Stata svy:regres Command for Multiple Linear Regression
| Statements | Explanation |
|---|---|
| use "C:\Stata\tutorial\analysis_data.dta", clear |
Use the use command to load the Stata-format dataset. Use the clear option to replace any data in memory. |
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svyset sdmvpsu [pweight=wtmec4yr], strata(sdmvstra) vce(linearized) |
Use the svyset command to declare the survey design for the dataset. Specifiy the psu variable sdmvpsu. Use the [pweight=] option to account for the unequal probability of sampling and non-response. In this example, the MEC weight for four years of data (wtmec4yr) is used. Use the strata ( ) option to specify the stratum identifier (sdmvstra). Use the vce( ) option to specific the variance estimation method (linearized) for Taylor linearization. |
|
char ridreth1[omit]3 char smoker[omit]3 char educ[omit]3 char bmicat[omit]2 |
Use these options to choose your reference group for the categorical variables. For example, the 3rd race/ethnicity (ridreth1) category (non-Hispanic White) is chosen as the reference group. If you do not specify the reference group options, Stata will choose the lowest numbered group by default. |
| xi: svy, subpop(if ridageyr >=20) vce(linearized): regress lbdhdl i.riagendr i.ridreth1 ridageyr bmxbmi i.smoker i.educ i.bmicat |
Use the xi command to expand terms containing categorical variables (denoted i.varname) into indicator (also called dummy) variable sets. Use the svy: regress command to perform multiple linear regression to specify the dependent variable HDL cholesterol (lbdhdl) and independent variables, including: gender, race, age, body mass index, smoking, education and BMI category. |
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test ******************************* test _Iriagendr_2, nosvyadjust test _Iridreth1_1 _Iridreth1_2 _Iridreth1_4 _Iridreth1_5, nosvyadjust test ridageyr, nosvyadjust test _Ibmicat_1 _Ibmicat_3 _Ibmicat_4, nosvyadjust test _Ismoker_1 _Ismoker_2, nosvyadjust test _Ieduc_1 _Ieduc_2, nosvyadjust ******************************* test _Ismoker_1 - _Ismoker_2 =0 |
Use the test postestimation command to produce the Wald F statistic and the corresponding p-value. Use the nosvyadjust option to produce the unadjusted Wald F. In the example, the command test is used to test all coefficient together; all coefficients separately; and to test the hypothesis that HDL cholesterol for non-smokers is the same as that for past smokers. |
