.899 423.639
74 black.2.90 0.227 0.934
75 black.3.90 2.016 1.111
76 black.4.90 13.532 423.639
77 white.2.90 0.124 0.932
78 white.3.90 1.950 1.108
79 white.4.90 13.125 423.639
fed4.year
80 2.90 -2.802 1.451
81 3.90 12.371 275.111
82 4.90 -23.424** 1.172
mfulleth.fed4.year
83 Hisp.2.90 2.137* 1.089
84 Hisp.3.90 -9.910 275.108
85 Hisp.4.90 11.774 173.721
86 black.2.90 1.727 1.090
87 black.3.90 -10.320 275.108
88 black.4.90 11.709 173.721
89 white.2.90 1.862 1.088
90 white.3.90 -9.934 275.108
91 white.4.90 11.933 173.721
med4.fed4.year
92 2.2.90 0.314 1.696
93 2.4.90 16.056 15.702
94 3.2.90 1.721 1.881
95 3.3.90 -13.152 275.113
96 3.4.90 23.094 .
97 4.2.90 27.192** 1.349
98 4.3.90 10.881 275.110
99 4.4.90 47.947** 1.517
mfulleth.med4.fed4.year
100 Hisp.2.2.90 -0.504 1.250
101 Hisp.2.3.90 11.611 275.109
102 Hisp.2.4.90 -4.133 174.424
103 Hisp.3.2.90 -2.620 1.388
104 Hisp.3.3.90 9.966 275.110
105 Hisp.3.4.90 -11.967 173.721
106 Hisp.4.2.90 -14.409 423.640
107 Hisp.4.3.90 -0.881 505.128
108 Hisp.4.4.90 -23.344 457.874
109 black.2.2.90 -0.078 1.250
110 black.2.3.90 11.847 275.109
111 black.2.4.90 -4.388 174.424
112 black.3.2.90 -2.258 1.388
113 black.3.3.90 10.209 275.110
114 black.3.4.90 -12.074 173.721
115 black.4.2.90 -14.535 423.640
116 black.4.3.90 -1.373 505.128
117 black.4.4.90 -24.284 457.874
118 white.2.2.90 -0.433 1.248
119 white.2.3.90 11.420 275.109
120 white.2.4.90 -4.577 174.423
121 white.3.2.90 -2.648 1.384
122 white.3.3.90 9.560 275.110
123 white.3.4.90 -12.554 173.721
124 white.4.2.90 -14.641 423.639
125 white.4.3.90 -1.646 505.128
126 white.4.4.90 -24.122 457.874
ffulleth
127 Hisp 5.573** 0.334
128 black 5.435** 0.334
129 white 7.495** 0.333
ffulleth.med4
130 Hisp.2 -0.250 0.505
131 Hisp.3 -0.825 0.669
132 Hisp.4 -2.018 1.067
133 black.2 0.080 0.505
134 black.3 -0.599 0.669
135 black.4 -1.393 1.062
136 white.2 0.235 0.504
137 white.3 -0.556 0.667
138 white.4 -1.217 1.055
ffulleth.fed4
139 Hisp.2 -0.546 0.473
140 Hisp.3 -0.050 1.057
141 Hisp.4 -3.088** 0.688
142 black.2 0.065 0.472
143 black.3 0.975 1.056
144 black.4 -2.164** 0.676
145 white.2 0.108 0.472
146 white.3 0.367 1.055
147 white.4 -2.672** 0.669
ffulleth.med4.fed4
148 Hisp.2.2 -1.062 0.616
149 Hisp.2.3 -2.254* 1.135
150 Hisp.2.4 0.273 0.878
151 Hisp.3.2 -0.936 0.763
152 Hisp.3.3 -2.236 1.210
153 Hisp.3.4 -0.077 0.914
154 Hisp.4.2 0.447 1.169
155 Hisp.4.3 -1.765 1.470
156 Hisp.4.4 0.301 1.228
157 black.2.2 -1.206* 0.615
158 black.2.3 -2.457* 1.134
159 black.2.4 0.727 0.864
160 black.3.2 -1.159 0.763
161 black.3.3 -2.593* 1.209
162 black.3.4 0.000 0.904
163 black.4.2 -0.421 1.163
164 black.4.3 -2.662 1.466
165 black.4.4 -0.039 1.217
166 white.2.2 -0.969 0.614
167 white.2.3 -1.882 1.133
168 white.2.4 1.342 0.856
169 white.3.2 -0.639 0.760
170 white.3.3 -1.670 1.207
171 white.3.4 0.944 0.896
172 white.4.2 0.625 1.155
173 white.4.3 -1.105 1.459
174 white.4.4 1.500 1.206
ffulleth.year
175 Hisp.90 -0.239 0.559
176 black.90 -0.451 0.559
177 white.90 0.065 0.558
ffulleth.med4.year
178 Hisp.2.90 -0.258 0.811
179 Hisp.3.90 -0.369 0.950
180 Hisp.4.90 12.605 423.639
181 black.2.90 -0.320 0.811
182 black.3.90 -0.396 0.951
183 black.4.90 12.447 423.639
184 white.2.90 -0.408 0.809
185 white.3.90 -0.468 0.946
186 white.4.90 12.126 423.639
ffulleth.fed4.year
187 Hisp.2.90 1.106 0.962
188 Hisp.3.90 -1.381 1.220
189 Hisp.4.90 11.665 173.722
190 black.2.90 0.731 0.962
191 black.3.90 -1.741 1.218
192 black.4.90 11.985 173.721
193 white.2.90 0.888 0.960
194 white.3.90 -1.365 1.216
195 white.4.90 12.208 173.721
med4.fed4.year
196 2.3.90 -14.148 275.113
ffulleth.med4.fed4.year
197 Hisp.2.2.90 0.107 1.152
198 Hisp.2.3.90 2.625 1.383
199 Hisp.2.4.90 -10.856 173.724
200 Hisp.3.2.90 0.533 1.280
201 Hisp.3.3.90 3.152* 1.455
202 Hisp.3.4.90 -10.448 173.722
203 Hisp.4.2.90 -13.606 423.640
204 Hisp.4.3.90 -10.027 423.640
205 Hisp.4.4.90 -23.557 457.874
206 black.2.2.90 0.478 1.152
207 black.2.3.90 2.839* 1.381
208 black.2.4.90 -11.512 173.723
209 black.3.2.90 0.875 1.280
210 black.3.3.90 3.400* 1.454
211 black.3.4.90 -10.786 173.721
212 black.4.2.90 -12.915 423.640
213 black.4.3.90 -9.931 423.640
214 black.4.4.90 -24.389 457.874
215 white.2.2.90 0.098 1.150
216 white.2.3.90 2.399 1.378
217 white.2.4.90 -11.737 173.723
218 white.3.2.90 0.486 1.276
219 white.3.3.90 2.774 1.449
220 white.3.4.90 -11.240 173.721
221 white.4.2.90 -13.186 423.640
222 white.4.3.90 -10.267 423.640
223 white.4.4.90 -24.311 457.874
224 _cons -6.639** 0.667
------------------------------------------------------------------------------------------
* p < .05
** p < .01
. *what you get here is grotesquely large standard errors for some of these coefficients.
> It is not by itself a disqualifying problem, unless the coefficients with huge SE are coefficients you need for some hypothesis testing.
. desmat: poisson count mfulleth*med4*year ffulleth*fed4*year, difficult verbose
Desmat generated the following design matrix:
nr Variables Term Parameterization
First Last
1 _x_1 _x_3 mfulleth ind(1)
2 _x_4 _x_6 med4 ind(1)
3 _x_7 _x_15 mfulleth.med4 ind(1).ind(1)
4 _x_16 year ind(80)
5 _x_17 _x_19 mfulleth.year ind(1).ind(80)
6 _x_20 _x_22 med4.year ind(1).ind(80)
7 _x_23 _x_31 mfulleth.med4.year ind(1).ind(1).ind(80)
8 _x_32 _x_34 ffulleth ind(1)
9 _x_35 _x_37 fed4 ind(1)
10 _x_38 _x_46 ffulleth.fed4 ind(1).ind(1)
11 _x_47 _x_49 ffulleth.year ind(1).ind(80)
12 _x_50 _x_52 fed4.year ind(1).ind(80)
13 _x_53 _x_61 ffulleth.fed4.year ind(1).ind(1).ind(80)
Iteration 0: log likelihood = -2101751.6
Iteration 1: log likelihood = -1680207.8 (backed up)
Iteration 2: log likelihood = -1440759.3
Iteration 3: log likelihood = -538553.17
Iteration 4: log likelihood = -280289.69
Iteration 5: log likelihood = -273068.86
Iteration 6: log likelihood = -272981.88
Iteration 7: log likelihood = -272981.86
Poisson regression Number of obs = 512
LR chi2(61) = 2258852.86
Prob > chi2 = 0.0000
Log likelihood = -272981.86 Pseudo R2 = 0.8053
------------------------------------------------------------------------------
count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_x_1 | 5.732471 .2676082 21.42 0.000 5.207969 6.256974
_x_2 | 5.890018 .2675452 22.02 0.000 5.365639 6.414397
_x_3 | 7.872986 .2672267 29.46 0.000 7.349231 8.396741
_x_4 | 1.955423 .2854539 6.85 0.000 1.395944 2.514903
_x_5 | 2.454054 .2784204 8.81 0.000 1.90836 2.999748
_x_6 | 2.767475 .2754401 10.05 0.000 2.227623 3.307328
_x_7 | -1.656304 .2861584 -5.79 0.000 -2.217164 -1.095444
_x_8 | -2.861023 .2794574 -10.24 0.000 -3.40875 -2.313296
_x_9 | -4.207195 .2776222 -15.15 0.000 -4.751324 -3.663065
_x_10 | -1.143068 .285953 -4.00 0.000 -1.703525 -.5826102
_x_11 | -2.382234 .2791044 -8.54 0.000 -2.929268 -1.835199
_x_12 | -3.576285 .2766012 -12.93 0.000 -4.118413 -3.034156
_x_13 | -.7645021 .285516 -2.68 0.007 -1.324103 -.204901
_x_14 | -1.902726 .2784973 -6.83 0.000 -2.448571 -1.356881
_x_15 | -2.366507 .2755225 -8.59 0.000 -2.906521 -1.826492
_x_16 | -.6359079 .5793622 -1.10 0.272 -1.771437 .4996212
_x_17 | .0201011 .4635997 0.04 0.965 -.8885377 .9287399
_x_18 | -.2842166 .4636407 -0.61 0.540 -1.192936 .6245026
_x_19 | .2870418 .4629341 0.62 0.535 -.6202924 1.194376
_x_20 | -.3749736 .5037388 -0.74 0.457 -1.362283 .6123363
_x_21 | .0188761 .4821029 0.04 0.969 -.9260281 .9637804
_x_22 | .0657375 .4765791 0.14 0.890 -.8683404 .9998154
_x_23 | .4916962 .5048843 0.97 0.330 -.4978588 1.481251
_x_24 | .5775696 .4835602 1.19 0.232 -.370191 1.52533
_x_25 | .1540088 .4799209 0.32 0.748 -.7866189 1.094637
_x_26 | .5833444 .50473 1.16 0.248 -.4059083 1.572597
_x_27 | .6714441 .4832958 1.39 0.165 -.2757982 1.618686
_x_28 | .0941022 .4788516 0.20 0.844 -.8444297 1.032634
_x_29 | .2551188 .5038284 0.51 0.613 -.7323667 1.242604
_x_30 | .2659069 .482208 0.55 0.581 -.6792034 1.211017
_x_31 | -.1254384 .4767 -0.26 0.792 -1.059753 .8088765
_x_32 | 5.34353 .2241405 23.84 0.000 4.904222 5.782837
_x_33 | 5.356114 .2241338 23.90 0.000 4.91682 5.795409
_x_34 | 7.488601 .2236693 33.48 0.000 7.050217 7.926985
_x_35 | 2.014903 .2380476 8.46 0.000 1.548338 2.481468
_x_36 | 2.256541 .2350226 9.60 0.000 1.795905 2.717177
_x_37 | 2.420368 .2333333 10.37 0.000 1.963043 2.877693
_x_38 | -1.591206 .2388766 -6.66 0.000 -2.059396 -1.123017
_x_39 | -2.808836 .2364099 -11.88 0.000 -3.272191 -2.345481
_x_40 | -4.194365 .2368372 -17.71 0.000 -4.658557 -3.730173
_x_41 | -1.031894 .2387277 -4.32 0.000 -1.499791 -.5639958
_x_42 | -1.981623 .2359042 -8.40 0.000 -2.443987 -1.519259
_x_43 | -3.066502 .2347992 -13.06 0.000 -3.5267 -2.606304
_x_44 | -.6599469 .2381215 -2.77 0.006 -1.126656 -.1932373
_x_45 | -1.700147 .2351162 -7.23 0.000 -2.160966 -1.239328
_x_46 | -2.285499 .2334456 -9.79 0.000 -2.743044 -1.827954
_x_47 | -.4147245 .3495487 -1.19 0.235 -1.099827 .2703784
_x_48 | -.6504528 .3497292 -1.86 0.063 -1.335909 .0350038
_x_49 | -.1883215 .3485753 -0.54 0.589 -.8715166 .4948736
_x_50 | -1.043043 .3938556 -2.65 0.008 -1.814985 -.2710997
_x_51 | -.4182617 .3715209 -1.13 0.260 -1.146429 .3099059
_x_52 | -.0826917 .3643756 -0.23 0.820 -.7968547 .6314713
_x_53 | 1.180953 .3953848 2.99 0.003 .4060129 1.955893
_x_54 | 1.397664 .3735449 3.74 0.000 .6655291 2.129798
_x_55 | .8736175 .3692086 2.37 0.018 .149982 1.597253
_x_56 | 1.093705 .3953761 2.77 0.006 .3187818 1.868628
_x_57 | 1.211124 .373244 3.24 0.001 .4795789 1.942668
_x_58 | .5221796 .367282 1.42 0.155 -.1976798 1.242039
_x_59 | .8987948 .3939798 2.28 0.023 .1266087 1.670981
_x_60 | .9947627 .371666 2.68 0.007 .2663108 1.723215
_x_61 | .4022689 .3645557 1.10 0.270 -.3122471 1.116785
_cons | -7.025743 .348396 -20.17 0.000 -7.708587 -6.3429
------------------------------------------------------------------------------
------------------------------------------------------------------------------------------
Poisson regression
------------------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -272981.859
LR chi square: 2258852.856
Model degrees of freedom: 61
Pseudo R-squared: 0.805
Prob: 0.000
------------------------------------------------------------------------------------------
nr Effect Coeff s.e.
------------------------------------------------------------------------------------------
count
mfulleth
1 Hisp 5.732** 0.268
2 black 5.890** 0.268
3 white 7.873** 0.267
med4
4 2 1.955** 0.285
5 3 2.454** 0.278
6 4 2.767** 0.275
mfulleth.med4
7 Hisp.2 -1.656** 0.286
8 Hisp.3 -2.861** 0.279
9 Hisp.4 -4.207** 0.278
10 black.2 -1.143** 0.286
11 black.3 -2.382** 0.279
12 black.4 -3.576** 0.277
13 white.2 -0.765** 0.286
14 white.3 -1.903** 0.278
15 white.4 -2.367** 0.276
year
16 90 -0.636 0.579
mfulleth.year
17 Hisp.90 0.020 0.464
18 black.90 -0.284 0.464
19 white.90 0.287 0.463
med4.year
20 2.90 -0.375 0.504
21 3.90 0.019 0.482
22 4.90 0.066 0.477
mfulleth.med4.year
23 Hisp.2.90 0.492 0.505
24 Hisp.3.90 0.578 0.484
25 Hisp.4.90 0.154 0.480
26 black.2.90 0.583 0.505
27 black.3.90 0.671 0.483
28 black.4.90 0.094 0.479
29 white.2.90 0.255 0.504
30 white.3.90 0.266 0.482
31 white.4.90 -0.125 0.477
ffulleth
32 Hisp 5.344** 0.224
33 black 5.356** 0.224
34 white 7.489** 0.224
fed4
35 2 2.015** 0.238
36 3 2.257** 0.235
37 4 2.420** 0.233
ffulleth.fed4
38 Hisp.2 -1.591** 0.239
39 Hisp.3 -2.809** 0.236
40 Hisp.4 -4.194** 0.237
41 black.2 -1.032** 0.239
42 black.3 -1.982** 0.236
43 black.4 -3.067** 0.235
44 white.2 -0.660** 0.238
45 white.3 -1.700** 0.235
46 white.4 -2.285** 0.233
ffulleth.year
47 Hisp.90 -0.415 0.350
48 black.90 -0.650 0.350
49 white.90 -0.188 0.349
fed4.year
50 2.90 -1.043** 0.394
51 3.90 -0.418 0.372
52 4.90 -0.083 0.364
ffulleth.fed4.year
53 Hisp.2.90 1.181** 0.395
54 Hisp.3.90 1.398** 0.374
55 Hisp.4.90 0.874* 0.369
56 black.2.90 1.094** 0.395
57 black.3.90 1.211** 0.373
58 black.4.90 0.522 0.367
59 white.2.90 0.899* 0.394
60 white.3.90 0.995** 0.372
61 white.4.90 0.402 0.365
62 _cons -7.026** 0.348
------------------------------------------------------------------------------------------
* p < .05
** p < .01
. exit, clear
*And here is a much smaller model for the same data, and it fits wonderfully, and the
maximum likelihood maximizes quickly. So the sparse data only become a problem as you fit
models that are less parsimonious and make more demands on the data.