[Ruth-Alma Turkson-Ocran]

I forgot to add a sample of the dataset:

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(age_p sex_r married edu_cat poor2 emp_stat) byte notcov float(usupl stay_us black_african)
80 0 0 1 1 0 2 1 . 0
56 0 1 3 3 0 2 1 1 0
46 0 1 3 3 1 2 1 . 0
22 0 0 2 3 1 2 1 . 0
31 0 0 2 3 0 2 1 . 0
56 0 0 1 3 1 2 1 . 0
58 0 0 3 3 1 2 1 . 0
55 0 0 2 1 0 2 1 . 0
56 0 1 3 3 0 2 1 . 0
58 0 1 1 2 0 2 1 . 0
78 0 0 1 2 0 2 1 2 0
62 0 1 1 3 0 2 1 . 0
33 0 0 2 3 1 2 0 . 0
59 0 1 1 3 1 1 0 . 0
68 0 0 2 1 0 2 1 . 0
20 0 0 1 1 0 1 1 . 0
73 0 0 1 2 0 2 1 . 0
64 0 0 2 3 1 2 1 . 0
67 0 0 3 2 1 2 0 . 0
55 0 0 3 3 0 1 0 . 0
60 0 0 3 3 1 2 1 . 0
62 0 0 1 2 0 1 1 . 0
27 0 0 2 3 1 1 0 . 0
76 0 0 . 3 0 2 1 1 1
51 0 0 1 3 1 1 0 . 0
35 0 0 2 3 1 2 1 2 1
34 0 1 3 3 1 2 1 . 0
45 0 1 1 3 0 1 1 . 0
67 0 0 3 3 0 2 1 2 0
24 0 0 3 3 0 2 1 . 0
48 0 0 3 3 1 2 1 . 0
38 0 0 1 3 0 2 1 2 1
27 0 0 3 3 1 2 1 . 0
54 0 0 1 2 1 2 1 . 0
61 0 0 1 2 0 2 1 . 0
33 0 1 3 3 1 2 1 . 0
56 0 0 1 2 1 2 1 . 0
28 0 0 2 1 0 2 1 . 0
77 0 0 2 3 0 2 1 . 0
81 0 0 1 3 0 2 1 . 0
32 0 0 2 2 1 2 1 2 1
58 0 1 2 3 1 2 1 . 0
50 0 1 1 3 0 2 1 . 0
35 0 1 2 2 0 2 1 . 0
52 0 0 3 3 1 2 1 . 0
59 0 1 3 3 1 2 1 . 0
35 0 1 . 1 0 1 0 2 0
59 0 0 1 1 0 2 1 . 0
50 0 1 1 3 1 2 1 . 0
29 0 0 1 1 0 2 1 2 1
52 0 0 1 3 1 2 1 2 0
25 0 0 1 3 1 2 1 . 0
59 0 0 2 3 1 2 1 . 0
30 0 0 1 1 1 2 1 1 1
36 0 1 2 3 0 2 1 . 0
28 0 0 2 2 1 1 0 . 0
53 0 1 2 2 0 1 1 . 0
25 0 1 3 2 0 2 0 . 0
28 0 0 1 1 1 2 1 2 0
34 0 1 3 3 1 2 1 1 0
46 0 0 1 2 0 2 1 . 0
35 0 1 1 3 1 1 0 2 1
78 0 0 1 2 0 2 1 . 0
81 0 1 2 3 0 2 1 . 0
32 0 1 3 3 1 2 1 . 0
30 0 0 2 2 1 1 1 . 0
20 0 0 2 1 1 1 1 1 1
70 0 1 2 3 0 2 1 . 0
30 0 1 1 1 1 2 0 1 1
38 0 0 3 3 1 2 1 . 0
36 0 0 1 2 1 2 0 1 1
78 0 0 1 3 0 2 1 . 0
74 0 0 1 2 0 2 1 . 0
27 0 0 2 3 1 1 0 . 0
44 0 0 1 3 1 1 0 . 0
65 0 1 2 3 0 2 1 . 0
36 0 0 2 3 1 2 1 . 0
25 0 0 2 1 0 2 1 . 0
63 0 0 2 2 0 1 0 . 0
32 0 0 3 3 1 2 1 . 0
71 0 1 1 3 0 2 1 . 0
82 0 1 1 3 0 2 1 . 0
65 0 1 1 3 1 2 1 . 0
46 0 1 2 2 1 1 0 . 0
31 0 0 3 1 0 2 1 1 1
55 0 1 1 3 1 1 0 2 1
55 0 1 1 2 0 1 1 2 0
57 0 1 2 2 0 2 1 . 0
27 0 1 2 3 1 2 1 2 1
37 0 1 2 3 1 2 1 . 0
31 0 0 1 3 1 1 1 1 0
69 0 0 2 2 0 2 1 . 0
21 0 0 1 1 0 1 1 . 0
20 0 0 2 3 1 2 0 . 0
60 0 0 3 3 0 2 1 . 0
36 0 1 1 2 1 1 1 2 1
36 0 1 3 2 1 2 1 . 0
24 0 0 1 3 0 1 0 . 0
66 0 0 2 2 1 2 1 . 0
26 0 1 1 1 1 1 1 . 0
end
label values age_p pep031x
label values sex_r sex_r
label def sex_r 0 "0: Females", modify
label values married married
label def married 0 "0: Not Married", modify
label def married 1 "1: Currently Married", modify
label values edu_cat edu_cat
label def edu_cat 1 "1: High School", modify
label def edu_cat 2 "2: College", modify
label def edu_cat 3 "3: Graduate School", modify
label values poor2 poor2
label def poor2 1 "1: Poor", modify
label def poor2 2 "2: Near Poor", modify
label def poor2 3 "3: Not Poor/Near Poor", modify
label values emp_stat emp_stat
label def emp_stat 0 "0: Unemployed", modify
label def emp_stat 1 "1: Employed", modify
label values notcov pep394x
label def pep394x 1 "1 Not covered", modify
label def pep394x 2 "2 Covered", modify
label values usupl usupl
label def usupl 0 "0: Do not have a usual place", modify
label def usupl 1 "1: Have a usual place", modify
label values stay_us stay_us
label def stay_us 1 "1: ≥ 5 yrs but < 10 yrs", modify
label def stay_us 2 "2: ≥ 10 years", modify
label values black_african black_african
label def black_african 0 "0: Not Black African", modify
label def black_african 1 "1: Black African", modify

Also, please delete racreci3 from the code in the previous post. The true code should be:

tabxml, ivc(age_p) ivd(sex_r married edu_cat poor2 emp_stat notcov usupl stay_us) order(age_p sex_r married edu_cat poor2 emp_stat notcov usupl stay_us) save("/location_filename_$datentime") split(sex_r) options(n per) display bold justify(c) pval sp

[Steven Spivack]

Thanks for these examples. I am running into another problem with the table1 command though, which is that it does not appear to allow for the use of analytical weights. Do you know of another command that allows for this? I tried tabout but you need to survey set the data first which is not applicable for these analyses.

[Ruth-Alma Turkson-Ocran]

Steven Spivack Depending on what you want to examine, the following commands can handle analytic weights:

tabstat (aweights, fweights)

tabulate (aweights , fweights ) ...also goes with summarize...

table ( fweights, iweights, and pweights)

These are the few that come to mind...

[Steven Spivack]

Thanks again Ruth-Alma. I like the Table1 and tabout commands because they are so efficient and allow for ANOVA and Chi2, so I was hoping they would allow the use of aweights. It looks like tabxml does not allow for weights either when the data are not svyset.

I am aware of these other commands but they are so much more tedious since I have to create 5 distinct descriptive tables which are quite long.

[Steven Spivack]

What am I doing wrong?

Did you use the osvy option? Have you installed dm49 which is required for tabxml?

[Ruth-Alma Turkson-Ocran]

Steven Spivack No; I did not use the osvy option. My dataset is a survey with pweights so I svyset the data prior to using, so I left out the osvy option.

UPDATE: I also believe I fixed my issue. I think the sex variable was tripping me up as I was trying to examine sex differences. So the code that worked for me was:

tabxml, dv(sex_r) ivc(age_p) ivd(married edu_cat poor2 emp_stat notcov usupl) order(age_p married edu_cat poor2 emp_stat notcov usupl) save("/location_filename_$datentime") display bold justify(c) subcond(black_african==1) pval sp

I also like Table1, but its huge disadvantage is that it just does not work with weights.

Regarding tabxml, I find that when I examine continuous variables, I do not get the p-values (I can get them from the output window when I use the display command). I get the codes with the categorical/dichotomous variables. I also have to sometimes run it a few times differently to get what I want because some variables end up missing in the Excel output--To expand: I run it once with only continuous variables, then I run it again with only categorical variables and then copy/paste/rearrange in the Excel output, and save.

[Steven Spivack]

[USER="18711"]I find that when I examine continuous variables, I do not get the p-values (I can get them from the output window when I use the display command). I get the codes with the categorical/dichotomous variables. I also have to sometimes run it a few times differently to get what I want because some variables end up missing in the Excel output--To expand: I run it once with only continuous variables, then I run it again with only categorical variables and then copy/paste/rearrange in the Excel output, and save.

I am not getting p values for any continuous variables either with tabxml. Instead, do you just regress each IV on the DV and then use the testparm command to get a p value, that is what I am doing now but it is not super efficient.

[Ruth-Alma Turkson-Ocran]

Steven Spivack Sorry for the delayed response. I used the option display while running the tabxml command. This lets you see the output for all the different tests being run to create the table. In the output window, I just scrolled through to find the test I was looking for and its associated p-value and put the p-value in my table - yes tedious, but was the only option I saw.

Unfortunately, I have not yet used the testparm command, so I cannot comment on that.

[Steven Spivack]

Ok thanks so much for this tip.

[bln tfte]

hi Mark,
When I use "cate" in table1_mc for Fisher's test incase categories have small numbers, I am getting an error meassage that says " exceeded memory limits using exact(1); try again with larger #;........"
Any advice?

[Mark Chatfield]

help tabulate twoway explains that the syntax in my program (or in a simple . tab cat1 cat2, exact command) needs to be something like . tab cat1 cat2, exact(2). To quote: The optional positive
integer # is a multiplier on the amount of memory that the command is permitted to consume.
The default is 1. This option should not be necessary for reasonable r x c tables. If the
command terminates with error 910, try exact(2). The maximum row or column dimension allowed
when computing Fisher's exact test is the maximum row or column dimension for tabulate (see
limits).

[Magnus Lyngbakken]

Hi,

This is a really great extension, thank you for the time and work invested. Is there a way to include number of valid observations (N) directly to the table output? Like this:

I manage to compile two separate tables, one with N_0, N_1, m_0 and m_1, and one with the actual data, but it would save time and editing if these could be combined.

Best regards,
Magnus

[Mark Chatfield]

Hi Magnus

It is possible. The key to seeing this is in . help table1_mc_dta2docx (Description).

sysuse auto, clear
table1_mc, by(foreign) vars(price conts \ weight contn %5.0f \ rep78 cate) extraspace clear
rename N_* n_*
order n_0, before(foreign_0)
order n_1, before(foreign_1)
table1_mc_dta2docx using "H:\example Table 1.docx", replace

BW,
Mark

[Wei Chen Einstein]

Hi,
I tried the table1_mc command as suggested after installing the program, but received the error Invalid 'Chen". 'Chen' is my last name. I cannot figure out what the problem is. Help, please! Thanks!

Wei

. sysuse auto, clear
(1978 Automobile Data)

. generate much_headroom = (headroom>3)

. table1_mc, by(foreign) vars(price conts \ price contln %5.0f %4.2f \ weight contn %5.0f \ rep78 cate \ much_headroom bine)
invalid 'Chen'
r(198);

[Ruth-Alma Turkson-Ocran]

Perhaps "price contln" should be

price contn