-------------------------------------------------------------------------------------------------------
log: C:\AAA Miker Files\newer web pages\soc_388_notes\soc_388_2007\seventh_class_log.log
log type: text
opened on: 16 Oct 2007, 11:00:43
. set linesize 75
. *first, let me say a word about dealing with string variables
. *this is the string variable version of LA intermarriage.
. *note the results from describe
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\LA_intermar.dta
obs: 25
vars: 3 6 Oct 2001 16:50
size: 500 (99.9% of memory free)
---------------------------------------------------------------------------
> ----
storage display value
variable name type format label variable label
---------------------------------------------------------------------------
> ----
husb str7 %9s
wife str7 %9s
count int %8.0g
---------------------------------------------------------------------------
> ----
Sorted by:
. set linesize 79
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\LA_intermar
> .dta
obs: 25
vars: 3 6 Oct 2001 16:50
size: 500 (99.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
husb str7 %9s
wife str7 %9s
count int %8.0g
-------------------------------------------------------------------------------
Sorted by:
. tabulate husb
husb | Freq. Percent Cum.
------------+-----------------------------------
AllOth | 5 20.00 20.00
Black | 5 20.00 40.00
Mex | 5 20.00 60.00
OthHisp | 5 20.00 80.00
White | 5 20.00 100.00
------------+-----------------------------------
Total | 25 100.00
. *If I want to generate a variable for endogamy which is different for every group, it would look something like this:
. gen endog_full=0
. replace endog_full=1 if husb=="White" & husb==wife
(1 real change made)
. replace endog_full=2 if husb=="Black" & husb==wife
(1 real change made)
. table husb wife, contents(mean endog_full)
-------------------------------------------------------
| wife
husb | AllOth Black Mex OthHisp White
----------+--------------------------------------------
AllOth | 0 0 0 0 0
Black | 0 2 0 0 0
Mex | 0 0 0 0 0
OthHisp | 0 0 0 0 0
White | 0 0 0 0 1
-------------------------------------------------------
. *remember to use double quotes when dealing with text vars, and spell correctly and use capitalization.
. clear
. mem
unrecognized command: mem
r(199);
. memory
bytes
--------------------------------------------------------------------
Details of set memory usage
overhead (pointers) 0 0.00%
data 0 0.00%
----------------------------
data + overhead 0 0.00%
free 10,485,752 100.00%
----------------------------
Total allocated 10,485,752 100.00%
--------------------------------------------------------------------
Other memory usage
set maxvar usage 1,816,666
set matsize usage 1,315,200
programs, saved results, etc. 437
---------------
Total 3,132,303
-------------------------------------------------------
Grand total 13,618,055
. *One of the things to keep in mind about Stata is that Stata wants to load all data into memory, which sometimes means you need to add more memory before working with the data.
. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta", clear
. *This is a dataset available on my website under Soc 180, intro social research
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_nu
> meric.dta
obs: 133,710
vars: 42 16 May 2004 11:38
size: 9,894,540 (5.6% 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:
. *Key: how to contract a big dataset like the CPS into the cross tabulate data that we will use.
.
. 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 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
. *The dataset we want is a cross tabulation of these 3 variables, with 7*4*2=56 cells
. tabulate maritl race if age>19 & age<60
| p25
Marital Status p17 | White Black Amer Indi Asian | Total
----------------------+--------------------------------------------+----------
married, spouse prese | 38,255 2,677 477 1,624 | 43,033
married, AF spouse pr | 275 42 2 20 | 339
married, spouse absen | 783 122 21 96 | 1,022
widowed | 808 159 13 33 | 1,013
divorced | 6,670 852 128 141 | 7,791
separated | 1,284 420 30 47 | 1,781
never married | 13,654 2,773 286 776 | 17,489
----------------------+--------------------------------------------+----------
Total | 61,729 7,045 957 2,737 | 72,468
. *We are looking at something like 72K cases.
. *The command we want is contract
. contract maritl race sex if age>19 & age<60
no room to add more variables due to width
An attempt was made to add a variable that would have increased the memory
required to store an observation beyond what is currently possible. You
have the following alternatives:
1. Store existing variables more efficiently; see help compress.
2. Drop some variables or observations; see help drop. (Think of Stata's
data area as the area of a rectangle; Stata can trade off width and
length.)
3. Increase the amount of memory allocated to the data area using the set
memory command; see help memory.
r(902);
. *typical stata error, when not enough memory.
. clear
. set mem 250m
Current memory allocation
current memory usage
settable value description (1M = 1024k)
--------------------------------------------------------------------
set maxvar 5000 max. variables allowed 1.733M
set memory 250M max. data space 250.000M
set matsize 400 max. RHS vars in models 1.254M
-----------
252.987M
. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta", clear
. contract maritl race sex if age>19 & age<60
. 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
. *Normally, Stata would drop the cells with zero observations, which is why we ended up with 55 instead of 56
. clear
. use "C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_numeric.dta", clear
. contract maritl race sex if age>19 & age<60, 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
. *When we use the zero option, we end up keeping the cells with zero counts, which is important. If they are observational or probability zeros.
. rename _freq count
. tabulate count
Frequency | Freq. Percent Cum.
------------+-----------------------------------
0 | 1 1.79 1.79
1 | 1 1.79 3.57
2 | 2 3.57 7.14
3 | 1 1.79 8.93
9 | 2 3.57 12.50
10 | 2 3.57 16.07
12 | 1 1.79 17.86
18 | 1 1.79 19.64
19 | 1 1.79 21.43
21 | 2 3.57 25.00
29 | 1 1.79 26.79
31 | 1 1.79 28.57
32 | 2 3.57 32.14
42 | 1 1.79 33.93
48 | 1 1.79 35.71
53 | 1 1.79 37.50
54 | 2 3.57 41.07
69 | 1 1.79 42.86
74 | 1 1.79 44.64
93 | 1 1.79 46.43
127 | 1 1.79 48.21
132 | 1 1.79 50.00
141 | 1 1.79 51.79
151 | 1 1.79 53.57
154 | 1 1.79 55.36
230 | 1 1.79 57.14
247 | 1 1.79 58.93
254 | 1 1.79 60.71
279 | 1 1.79 62.50
325 | 1 1.79 64.29
355 | 1 1.79 66.07
357 | 1 1.79 67.86
421 | 1 1.79 69.64
426 | 1 1.79 71.43
487 | 1 1.79 73.21
527 | 1 1.79 75.00
657 | 1 1.79 76.79
741 | 1 1.79 78.57
797 | 1 1.79 80.36
883 | 1 1.79 82.14
1158 | 1 1.79 83.93
1320 | 1 1.79 85.71
1357 | 1 1.79 87.50
1615 | 1 1.79 89.29
2905 | 1 1.79 91.07
3765 | 1 1.79 92.86
5957 | 1 1.79 94.64
7697 | 1 1.79 96.43
18700 | 1 1.79 98.21
19555 | 1 1.79 100.00
------------+-----------------------------------
Total | 56 100.00
. *This dataset has only one zero and a couple of other small number cells. Would not appear to be a problem.
. tabulate maritl race
| p25
Marital Status p17 | White Black Amer Indi Asian | Total
----------------------+--------------------------------------------+----------
married, spouse prese | 2 2 2 2 | 8
married, AF spouse pr | 2 2 2 2 | 8
married, spouse absen | 2 2 2 2 | 8
widowed | 2 2 2 2 | 8
divorced | 2 2 2 2 | 8
separated | 2 2 2 2 | 8
never married | 2 2 2 2 | 8
----------------------+--------------------------------------------+----------
Total | 14 14 14 14 | 56
. *that just told us how many cells we have
. tabulate maritl race [fweight=count]
| p25
Marital Status p17 | White Black Amer Indi Asian | Total
----------------------+--------------------------------------------+----------
married, spouse prese | 38,255 2,677 477 1,624 | 43,033
married, AF spouse pr | 275 42 2 20 | 339
married, spouse absen | 783 122 21 96 | 1,022
widowed | 808 159 13 33 | 1,013
divorced | 6,670 852 128 141 | 7,791
separated | 1,284 420 30 47 | 1,781
never married | 13,654 2,773 286 776 | 17,489
----------------------+--------------------------------------------+----------
Total | 61,729 7,045 957 2,737 | 72,468
. *One additional issue that sometimes comes up, is that you make a crosstabulation and then later you realize that one of your variables has a category that is so small, that it creates data sparseness problems for you.
. *One thing you might want to do is collapse again, to get rid of a small category in one variable.
. *Let's contract marital status to get rid of the very small married AF present category.
. gen byte maritl_small=maritl
. replace maritl_small=1 if maritl==2
(8 real changes made)
. tabulate maritl maritl_small [fweight=count]
| maritl_small
Marital Status p17 | 1 3 4 5 | Total
----------------------+--------------------------------------------+----------
married, spouse prese | 43,033 0 0 0 | 43,033
married, AF spouse pr | 339 0 0 0 | 339
married, spouse absen | 0 1,022 0 0 | 1,022
widowed | 0 0 1,013 0 | 1,013
divorced | 0 0 0 7,791 | 7,791
separated | 0 0 0 0 | 1,781
never married | 0 0 0 0 | 17,489
----------------------+--------------------------------------------+----------
Total | 43,372 1,022 1,013 7,791 | 72,468
| maritl_small
Marital Status p17 | 6 7 | Total
----------------------+----------------------+----------
married, spouse prese | 0 0 | 43,033
married, AF spouse pr | 0 0 | 339
married, spouse absen | 0 0 | 1,022
widowed | 0 0 | 1,013
divorced | 0 0 | 7,791
separated | 1,781 0 | 1,781
never married | 0 17,489 | 17,489
----------------------+----------------------+----------
Total | 1,781 17,489 | 72,468
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_nu
> meric.dta
obs: 56
vars: 5 16 May 2004 11:38
size: 560 (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
count int %12.0g Frequency
maritl_small byte %8.0g
-------------------------------------------------------------------------------
Sorted by: maritl race sex
Note: dataset has changed since last saved
. label val maritl_small marlbl
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_nu
> meric.dta
obs: 56
vars: 5 16 May 2004 11:38
size: 560 (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
count int %12.0g Frequency
maritl_small byte %26.0g marlbl
-------------------------------------------------------------------------------
Sorted by: maritl race sex
Note: dataset has changed since last saved
. contract maritl_small race sex [fweight=count], zero
. rename _freq count
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_meth_proj3\cps_y2k_nu
> meric.dta
obs: 48
vars: 4 16 May 2004 11:38
size: 432 (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
maritl_small byte %26.0g marlbl
count int %12.0g Frequency
-------------------------------------------------------------------------------
Sorted by: maritl_small race sex
Note: dataset has changed since last saved
. tabulate maritl_small race [fweight=count]
| p25
maritl_small | White Black Amer Indi Asian | Total
----------------------+--------------------------------------------+----------
married, spouse prese | 38,530 2,719 479 1,644 | 43,372
married, spouse absen | 783 122 21 96 | 1,022
widowed | 808 159 13 33 | 1,013
divorced | 6,670 852 128 141 | 7,791
separated | 1,284 420 30 47 | 1,781
never married | 13,654 2,773 286 776 | 17,489
----------------------+--------------------------------------------+----------
Total | 61,729 7,045 957 2,737 | 72,468
. *That is a word or two about contract.
. clear
. use "C:\AAA Miker Files\newer web pages\soc_388_notes\70-80-90 MR intermar.dt
> a", clear
. *This is the HW3 dataset
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\70-80-90 MR
> intermar.dta
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
. 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 [fweight=count]
meth | Freq. Percent Cum.
------------+-----------------------------------
Blk_NH | 45,681 7.03 7.03
Mex_Am | 25,294 3.89 10.92
Oth_H | 11,609 1.79 12.71
Oth_NH | 8,100 1.25 13.96
Wht_NH | 559,137 86.04 100.00
------------+-----------------------------------
Total | 649,821 100.00
. tabulate mgen
mgen | Freq. Percent Cum.
------------+-----------------------------------
1 | 75 33.33 33.33
2 | 150 66.67 100.00
------------+-----------------------------------
Total | 225 100.00
. tabulate mgen [fweight=count]
mgen | Freq. Percent Cum.
------------+-----------------------------------
1 | 19,825 3.05 3.05
2 | 629,996 96.95 100.00
------------+-----------------------------------
Total | 649,821 100.00
. 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
. display 5*5*2*2*3
300
. describe
Contains data from C:\AAA Miker Files\newer web pages\soc_388_notes\70-80-90 MRintermar.dta
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
. *We should have 300 cells, but we only have 225. What is missing?
. tabulate mgen fgen [fweight=count]
| fgen
mgen | 1 2 | Total
-----------+----------------------+----------
1 | 0 19,825 | 19,825
2 | 19,166 610,830 | 629,996
-----------+----------------------+----------
Total | 19,166 630,655 | 649,821
. *I have created a structural zero in this dataset, by not including the cells where husband and wife are both gen==1, that is to say foreign born.
. tabulate mgen fgen
| fgen
mgen | 1 2 | Total
-----------+----------------------+----------
1 | 0 75 | 75
2 | 75 75 | 150
-----------+----------------------+----------
Total | 75 150 | 225
. *That makes the dataset a little bit more of a puzzle in some ways.
. tabulate count
Frequency | Freq. Percent Cum.
------------+-----------------------------------
0 | 8 3.56 3.56
1 | 7 3.11 6.67
2 | 7 3.11 9.78
3 | 8 3.56 13.33
5 | 9 4.00 17.33
6 | 4 1.78 19.11
7 | 5 2.22 21.33
8 | 2 0.89 22.22
9 | 1 0.44 22.67
10 | 1 0.44 23.11
11 | 2 0.89 24.00
12 | 7 3.11 27.11
13 | 1 0.44 27.56
14 | 4 1.78 29.33
15 | 1 0.44 29.78
16 | 1 0.44 30.22
17 | 1 0.44 30.67
19 | 1 0.44 31.11
20 | 4 1.78 32.89
21 | 2 0.89 33.78
22 | 1 0.44 34.22
23 | 1 0.44 34.67
25 | 1 0.44 35.11
26 | 4 1.78 36.89
27 | 1 0.44 37.33
29 | 1 0.44 37.78
33 | 3 1.33 39.11
34 | 1 0.44 39.56
35 | 3 1.33 40.89
36 | 2 0.89 41.78
38 | 2 0.89 42.67
40 | 2 0.89 43.56
41 | 1 0.44 44.00
42 | 1 0.44 44.44
43 | 3 1.33 45.78
45 | 1 0.44 46.22
46 | 1 0.44 46.67
47 | 1 0.44 47.11
50 | 2 0.89 48.00
54 | 1 0.44 48.44
56 | 1 0.44 48.89
60 | 2 0.89 49.78
61 | 1 0.44 50.22
62 | 1 0.44 50.67
64 | 1 0.44 51.11
65 | 1 0.44 51.56
66 | 3 1.33 52.89
67 | 1 0.44 53.33
68 | 3 1.33 54.67
71 | 1 0.44 55.11
76 | 1 0.44 55.56
78 | 3 1.33 56.89
79 | 1 0.44 57.33
80 | 2 0.89 58.22
81 | 1 0.44 58.67
85 | 2 0.89 59.56
88 | 1 0.44 60.00
91 | 1 0.44 60.44
94 | 1 0.44 60.89
95 | 1 0.44 61.33
96 | 1 0.44 61.78
104 | 1 0.44 62.22
105 | 1 0.44 62.67
107 | 1 0.44 63.11
109 | 2 0.89 64.00
121 | 1 0.44 64.44
122 | 1 0.44 64.89
123 | 1 0.44 65.33
126 | 1 0.44 65.78
129 | 1 0.44 66.22
130 | 1 0.44 66.67
131 | 1 0.44 67.11
132 | 1 0.44 67.56
134 | 1 0.44 68.00
138 | 1 0.44 68.44
139 | 1 0.44 68.89
144 | 1 0.44 69.33
147 | 1 0.44 69.78
148 | 1 0.44 70.22
156 | 1 0.44 70.67
158 | 1 0.44 71.11
186 | 1 0.44 71.56
224 | 1 0.44 72.00
230 | 1 0.44 72.44
232 | 2 0.89 73.33
239 | 1 0.44 73.78
246 | 1 0.44 74.22
257 | 1 0.44 74.67
263 | 1 0.44 75.11
296 | 1 0.44 75.56
315 | 1 0.44 76.00
329 | 1 0.44 76.44
381 | 1 0.44 76.89
391 | 1 0.44 77.33
401 | 1 0.44 77.78
405 | 1 0.44 78.22
413 | 1 0.44 78.67
481 | 1 0.44 79.11
482 | 1 0.44 79.56
627 | 1 0.44 80.00
628 | 1 0.44 80.44
632 | 1 0.44 80.89
640 | 1 0.44 81.33
686 | 1 0.44 81.78
756 | 1 0.44 82.22
773 | 1 0.44 82.67
789 | 1 0.44 83.11
809 | 1 0.44 83.56
878 | 1 0.44 84.00
914 | 1 0.44 84.44
919 | 1 0.44 84.89
1006 | 1 0.44 85.33
1012 | 1 0.44 85.78
1083 | 1 0.44 86.22
1135 | 1 0.44 86.67
1163 | 1 0.44 87.11
1176 | 1 0.44 87.56
1197 | 1 0.44 88.00
1227 | 1 0.44 88.44
1392 | 1 0.44 88.89
1430 | 1 0.44 89.33
1454 | 1 0.44 89.78
1492 | 1 0.44 90.22
1514 | 1 0.44 90.67
1527 | 1 0.44 91.11
1545 | 1 0.44 91.56
1558 | 1 0.44 92.00
1586 | 1 0.44 92.44
1653 | 1 0.44 92.89
2171 | 1 0.44 93.33
2204 | 1 0.44 93.78
2210 | 1 0.44 94.22
2556 | 1 0.44 94.67
3629 | 1 0.44 95.11
3752 | 1 0.44 95.56
4596 | 1 0.44 96.00
5019 | 1 0.44 96.44
5020 | 1 0.44 96.89
5151 | 1 0.44 97.33
7116 | 1 0.44 97.78
12005 | 1 0.44 98.22
24628 | 1 0.44 98.67
54331 | 1 0.44 99.11
188975 | 1 0.44 99.56
280562 | 1 0.44 100.00
------------+-----------------------------------
Total | 225 100.00
. *There are also some sampling zeros in the data, 8 out of 225 cells are zero in fact. We deal with the sampling zeros by modeling them as best we can.
. clear
. *Now let's look at a dataset that has even more sampling zeros.
. 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.dta
obs: 512
vars: 7 24 Oct 2005 11:48
size: 11,264 (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
r_endog byte %9.0g
-------------------------------------------------------------------------------
Sorted by: year med4 fed4
. *This is a dataset that comes from a paper by Qian from demography 1997.
. tabulate mfulleth
mfulleth | Freq. Percent Cum.
------------+-----------------------------------
Asian | 128 25.00 25.00
Hisp | 128 25.00 50.00
black | 128 25.00 75.00
white | 128 25.00 100.00
------------+-----------------------------------
Total | 512 100.00
. tabulate med4
med4 | Freq. Percent Cum.
------------+-----------------------------------
1 | 128 25.00 25.00
2 | 128 25.00 50.00
3 | 128 25.00 75.00
4 | 128 25.00 100.00
------------+-----------------------------------
Total | 512 100.00
. tabulate year
year | Freq. Percent Cum.
------------+-----------------------------------
80 | 256 50.00 50.00
90 | 256 50.00 100.00
------------+-----------------------------------
Total | 512 100.00
. 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
. *102 of the 512 cells are zero. This actually can easily be a problem when you are trying to estimate models. Let's see how.
. desmat: poisson count mfulleth*med4*year ffulleth*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 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
. poisgof
Goodness-of-fit chi2 = 543766.7
Prob > chi2(450) = 0.0000
. *That one fit easy, not a good fit but a fast likelihood maximization.
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*fed4*med4*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.fed4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.med4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.fed4.med4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.fed4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.med4.year ind(1).ind(1).ind(80)
23 _x_196 fed4.med4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.fed4.med4.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)
--Break--
r(1);
. *That maximization process did not seem to be working very well. It was stuck
on one value and not improving.
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*fed4*med4*year, verbose difficult
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.fed4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.med4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.fed4.med4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.fed4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.med4.year ind(1).ind(1).ind(80)
23 _x_196 fed4.med4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.fed4.med4.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)
--Break--
r(1);
. *difficult usually works, but here it doesn't seem to make a difference.
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*fed4*med4*year, verbose difficult iteration(40)
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.fed4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.med4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.fed4.med4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.fed4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.med4.year ind(1).ind(1).ind(80)
23 _x_196 fed4.med4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.fed4.med4.year ind(1).ind(1).ind(1).ind(80)
option iteration() not allowed
r(198);
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*fed4*med4*year, verbose difficult iterations(40)
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.fed4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.med4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.fed4.med4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.fed4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.med4.year ind(1).ind(1).ind(80)
23 _x_196 fed4.med4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.fed4.med4.year ind(1).ind(1).ind(1).ind(80)
option iterations() not allowed
r(198);
. desmat: poisson count mfulleth*med4*fed4*year ffulleth*fed4*med4*year, verbose difficult iterate(40)
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.fed4 ind(1).ind(1)
18 _x_139 _x_147 ffulleth.med4 ind(1).ind(1)
19 _x_148 _x_174 ffulleth.fed4.med4 ind(1).ind(1).ind(1)
20 _x_175 _x_177 ffulleth.year ind(1).ind(80)
21 _x_178 _x_186 ffulleth.fed4.year ind(1).ind(1).ind(80)
22 _x_187 _x_195 ffulleth.med4.year ind(1).ind(1).ind(80)
23 _x_196 fed4.med4.year ind(1).ind(1).ind(80)
24 _x_197 _x_223 ffulleth.fed4.med4.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)
Iteration 36: log likelihood = -155780.42 (not concave)
Iteration 37: log likelihood = -155780.42 (not concave)
Iteration 38: log likelihood = -155780.42 (not concave)
Iteration 39: log likelihood = -155780.42 (not concave)
Iteration 40: 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 | 6.73944 .5777206 11.67 0.000 5.607128 7.871751
_x_2 | 6.557828 .5777886 11.35 0.000 5.425383 7.690273
_x_3 | 8.580834 .5774333 14.86 0.000 7.449086 9.712583
_x_4 | .6736566 .8543854 0.79 0.430 -1.000908 2.348221
_x_5 | 1.434319 .9593896 1.50 0.135 -.4460503 3.314688
_x_6 | .1999571 1.56285 0.13 0.898 -2.863172 3.263086
_x_7 | -1.410099 .6908131 -2.04 0.041 -2.764068 -.0561305
_x_8 | -2.812962 .6924395 -4.06 0.000 -4.170119 -1.455806
_x_9 | -3.075823 1.165956 -2.64 0.008 -5.361055 -.7905908
_x_10 | -1.002165 .6907685 -1.45 0.147 -2.356047 .3517158
_x_11 | -2.497116 .6922407 -3.61 0.000 -3.853882 -1.140349
_x_12 | -2.568789 1.162932 -2.21 0.027 -4.848093 -.289485
_x_13 | -.8567484 .6901173 -1.24 0.214 -2.209353 .4958565
_x_14 | -2.501941 .6903992 -3.62 0.000 -3.855099 -1.148784
_x_15 | -2.304137 1.155578 -1.99 0.046 -4.569028 -.0392452
_x_16 | 1.158021 .80964 1.43 0.153 -.4288443 2.744886
_x_17 | -12.74245 275.0719 -0.05 0.963 -551.8734 526.3885
_x_18 | 1.541239 1.332498 1.16 0.247 -1.070409 4.152886
_x_19 | -1.742181 .6591217 -2.64 0.008 -3.034036 -.4503263
_x_20 | 10.11137 275.0699 0.04 0.971 -529.0157 549.2384
_x_21 | -3.18427 1.167109 -2.73 0.006 -5.471761 -.8967791
_x_22 | -1.143015 .6590063 -1.73 0.083 -2.434644 .1486132
_x_23 | 11.10915 275.0699 0.04 0.968 -528.0179 550.2362
_x_24 | -2.175979 1.160237 -1.88 0.061 -4.450001 .098044
_x_25 | -1.09283 .6583994 -1.66 0.097 -2.383269 .197609
_x_26 | 10.4994 275.0699 0.04 0.970 -528.6276 549.6264
_x_27 | -2.654085 1.155814 -2.30 0.022 -4.919438 -.3887311
_x_28 | 1.930809 .9844192 1.96 0.050 .001383 3.860235
_x_29 | 15.64376 275.0725 0.06 0.955 -523.4885 554.776
_x_30 | -.703926 1.566527 -0.45 0.653 -3.774262 2.36641
_x_31 | 1.219002 1.085463 1.12 0.261 -.9084671 3.346472
_x_32 | 16.61478 275.0728 0.06 0.952 -522.518 555.7475
_x_33 | 1.812701 1.518033 1.19 0.232 -1.16259 4.787992
_x_34 | 1.151764 1.678463 0.69 0.493 -2.137963 4.441491
_x_35 | 17.73444 275.0756 0.06 0.949 -521.4038 556.8726
_x_36 | 4.670335 1.945946 2.40 0.016 .8563511 8.484319
_x_37 | .4775152 .7710888 0.62 0.536 -1.033791 1.988821
_x_38 | -12.11646 275.0702 -0.04 0.965 -551.2441 527.0112
_x_39 | .9122575 1.329514 0.69 0.493 -1.693543 3.518058
_x_40 | 1.273589 .7784215 1.64 0.102 -.2520888 2.799267
_x_41 | -11.53621 275.0702 -0.04 0.967 -550.6638 527.5914
_x_42 | 1.225735 1.242363 0.99 0.324 -1.209252 3.660722
_x_43 | .671465 1.231545 0.55 0.586 -1.742319 3.085249
_x_44 | -12.15004 275.0718 -0.04 0.965 -551.2808 526.9807
_x_45 | .5949856 1.548382 0.38 0.701 -2.439787 3.629758
_x_46 | .3338527 .770809 0.43 0.665 -1.176905 1.844611
_x_47 | -12.28881 275.0702 -0.04 0.964 -551.4165 526.8389
_x_48 | 1.282356 1.319877 0.97 0.331 -1.304555 3.869267
_x_49 | 1.019499 .7779038 1.31 0.190 -.5051643 2.544162
_x_50 | -11.86524 275.0701 -0.04 0.966 -550.9928 527.2623
_x_51 | 1.193399 1.234582 0.97 0.334 -1.226336 3.613134
_x_52 | .1782504 1.228026 0.15 0.885 -2.228637 2.585138
_x_53 | -12.6792 275.0717 -0.05 0.963 -551.8099 526.4515
_x_54 | .2473091 1.5404 0.16 0.872 -2.771818 3.266437
_x_55 | .5782794 .7698883 0.75 0.453 -.930674 2.087233
_x_56 | -11.69552 275.0702 -0.04 0.966 -550.8232 527.4321
_x_57 | 1.888932 1.314997 1.44 0.151 -.6884142 4.466278
_x_58 | 1.555897 .7757238 2.01 0.045 .0355065 3.076288
_x_59 | -10.89788 275.0701 -0.04 0.968 -550.0254 528.2297
_x_60 | 2.146947 1.229021 1.75 0.081 -.26189 4.555783
_x_61 | 1.078131 1.219899 0.88 0.377 -1.312827 3.469089
_x_62 | -11.21938 275.0717 -0.04 0.967 -550.35 527.9113
_x_63 | 1.677639 1.531361 1.10 0.273 -1.323774 4.679052
_x_64 | .2959536 .9455909 0.31 0.754 -1.55737 2.149278
_x_65 | -1.155341 .7646003 -1.51 0.131 -2.65393 .3432477
_x_66 | -1.311141 .764898 -1.71 0.087 -2.810314 .1880313
_x_67 | -.8030646 .7638373 -1.05 0.293 -2.300158 .694029
_x_68 | .090156 1.233941 0.07 0.942 -2.328325 2.508637
_x_69 | -1.091078 1.455874 -0.75 0.454 -3.944539 1.762383
_x_70 | -25.31476 . . . . .
_x_71 | .310414 .9341474 0.33 0.740 -1.520481 2.141309
_x_72 | 2.015753 1.110938 1.81 0.070 -.1616448 4.193151
_x_73 | 12.89176 422.9353 0.03 0.976 -816.0463 841.8298
_x_74 | .2269047 .9343273 0.24 0.808 -1.604343 2.058153
_x_75 | 2.016265 1.111023 1.81 0.070 -.1613012 4.19383
_x_76 | 13.52448 422.9353 0.03 0.974 -815.4134 842.4624
_x_77 | .123614 .9323883 0.13 0.895 -1.703833 1.951061
_x_78 | 1.950143 1.107576 1.76 0.078 -.2206655 4.120951
_x_79 | 13.11719 422.9352 0.03 0.975 -815.8206 842.055
_x_80 | -2.802183 1.450903 -1.93 0.053 -5.6459 .0415337
_x_81 | 12.36961 275.0748 0.04 0.964 -526.7671 551.5063
_x_82 | -23.4526 1.172079 -20.01 0.000 -25.74984 -21.15537
_x_83 | 2.137327 1.089376 1.96 0.050 .0021906 4.272464
_x_84 | -9.909048 275.0721 -0.04 0.971 -549.0405 529.2224
_x_85 | 11.80243 175.0192 0.07 0.946 -331.2288 354.8337
_x_86 | 1.727103 1.089572 1.59 0.113 -.4084183 3.862624
_x_87 | -10.31844 275.0721 -0.04 0.970 -549.4499 528.813
_x_88 | 11.73769 175.0191 0.07 0.947 -331.2934 354.7688
_x_89 | 1.862171 1.088019 1.71 0.087 -.270306 3.994649
_x_90 | -9.932906 275.0721 -0.04 0.971 -549.0644 529.1986
_x_91 | 11.96198 175.019 0.07 0.946 -331.0689 354.9929
_x_92 | .3138928 1.696602 0.19 0.853 -3.011386 3.639172
_x_93 | 15.97719 16.55068 0.97 0.334 -16.46154 48.41593
_x_94 | 1.721134 1.881408 0.91 0.360 -1.966358 5.408626
_x_95 | -13.15124 275.0772 -0.05 0.962 -552.2926 525.9901
_x_96 | 23.12217 . . . . .
_x_97 | 27.1848 1.348626 20.16 0.000 24.54154 29.82806
_x_98 | 10.87482 275.0734 0.04 0.968 -528.2592 550.0088
_x_99 | 47.96806 1.516776 31.63 0.000 44.99524 50.94089
_x_100 | -.5039476 1.250366 -0.40 0.687 -2.954621 1.946726
_x_101 | 11.61002 275.0729 0.04 0.966 -527.5229 550.743
_x_102 | -4.054797 175.7942 -0.02 0.982 -348.6051 340.4955
_x_103 | -2.62022 1.387784 -1.89 0.059 -5.340226 .0997871
_x_104 | 9.964389 275.0734 0.04 0.971 -529.1695 549.0983
_x_105 | -11.99563 175.0192 -0.07 0.945 -355.027 331.0357
_x_106 | -14.40123 422.9363 -0.03 0.973 -843.3411 814.5386
_x_107 | -.8752997 504.5181 -0.00 0.999 -989.7126 987.962
_x_108 | -23.36513 457.7184 -0.05 0.959 -920.4766 873.7464
_x_109 | -.077235 1.250352 -0.06 0.951 -2.52788 2.37341
_x_110 | 11.84635 275.0729 0.04 0.966 -527.2866 550.9793
_x_111 | -4.30948 175.7941 -0.02 0.980 -348.8595 340.2405
_x_112 | -2.257531 1.387617 -1.63 0.104 -4.97721 .4621482
_x_113 | 10.20745 275.0734 0.04 0.970 -528.9265 549.3414
_x_114 | -12.10314 175.0191 -0.07 0.945 -355.1343 330.928
_x_115 | -14.52725 422.9362 -0.03 0.973 -843.467 814.4125
_x_116 | -1.366681 504.518 -0.00 0.998 -990.2039 987.4705
_x_117 | -24.30564 457.7183 -0.05 0.958 -921.417 872.8057
_x_118 | -.4321752 1.248016 -0.35 0.729 -2.878241 2.01389
_x_119 | 11.41878 275.0729 0.04 0.967 -527.7141 550.5517
_x_120 | -4.498391 175.7939 -0.03 0.980 -349.0482 340.0514
_x_121 | -2.64816 1.383712 -1.91 0.056 -5.360186 .063866
_x_122 | 9.558172 275.0734 0.03 0.972 -529.5757 548.6921
_x_123 | -12.58291 175.019 -0.07 0.943 -355.6138 330.448
_x_124 | -14.63387 422.9361 -0.03 0.972 -843.5734 814.3057
_x_125 | -1.640346 504.518 -0.00 0.997 -990.4774 987.1967
_x_126 | -24.14334 457.7182 -0.05 0.958 -921.2545 872.9678
_x_127 | 5.572932 .3339546 16.69 0.000 4.918393 6.227471
_x_128 | 5.43511 .334048 16.27 0.000 4.780388 6.089832
_x_129 | 7.495105 .3334146 22.48 0.000 6.841624 8.148585
_x_130 | -.5466211 .4726444 -1.16 0.247 -1.472987 .379745
_x_131 | -.0510632 1.056377 -0.05 0.961 -2.121525 2.019398
_x_132 | -3.087925 .6875084 -4.49 0.000 -4.435416 -1.740433
_x_133 | .0645723 .4724194 0.14 0.891 -.8613527 .9904972
_x_134 | .9738265 1.055293 0.92 0.356 -1.094509 3.042162
_x_135 | -2.164175 .6764692 -3.20 0.001 -3.490031 -.8383199
_x_136 | .1071475 .471549 0.23 0.820 -.8170716 1.031367
_x_137 | .3666443 1.054494 0.35 0.728 -1.700126 2.433415
_x_138 | -2.672041 .6687381 -4.00 0.000 -3.982743 -1.361338
_x_139 | -.2500678 .5050335 -0.50 0.620 -1.239915 .7397796
_x_140 | -.8249688 .6691735 -1.23 0.218 -2.136525 .4865872
_x_141 | -2.017957 1.067579 -1.89 0.059 -4.110374 .0744603
_x_142 | .0798997 .5049746 0.16 0.874 -.9098324 1.069632
_x_143 | -.5986912 .6690373 -0.89 0.371 -1.90998 .7125977
_x_144 | -1.39243 1.062431 -1.31 0.190 -3.474757 .689897
_x_145 | .234673 .504048 0.47 0.642 -.7532429 1.222589
_x_146 | -.5557142 .6669825 -0.83 0.405 -1.862976 .7515474
_x_147 | -1.216953 1.054832 -1.15 0.249 -3.284385 .8504786
_x_148 | -1.061259 .6154973 -1.72 0.085 -2.267611 .145094
_x_149 | -.9349294 .7630389 -1.23 0.220 -2.430458 .5605993
_x_150 | .4468531 1.168848 0.38 0.702 -1.844047 2.737753
_x_151 | -2.253357 1.134819 -1.99 0.047 -4.477561 -.0291525
_x_152 | -2.234984 1.210179 -1.85 0.065 -4.606891 .1369232
_x_153 | -1.764794 1.470449 -1.20 0.230 -4.64682 1.117233
_x_154 | .2736251 .8778975 0.31 0.755 -1.447022 1.994273
_x_155 | -.0770418 .913858 -0.08 0.933 -1.86817 1.714087
_x_156 | .3002043 1.22863 0.24 0.807 -2.107866 2.708275
_x_157 | -1.205586 .6151144 -1.96 0.050 -2.411188 .0000161
_x_158 | -1.158684 .7625333 -1.52 0.129 -2.653222 .3358539
_x_159 | -.4212113 1.163719 -0.36 0.717 -2.702059 1.859637
_x_160 | -2.455793 1.133384 -2.17 0.030 -4.677185 -.2344008
_x_161 | -2.591825 1.208918 -2.14 0.032 -4.961261 -.2223882
_x_162 | -2.661472 1.465489 -1.82 0.069 -5.533778 .2108348
_x_163 | .7268306 .8638973 0.84 0.400 -.966377 2.420038
_x_164 | .0003969 .9038096 0.00 1.000 -1.771037 1.771831
_x_165 | -.0395532 1.217227 -0.03 0.974 -2.425274 2.346167
_x_166 | -.9684716 .6139198 -1.58 0.115 -2.171732 .234789
_x_167 | -.6383061 .7601536 -0.84 0.401 -2.12818 .8515676
_x_168 | .6249562 1.155451 0.54 0.589 -1.639687 2.889599
_x_169 | -1.881275 1.132227 -1.66 0.097 -4.100399 .3378493
_x_170 | -1.668976 1.207088 -1.38 0.167 -4.034826 .6968735
_x_171 | -1.104314 1.459078 -0.76 0.449 -3.964054 1.755425
_x_172 | 1.342173 .8562841 1.57 0.117 -.336113 3.020459
_x_173 | .9439084 .8959723 1.05 0.292 -.8121651 2.699982
_x_174 | 1.500193 1.206096 1.24 0.214 -.8637124 3.864098
_x_175 | -.2387882 .558917 -0.43 0.669 -1.334245 .8566689
_x_176 | -.4506401 .5593337 -0.81 0.420 -1.546914 .6456339
_x_177 | .0651994 .557824 0.12 0.907 -1.028115 1.158514
_x_178 | 1.106452 .9619868 1.15 0.250 -.7790075 2.991911
_x_179 | -1.380936 1.219368 -1.13 0.257 -3.770853 1.00898
_x_180 | 11.66576 175.0192 0.07 0.947 -331.3656 354.6971
_x_181 | .7312732 .9621722 0.76 0.447 -1.15455 2.617096
_x_182 | -1.741071 1.21822 -1.43 0.153 -4.128739 .6465968
_x_183 | 11.9855 175.0191 0.07 0.945 -331.0456 355.0166
_x_184 | .8882458 .9603348 0.92 0.355 -.9939758 2.770467
_x_185 | -1.365276 1.216097 -1.12 0.262 -3.748781 1.018229
_x_186 | 12.20869 175.019 0.07 0.944 -330.8222 355.2396
_x_187 | -.2585625 .8109299 -0.32 0.750 -1.847956 1.330831
_x_188 | -.3697929 .9501005 -0.39 0.697 -2.231956 1.49237
_x_189 | 12.60409 422.9353 0.03 0.976 -816.3339 841.542
_x_190 | -.3207731 .8112266 -0.40 0.693 -1.910748 1.269202
_x_191 | -.3965707 .9505566 -0.42 0.677 -2.259627 1.466486
_x_192 | 12.44602 422.9353 0.03 0.977 -816.4919 841.3839
_x_193 | -.409466 .8088705 -0.51 0.613 -1.994823 1.175891
_x_194 | -.4687637 .9461933 -0.50 0.620 -2.323269 1.385741
_x_195 | 12.12572 422.9352 0.03 0.977 -816.8121 841.0635
_x_196 | -14.14741 275.0763 -0.05 0.959 -553.2871 524.9922
_x_197 | .1072744 1.152625 0.09 0.926 -2.151829 2.366378
_x_198 | .5330049 1.280488 0.42 0.677 -1.976706 3.042716
_x_199 | -13.60659 422.9366 -0.03 0.974 -842.547 815.3338
_x_200 | 2.625665 1.382599 1.90 0.058 -.0841799 5.33551
_x_201 | 3.152958 1.454585 2.17 0.030 .3020239 6.003891
_x_202 | -10.02711 422.9368 -0.02 0.981 -838.9679 818.9137
_x_203 | -10.8558 175.0212 -0.06 0.951 -353.8911 332.1795
_x_204 | -10.4474 175.0192 -0.06 0.952 -353.4788 332.584
_x_205 | -23.5568 457.7186 -0.05 0.959 -920.6687 873.5552
_x_206 | .4775916 1.152653 0.41 0.679 -1.781566 2.73675
_x_207 | .8745539 1.280564 0.68 0.495 -1.635305 3.384413
_x_208 | -12.91604 422.9365 -0.03 0.976 -841.8564 816.0243
_x_209 | 2.839332 1.381101 2.06 0.040 .1324245 5.54624
_x_210 | 3.400683 1.453506 2.34 0.019 .5518643 6.249502
_x_211 | -9.930718 422.9367 -0.02 0.981 -838.8715 819.01
_x_212 | -11.51233 175.021 -0.07 0.948 -354.5473 331.5226
_x_213 | -10.78564 175.0191 -0.06 0.951 -353.8168 332.2455
_x_214 | -24.3889 457.7185 -0.05 0.958 -921.5007 872.7229
_x_215 | .0974774 1.149985 0.08 0.932 -2.156452 2.351407
_x_216 | .4856606 1.276007 0.38 0.703 -2.015268 2.986589
_x_217 | -13.18687 422.9365 -0.03 0.975 -842.1271 815.7534
_x_218 | 2.399228 1.378087 1.74 0.082 -.3017737 5.10023
_x_219 | 2.774459 1.449166 1.91 0.056 -.0658548 5.614773
_x_220 | -10.26621 422.9367 -0.02 0.981 -839.2068 818.6744
_x_221 | -11.73658 175.0209 -0.07 0.947 -354.7713 331.2981
_x_222 | -11.24034 175.019 -0.06 0.949 -354.2712 331.7906
_x_223 | -24.31107 457.7184 -0.05 0.958 -921.4227 872.8006
_cons | -6.638992 .6666491 -9.96 0.000 -7.9456 -5.332383
------------------------------------------------------------------------------
-------------------------------------------------------------------------------
Poisson regression
-------------------------------------------------------------------------------
Dependent variable count
Optimization: ml
Number of observations: 512
Initial log likelihood: -1402408.286
Log likelihood: -155780.421
LR chi square: 2493255.731
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 1.158 0.810
17 3 -12.742 275.072
18 4 1.541 1.332
mfulleth.fed4
19 Hisp.2 -1.742** 0.659
20 Hisp.3 10.111 275.070
21 Hisp.4 -3.184** 1.167
22 black.2 -1.143 0.659
23 black.3 11.109 275.070
24 black.4 -2.176 1.160
25 white.2 -1.093 0.658
26 white.3 10.499 275.070
27 white.4 -2.654* 1.156
med4.fed4
28 2.2 1.931* 0.984
29 2.3 15.644 275.073
30 2.4 -0.704 1.567
31 3.2 1.219 1.085
32 3.3 16.615 275.073
33 3.4 1.813 1.518
34 4.2 1.152 1.678
35 4.3 17.734 275.076
36 4.4 4.670* 1.946
mfulleth.med4.fed4
37 Hisp.2.2 0.478 0.771
38 Hisp.2.3 -12.116 275.070
39 Hisp.2.4 0.912 1.330
40 Hisp.3.2 1.274 0.778
41 Hisp.3.3 -11.536 275.070
42 Hisp.3.4 1.226 1.242
43 Hisp.4.2 0.671 1.232
44 Hisp.4.3 -12.150 275.072
45 Hisp.4.4 0.595 1.548
46 black.2.2 0.334 0.771
47 black.2.3 -12.289 275.070
48 black.2.4 1.282 1.320
49 black.3.2 1.019 0.778
50 black.3.3 -11.865 275.070
51 black.3.4 1.193 1.235
52 black.4.2 0.178 1.228
53 black.4.3 -12.679 275.072
54 black.4.4 0.247 1.540
55 white.2.2 0.578 0.770
56 white.2.3 -11.696 275.070
57 white.2.4 1.889 1.315
58 white.3.2 1.556* 0.776
59 white.3.3 -10.898 275.070
60 white.3.4 2.147 1.229
61 white.4.2 1.078 1.220
62 white.4.3 -11.219 275.072
63 white.4.4 1.678 1.531
year
64 90 0.296 0.946
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 0.090 1.234
69 3.90 -1.091 1.456
70 4.90 -25.315 .
mfulleth.med4.year
71 Hisp.2.90 0.310 0.934
72 Hisp.3.90 2.016 1.111
73 Hisp.4.90 12.892 422.935
74 black.2.90 0.227 0.934
75 black.3.90 2.016 1.111
76 black.4.90 13.524 422.935
77 white.2.90 0.124 0.932
78 white.3.90 1.950 1.108
79 white.4.90 13.117 422.935
fed4.year
80 2.90 -2.802 1.451
81 3.90 12.370 275.075
82 4.90 -23.453** 1.172
mfulleth.fed4.year
83 Hisp.2.90 2.137* 1.089
84 Hisp.3.90 -9.909 275.072
85 Hisp.4.90 11.802 175.019
86 black.2.90 1.727 1.090
87 black.3.90 -10.318 275.072
88 black.4.90 11.738 175.019
89 white.2.90 1.862 1.088
90 white.3.90 -9.933 275.072
91 white.4.90 11.962 175.019
med4.fed4.year
92 2.2.90 0.314 1.697
93 2.4.90 15.977 16.551
94 3.2.90 1.721 1.881
95 3.3.90 -13.151 275.077
96 3.4.90 23.122 .
97 4.2.90 27.185** 1.349
98 4.3.90 10.875 275.073
99 4.4.90 47.968** 1.517
mfulleth.med4.fed4.year
100 Hisp.2.2.90 -0.504 1.250
101 Hisp.2.3.90 11.610 275.073
102 Hisp.2.4.90 -4.055 175.794
103 Hisp.3.2.90 -2.620 1.388
104 Hisp.3.3.90 9.964 275.073
105 Hisp.3.4.90 -11.996 175.019
106 Hisp.4.2.90 -14.401 422.936
107 Hisp.4.3.90 -0.875 504.518
108 Hisp.4.4.90 -23.365 457.718
109 black.2.2.90 -0.077 1.250
110 black.2.3.90 11.846 275.073
111 black.2.4.90 -4.309 175.794
112 black.3.2.90 -2.258 1.388
113 black.3.3.90 10.207 275.073
114 black.3.4.90 -12.103 175.019
115 black.4.2.90 -14.527 422.936
116 black.4.3.90 -1.367 504.518
117 black.4.4.90 -24.306 457.718
118 white.2.2.90 -0.432 1.248
119 white.2.3.90 11.419 275.073
120 white.2.4.90 -4.498 175.794
121 white.3.2.90 -2.648 1.384
122 white.3.3.90 9.558 275.073
123 white.3.4.90 -12.583 175.019
124 white.4.2.90 -14.634 422.936
125 white.4.3.90 -1.640 504.518
126 white.4.4.90 -24.143 457.718
ffulleth
127 Hisp 5.573** 0.334
128 black 5.435** 0.334
129 white 7.495** 0.333
ffulleth.fed4
130 Hisp.2 -0.547 0.473
131 Hisp.3 -0.051 1.056
132 Hisp.4 -3.088** 0.688
133 black.2 0.065 0.472
134 black.3 0.974 1.055
135 black.4 -2.164** 0.676
136 white.2 0.107 0.472
137 white.3 0.367 1.054
138 white.4 -2.672** 0.669
ffulleth.med4
139 Hisp.2 -0.250 0.505
140 Hisp.3 -0.825 0.669
141 Hisp.4 -2.018 1.068
142 black.2 0.080 0.505
143 black.3 -0.599 0.669
144 black.4 -1.392 1.062
145 white.2 0.235 0.504
146 white.3 -0.556 0.667
147 white.4 -1.217 1.055
ffulleth.fed4.med4
148 Hisp.2.2 -1.061 0.615
149 Hisp.2.3 -0.935 0.763
150 Hisp.2.4 0.447 1.169
151 Hisp.3.2 -2.253* 1.135
152 Hisp.3.3 -2.235 1.210
153 Hisp.3.4 -1.765 1.470
154 Hisp.4.2 0.274 0.878
155 Hisp.4.3 -0.077 0.914
156 Hisp.4.4 0.300 1.229
157 black.2.2 -1.206 0.615
158 black.2.3 -1.159 0.763
159 black.2.4 -0.421 1.164
160 black.3.2 -2.456* 1.133
161 black.3.3 -2.592* 1.209
162 black.3.4 -2.661 1.465
163 black.4.2 0.727 0.864
164 black.4.3 0.000 0.904
165 black.4.4 -0.040 1.217
166 white.2.2 -0.968 0.614
167 white.2.3 -0.638 0.760
168 white.2.4 0.625 1.155
169 white.3.2 -1.881 1.132
170 white.3.3 -1.669 1.207
171 white.3.4 -1.104 1.459
172 white.4.2 1.342 0.856
173 white.4.3 0.944 0.896
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.fed4.year
178 Hisp.2.90 1.106 0.962
179 Hisp.3.90 -1.381 1.219
180 Hisp.4.90 11.666 175.019
181 black.2.90 0.731 0.962
182 black.3.90 -1.741 1.218
183 black.4.90 11.986 175.019
184 white.2.90 0.888 0.960
185 white.3.90 -1.365 1.216
186 white.4.90 12.209 175.019
ffulleth.med4.year
187 Hisp.2.90 -0.259 0.811
188 Hisp.3.90 -0.370 0.950
189 Hisp.4.90 12.604 422.935
190 black.2.90 -0.321 0.811
191 black.3.90 -0.397 0.951
192 black.4.90 12.446 422.935
193 white.2.90 -0.409 0.809
194 white.3.90 -0.469 0.946
195 white.4.90 12.126 422.935
fed4.med4.year
196 3.2.90 -14.147 275.076
ffulleth.fed4.med4.year
197 Hisp.2.2.90 0.107 1.153
198 Hisp.2.3.90 0.533 1.280
199 Hisp.2.4.90 -13.607 422.937
200 Hisp.3.2.90 2.626 1.383
201 Hisp.3.3.90 3.153* 1.455
202 Hisp.3.4.90 -10.027 422.937
203 Hisp.4.2.90 -10.856 175.021
204 Hisp.4.3.90 -10.447 175.019
205 Hisp.4.4.90 -23.557 457.719
206 black.2.2.90 0.478 1.153
207 black.2.3.90 0.875 1.281
208 black.2.4.90 -12.916 422.937
209 black.3.2.90 2.839* 1.381
210 black.3.3.90 3.401* 1.454
211 black.3.4.90 -9.931 422.937
212 black.4.2.90 -11.512 175.021
213 black.4.3.90 -10.786 175.019
214 black.4.4.90 -24.389 457.719
215 white.2.2.90 0.097 1.150
216 white.2.3.90 0.486 1.276
217 white.2.4.90 -13.187 422.936
218 white.3.2.90 2.399 1.378
219 white.3.3.90 2.774 1.449
220 white.3.4.90 -10.266 422.937
221 white.4.2.90 -11.737 175.021
222 white.4.3.90 -11.240 175.019
223 white.4.4.90 -24.311 457.718
224 _cons -6.639** 0.667
-------------------------------------------------------------------------------
* p < .05
** p < .01
. *signs of trouble here include Huge standard errors and large coefficients. What that means is that we have asked for answers in places where there simply
is not enough data to provide an answer.
. exit, clear