In my final submit, I confirmed you the way to use the brand new and improved desk command with the statistic() choice to create a traditional desk 1. On this submit, I wish to present you the way to use the command() choice to create a desk of statistical checks. Our objective is to create the desk within the Microsoft Phrase doc under.
Create the essential desk
Let’s start by typing webuse nhanes2l to open the NHANES dataset, and let’s kind describe to look at among the variables.
. webuse nhanes2l
(Second Nationwide Well being and Diet Examination Survey)
. describe highbp age hgb hct iron albumin vitaminc zinc copper lead
> top weight bmi bpsystol bpdiast tcresult tgresult hdresult
Variable Storage Show Worth
identify kind format label Variable label
-------------------------------------------------------------------------------
highbp byte %8.0g * Hypertension
age byte %9.0g Age (years)
hgb float %9.0g Hemoglobin (g/dL)
hct float %9.0g Hematocrit (%)
iron int %9.0g Serum iron (mcg/dL)
albumin float %9.0g Serum albumin (g/dL)
vitaminc float %9.0g Serum vitamin C (mg/dL)
zinc int %9.0g Serum zinc (mcg/dL)
copper int %9.0g Serum copper (mcg/dL)
lead byte %9.0g Lead (mcg/dL)
top float %9.0g Peak (cm)
weight float %9.0g Weight (kg)
bmi float %9.0g Physique mass index (BMI)
bpsystol int %9.0g Systolic blood stress
bpdiast int %9.0g Diastolic blood stress
tcresult int %9.0g Serum ldl cholesterol (mg/dL)
tgresult int %9.0g Serum triglycerides (mg/dL)
hdresult int %9.0g Excessive density lipids (mg/dL)
The dataset contains an indicator for hypertension (highbp), age, and lots of lab measurements. We want to take a look at the null speculation that the typical age and lab measurments are the identical within the teams with and with out, hypertension. We are able to do that with Stata’s ttest command. Let’s use ttest to check the null speculation that the typical age is identical within the two teams.
. ttest age, by(highbp)
Two-sample t take a look at with equal variances
------------------------------------------------------------------------------
Group | Obs Imply Std. err. Std. dev. [95% conf. interval]
---------+--------------------------------------------------------------------
0 | 5,975 42.16502 .2169725 16.77157 41.73968 42.59037
1 | 4,376 54.97281 .2253767 14.90897 54.53095 55.41466
---------+--------------------------------------------------------------------
Mixed | 10,351 47.57965 .1692044 17.21483 47.24798 47.91133
---------+--------------------------------------------------------------------
diff | -12.80779 .3185604 -13.43223 -12.18335
------------------------------------------------------------------------------
diff = imply(0) - imply(1) t = -40.2052
H0: diff = 0 Levels of freedom = 10349
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000
The output shows many statistics, together with the two-sided p-value for the t take a look at. A few of these statistics are briefly left in reminiscence and we are able to view them by typing return listing.
. return listing
scalars:
r(stage) = 95
r(sd) = 17.21482923023818
r(sd_2) = 14.9089715191102
r(sd_1) = 16.77156676799842
r(se) = .3185603831285
r(p_u) = 1
r(p_l) = 0
r(p) = 0
r(t) = -40.20520433030012
r(df_t) = 10349
r(mu_2) = 54.97280621572212
r(N_2) = 4376
r(mu_1) = 42.16502092050209
r(N_1) = 5975
We are able to use the command() possibility in desk to run a command, reminiscent of ttest, and put the ends in a desk. The instance under exhibits the essential syntax for making a desk of outcomes from ttest. The row dimension is command, the column dimension is outcome, and we place our ttest command within the command() possibility.
. desk (command) (outcome), > command(ttest age, by(highbp)) (output omitted)
I omitted the output as a result of desk tried to incorporate all of the ends in the desk and the output doesn’t match on the display screen. Let’s be extra particular in command() concerning the statistics we want to embody in our desk. Let’s add a column named Normotensive for the imply age of individuals with out hypertension, which is saved within the scalar r(mu_1) by ttest. We are able to additionally add a column named Hypertensive for the imply age of individuals with hypertension, which is saved within the scalar r(mu_2), a column named Diff for the distinction between the group means, and a column named pvalue that shows the p-value saved in r(p).
. desk (command) (outcome),
> command(Normotensive = r(mu_1)
> Hypertensive = r(mu_2)
> Diff = (r(mu_2)-r(mu_1))
> pvalue = r(p)
> : ttest age, by(highbp))
------------------------------------------------------------------------
| Normotensive Hypertensive Diff pvalue
----------------------+-------------------------------------------------
ttest age, by(highbp) | 42.16502 54.97281 12.80779 0
------------------------------------------------------------------------
The output shows the ttest command within the first column, adopted by the means, distinction, and p-value that we laid out in our desk command. I want to change the command identify within the desk with the label Age (years). Recall that dimensions have ranges and ranges can have labels. Let’s kind acquire label listing command, all to view the degrees and labels of the dimension command.
. acquire label listing command, all
Assortment: Desk
Dimension: command
Label: Command possibility index
Degree labels:
1 ttest age, by(highbp)
The output tells us that the dimension command has one stage, 1, which is labeled ttest age, by(highbp). We are able to change the label for stage 1 utilizing acquire label ranges. Then we are able to kind acquire preview to view our up to date desk.
. acquire label ranges command 1 "Age (years)", modify
. acquire preview
--------------------------------------------------------------
| Normotensive Hypertensive Diff pvalue
------------+-------------------------------------------------
Age (years) | 42.16502 54.97281 12.80779 0
--------------------------------------------------------------
Subsequent we are able to use acquire fashion cell to vary the variety of decimals displayed in every cell and take away the best border from the primary column.
. acquire fashion cell outcome[Normotensive Hypertensive Diff],
> nformat(%6.1f)
. acquire fashion cell outcome[pvalue], nformat(%6.4f)
. acquire fashion cell border_block, border(proper, sample(nil))
. acquire preview
--------------------------------------------------------
Normotensive Hypertensive Diff pvalue
--------------------------------------------------------
Age (years) 42.2 55.0 12.8 0.0000
--------------------------------------------------------
We did it! Our desk seems good, however we’ve got solely included a take a look at for one variable. Let’s add extra.
Create a bigger desk
The apparent means so as to add extra checks to our desk could be so as to add extra command() choices. That may work as we are able to see under.
. desk (command) (outcome),
> command(Normotensive = r(mu_1) Hypertensive = r(mu_2)
> Diff = (r(mu_2)-r(mu_1)) pvalue = r(p)
> : ttest age, by(highbp))
> command(Normotensive = r(mu_1) Hypertensive = r(mu_2)
> Diff = (r(mu_2)-r(mu_1)) pvalue = r(p)
> : ttest tcresult, by(highbp))
> command(Normotensive = r(mu_1) Hypertensive = r(mu_2)
> Diff = (r(mu_2)-r(mu_1)) pvalue = r(p)
> : ttest tgresult, by(highbp))
> command(Normotensive = r(mu_1) Hypertensive = r(mu_2)
> Diff = (r(mu_2)-r(mu_1)) pvalue = r(p)
> : ttest hdresult, by(highbp))
> nformat(%6.2f Normotensive Hypertensive Diff)
> nformat(%6.4f pvalue)
--------------------------------------------------------------------------
| Normotensive Hypertensive Diff pvalue
---------------------------+----------------------------------------------
ttest age, by(highbp) | 42.17 54.97 12.81 0.0000
ttest tcresult, by(highbp) | 208.73 229.88 21.15 0.0000
ttest tgresult, by(highbp) | 129.23 166.04 36.81 0.0000
ttest hdresult, by(highbp) | 49.94 49.22 -0.73 0.0195
--------------------------------------------------------------------------
However our desk command is rising sooner than our desk. Thankfully, we are able to use a little bit programming trick to make our process simpler and our code neater. Discover that many of the code in our command() choices is similar. We are able to retailer that code in a native macro and use the native macro in our command() choices. I’ve saved the instructions in an area macro named myresults.
. native myresults "Normotensive = r(mu_1) Hypertensive = r(mu_2) Diff = (r(mu_2)-r(mu_1)) pvalue = r(p)" . show "`myresults'" Normotensive = r(mu_1) Hypertensive = r(mu_2) Diff = (r(mu_2)-r(mu_1)) pvalue = r(p)
Now we are able to change the prolonged column definitions with the native macro `myresults’ in our command() choices.
. desk (command) (outcome),
> command(`myresults' : ttest age, by(highbp))
> command(`myresults' : ttest tcresult, by(highbp))
> command(`myresults' : ttest tgresult, by(highbp))
> command(`myresults' : ttest hdresult, by(highbp))
> nformat(%6.2f Normotensive Hypertensive Diff)
> nformat(%6.0f pvalue)
--------------------------------------------------------------------------
| Normotensive Hypertensive Diff pvalue
---------------------------+----------------------------------------------
ttest age, by(highbp) | 42.17 54.97 12.81 0
ttest tcresult, by(highbp) | 208.73 229.88 21.15 0
ttest tgresult, by(highbp) | 129.23 166.04 36.81 0
ttest hdresult, by(highbp) | 49.94 49.22 -0.73 0
--------------------------------------------------------------------------
Let’s add the remainder of the lab variables to our desk utilizing our intelligent new trick.
. desk (command) (outcome),
> command(`myresults' : ttest age, by(highbp))
> command(`myresults' : ttest hgb, by(highbp))
> command(`myresults' : ttest hct, by(highbp))
> command(`myresults' : ttest iron, by(highbp))
> command(`myresults' : ttest albumin, by(highbp))
> command(`myresults' : ttest vitaminc, by(highbp))
> command(`myresults' : ttest zinc, by(highbp))
> command(`myresults' : ttest copper, by(highbp))
> command(`myresults' : ttest lead, by(highbp))
> command(`myresults' : ttest top, by(highbp))
> command(`myresults' : ttest weight, by(highbp))
> command(`myresults' : ttest bmi, by(highbp))
> command(`myresults' : ttest bpsystol, by(highbp))
> command(`myresults' : ttest bpdiast, by(highbp))
> command(`myresults' : ttest tcresult, by(highbp))
> command(`myresults' : ttest tgresult, by(highbp))
> command(`myresults' : ttest hdresult, by(highbp))
--------------------------------------------------------------------------------
| Normotensive Hypertensive Diff pvalue
---------------------------+----------------------------------------------------
ttest age, by(highbp) | 42.16502 54.97281 12.80779 0
ttest top, by(highbp) | 167.7243 167.5506 -.1736495 .3661002
ttest weight, by(highbp) | 68.26626 76.85565 8.589386 9.1e-181
ttest bmi, by(highbp) | 24.20231 27.36081 3.158506 4.5e-241
ttest bpsystol, by(highbp) | 116.485 150.5388 34.05383 0
ttest bpdiast, by(highbp) | 74.17222 92.01394 17.84172 0
ttest tcresult, by(highbp) | 208.7272 229.8798 21.1526 4.3e-105
ttest tgresult, by(highbp) | 129.2284 166.0427 36.8143 7.01e-41
ttest hdresult, by(highbp) | 49.94449 49.21784 -.7266526 .0194611
ttest hgb, by(highbp) | 14.14038 14.42436 .2839752 4.99e-25
ttest hct, by(highbp) | 41.65235 42.44271 .7903588 2.16e-27
ttest iron, by(highbp) | 101.842 96.17436 -5.667648 5.70e-17
ttest albumin, by(highbp) | 4.680295 4.654088 -.0262068 .0000896
ttest vitaminc, by(highbp) | 1.048238 1.016469 -.0317686 .0070212
ttest zinc, by(highbp) | 87.06462 85.74782 -1.316802 .0000162
ttest copper, by(highbp) | 125.0756 126.3356 1.259952 .0673572
ttest lead, by(highbp) | 13.87513 14.93369 1.058555 2.36e-09
--------------------------------------------------------------------------------
Subsequent we are able to change the labels of the degrees of the dimension command. Let’s start by itemizing the labels for every stage.
. acquire label listing command, all
Assortment: Desk
Dimension: command
Label: Command possibility index
Degree labels:
1 ttest age, by(highbp)
10 ttest top, by(highbp)
11 ttest weight, by(highbp)
12 ttest bmi, by(highbp)
13 ttest bpsystol, by(highbp)
14 ttest bpdiast, by(highbp)
15 ttest tcresult, by(highbp)
16 ttest tgresult, by(highbp)
17 ttest hdresult, by(highbp)
2 ttest hgb, by(highbp)
3 ttest hct, by(highbp)
4 ttest iron, by(highbp)
5 ttest albumin, by(highbp)
6 ttest vitaminc, by(highbp)
7 ttest zinc, by(highbp)
8 ttest copper, by(highbp)
9 ttest lead, by(highbp)
Discover that the degrees are sorted as strings fairly than numbers. It is because ranges might be strings or numbers. We are able to see the variable names related to every stage, and we are able to relabel them utilizing acquire label ranges.
. acquire label ranges command 1 "Age (years)"
> 10 "Peak (cm)"
> 11 "Weight (kg)"
> 12 "Physique Mass Index"
> 13 "Systolic Blood Stress"
> 14 "Diastolic Blood Stress"
> 15 "Serum ldl cholesterol (mg/dL)"
> 16 "Serum triglycerides (mg/dL)"
> 17 "Excessive density lipids (mg/dL)"
> 2 "Hemoglobin (g/dL)"
> 3 "Hematocrit (%)"
> 4 "Serum iron (mcg/dL)"
> 5 "Serum albumin (g/dL)"
> 6 "Serum vitamin C (mg/dL)"
> 7 "Serum zinc (mcg/dL)"
> 8 "Serum copper (mcg/dL)"
> 9 "Lead (mcg/dL)"
> , modify
. acquire preview
---------------------------------------------------------------------------------
| Normotensive Hypertensive Diff pvalue
----------------------------+----------------------------------------------------
Age (years) | 42.16502 54.97281 12.80779 0
Peak (cm) | 167.7243 167.5506 -.1736495 .3661002
Weight (kg) | 68.26626 76.85565 8.589386 9.1e-181
Physique Mass Index | 24.20231 27.36081 3.158506 4.5e-241
Systolic Blood Stress | 116.485 150.5388 34.05383 0
Diastolic Blood Stress | 74.17222 92.01394 17.84172 0
Serum ldl cholesterol (mg/dL) | 208.7272 229.8798 21.1526 4.3e-105
Serum triglycerides (mg/dL) | 129.2284 166.0427 36.8143 7.01e-41
Excessive density lipids (mg/dL) | 49.94449 49.21784 -.7266526 .0194611
Hemoglobin (g/dL) | 14.14038 14.42436 .2839752 4.99e-25
Hematocrit (%) | 41.65235 42.44271 .7903588 2.16e-27
Serum iron (mcg/dL) | 101.842 96.17436 -5.667648 5.70e-17
Serum albumin (g/dL) | 4.680295 4.654088 -.0262068 .0000896
Serum vitamin C (mg/dL) | 1.048238 1.016469 -.0317686 .0070212
Serum zinc (mcg/dL) | 87.06462 85.74782 -1.316802 .0000162
Serum copper (mcg/dL) | 125.0756 126.3356 1.259952 .0673572
Lead (mcg/dL) | 13.87513 14.93369 1.058555 2.36e-09
---------------------------------------------------------------------------------
Lastly, let’s format the numbers utilizing acquire fashion cell and take away the best border from the primary column.
. acquire fashion cell outcome[Normotensive Hypertensive Diff], nformat(%8.2f)
. acquire fashion cell outcome[pvalue], nformat(%6.4f)
. acquire fashion cell border_block, border(proper, sample(nil))
. acquire preview
-------------------------------------------------------------------------
Normotensive Hypertensive Diff pvalue
-------------------------------------------------------------------------
Age (years) 42.17 54.97 12.81 0.0000
Peak (cm) 167.72 167.55 -0.17 0.3661
Weight (kg) 68.27 76.86 8.59 0.0000
Physique Mass Index 24.20 27.36 3.16 0.0000
Systolic Blood Stress 116.49 150.54 34.05 0.0000
Diastolic Blood Stress 74.17 92.01 17.84 0.0000
Serum ldl cholesterol (mg/dL) 208.73 229.88 21.15 0.0000
Serum triglycerides (mg/dL) 129.23 166.04 36.81 0.0000
Excessive density lipids (mg/dL) 49.94 49.22 -0.73 0.0195
Hemoglobin (g/dL) 14.14 14.42 0.28 0.0000
Hematocrit (%) 41.65 42.44 0.79 0.0000
Serum iron (mcg/dL) 101.84 96.17 -5.67 0.0000
Serum albumin (g/dL) 4.68 4.65 -0.03 0.0001
Serum vitamin C (mg/dL) 1.05 1.02 -0.03 0.0070
Serum zinc (mcg/dL) 87.06 85.75 -1.32 0.0000
Serum copper (mcg/dL) 125.08 126.34 1.26 0.0674
Lead (mcg/dL) 13.88 14.93 1.06 0.0000
-------------------------------------------------------------------------
Export the desk to Microsoft Phrase
As soon as we’re pleased with the structure of our desk, we are able to export it to many alternative file codecs. I’m going to make use of putdocx, acquire fashion putdocx, and putdocx acquire to export our desk to a Microsoft Phrase doc. A lot of you’ll discover that the instructions under are nearly similar to the instructions in my earlier posts about tables.
. putdocx clear
. putdocx start
. putdocx paragraph, fashion(Title)
. putdocx textual content ("Hypertension in the USA")
. putdocx paragraph, fashion(Heading1)
. putdocx textual content ("The Nationwide Well being and Diet Examination Survey (NHANES)")
. putdocx paragraph
. putdocx textual content ("Hypertension is a serious explanation for morbidity and mortality in ")
. putdocx textual content ("the USA. This report will discover the predictors ")
. putdocx textual content ("of hypertension utilizing the NHANES dataset.")
. acquire fashion putdocx, structure(autofitcontents)
> title("Desk 2: Comparability of demographic, anthropometric, and lab outcomes by Hypertension Standing")
. putdocx acquire
(assortment Desk posted to putdocx)
. putdocx save MyTable2.docx, change
efficiently changed "MyTable2.docx"
Conclusion
On this submit, we discovered the way to use the command() possibility with the desk command to create a desk of statistical checks. The steps are easy: run the command of curiosity, kind return listing to view the statistics left in reminiscence, create your desk utilizing the row dimension command and the column dimension outcome, and place your command within the command() possibility. It’s possible you’ll want to specify customized columns for the statistics in your desk, and we discovered the way to use native macros to simplify that process.
I’ll present you the way to use the command() choice to create a desk of regression coefficients in my subsequent submit.
