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  • #1

    Making predictor variables to have the same frequencies as the outcome variable (i.e. freq 39165=study sample)

    Hi. I created a composite variable "TBHIV_Knowledge" consisting of a total frequency of 39165 (which is people who answered all the 5 questions in my composite variable, which named: Prevention, Transmission1, Transmission2, Cure, coinfection). 39165 is my study sample. However, when I table my predictor variables, they show higher frequencies (e.g. Age 66200, Sex 65000, Employment 39,468 and so on). How can I generate these variables in a way that only shows a total frequencies of 39165 (i.e. each predictor variable has the same frequency as the outcome variable, TBHIV_Knowledge)?

    My data looks like this
    Code:
    * Example generated by -dataex-. To install: ssc install dataex
clear
input float TB_HIV_Knowledge byte(Prevention Transmission_1 Transmission_2 Cure Coinfection) int Age_Recode byte(Sex Residence_Type Region Marital_Status Employment) long Gross_Income2
. . . . . .  1 1 1 1 . . .
. . . . . .  0 2 2 5 . . .
. . . . . .  0 1 1 7 . . .
. . . . . .  0 2 1 7 . . .
. . . . . .  0 2 1 7 . . .
. . . . . .  0 2 1 7 . . .
. . . . . .  2 2 1 5 . . .
. . . . . .  1 1 1 7 . . .
. . . . . .  3 2 1 1 . . .
. . . . . . 10 2 1 7 . . .
. . . . . .  7 2 1 7 . . .
. . . . . .  3 2 1 7 . . .
. . . . . . 10 1 1 7 . . .
. . . . . .  0 2 1 7 . . .
. . . . . .  0 1 1 7 . . .
. . . . . .  0 2 1 7 . . .
. . . . . .  3 2 1 1 . . .
. . . . . .  3 1 2 3 . . .
. . . . . .  0 1 2 4 . . .
. . . . . .  5 2 2 4 . . .
. . . . . . 10 . 2 5 . . .
. . . . . .  0 2 2 5 . . .
. . . . . . 10 2 2 5 . . .
. . . . . . 10 2 1 5 . . .
. . . . . .  0 2 1 8 . . .
. . . . . .  1 1 1 8 . . .
. . . . . .  0 2 1 8 . . .
. . . . . .  0 2 2 8 . . .
. . . . . .  2 1 1 8 . . .
. . . . . .  3 1 1 8 . . .
. . . . . .  0 1 1 8 . . .
. . . . . .  0 1 1 8 . . .
1 1 1 1 2 1  9 1 2 1 1 4 .
. . . . . .  7 1 2 1 1 . .
1 1 1 1 3 2  7 2 2 1 2 4 .
1 1 1 1 1 2  2 1 2 1 2 4 .
3 3 3 3 3 3  1 1 2 1 2 1 .
. . . . . .  1 1 2 1 2 . .
. . . . . .  0 1 2 1 . . .
. . . . . .  0 2 2 1 . . .
1 1 1 1 1 2  1 2 2 1 2 3 1
. . . . . .  0 2 2 5 . . .
1 1 1 1 1 2  4 2 2 1 1 3 .
1 1 1 1 1 2 10 2 2 1 1 1 .
1 1 1 1 1 2 10 1 2 1 1 1 .
. . . . . .  0 1 2 1 . . .
. . . . . .  2 1 2 1 2 1 .
1 2 1 1 2 2 10 1 2 1 2 4 1
2 2 3 1 3 2  4 2 2 1 2 4 .
2 1 1 3 3 2  4 1 2 1 2 4 1
2 2 2 2 2 2  3 2 2 1 2 1 .
1 1 1 1 2 2  7 2 2 1 2 4 1
1 1 1 1 3 2  3 1 2 1 2 1 .
1 1 1 1 1 2  1 2 2 1 2 3 .
1 1 1 1 1 2  6 2 2 1 2 4 .
1 1 1 1 1 2  5 1 2 1 2 4 1
. . . . . .  0 2 2 1 . . .
. . . . . .  0 1 2 1 . . .
. . . . . .  0 1 2 1 . . .
2 3 2 1 2 3  2 2 2 1 1 4 1
2 1 3 1 3 3  8 1 2 1 1 4 .
2 1 3 3 3 3 10 2 2 1 1 1 .
1 1 1 1 1 3  2 1 2 1 2 4 1
. . . . . .  0 1 2 1 . . .
1 1 1 1 3 2  5 1 2 1 1 4 1
1 1 1 1 1 2  4 2 2 1 1 4 1
1 1 1 1 2 2  1 1 2 1 2 4 1
1 1 1 1 3 2  1 2 2 1 2 3 .
. . . . . .  0 2 2 1 . . .
. . . . . .  0 1 2 1 . . .
1 1 1 1 1 2  7 1 2 1 2 2 1
1 1 2 1 1 2  4 1 2 2 3 1 .
1 1 1 1 2 2  4 2 2 1 2 1 .
. . . . . .  0 1 2 1 . . .
1 1 2 1 1 2  4 1 2 2 3 1 .
. . . . . .  0 1 2 1 . . .
. . . . . .  0 2 2 1 . . .
. 3 3 . 3 3  9 1 2 1 3 1 .
1 2 1 1 1 2  6 1 2 1 2 4 1
1 1 1 1 1 2  5 2 2 1 2 4 1
. . . . . .  0 1 2 1 . . .
1 1 1 1 1 2  2 2 2 1 2 4 .
3 3 3 3 3 3  9 1 2 1 1 4 1
3 3 3 3 3 3 10 2 2 1 1 1 1
2 1 3 1 3 2  1 2 2 1 2 3 .
1 1 1 1 2 2  8 1 2 1 1 1 .
1 1 1 1 3 2 10 1 2 1 1 4 3
. . . . . .  0 1 2 1 . . .
. . . . . .  0 1 2 1 . . .
1 1 1 1 1 2  5 2 2 2 2 1 .
. . . . . .  0 2 2 1 . . .
. . . . . .  0 1 2 1 . . .
1 1 1 1 1 1  4 1 1 1 2 4 1
1 1 1 1 3 1  2 1 1 1 2 4 1
1 1 1 1 1 2  2 1 1 1 2 4 1
2 1 3 3 1 2  2 1 1 1 2 4 1
2 2 2 3 2 2  2 1 1 1 2 4 1
1 1 1 1 1 2  3 1 1 1 2 1 .
. . . . . .  0 1 1 1 . . .
1 1 1 1 1 2  9 2 1 1 2 1 1
end
label values TB_HIV_Knowledge TB_HIV_Knowledge
label def TB_HIV_Knowledge 1 "True", modify
label def TB_HIV_Knowledge 2 "False", modify
label def TB_HIV_Knowledge 3 "Do Not Know", modify
label values Prevention q3_2f
label def q3_2f 1 "True", modify
label def q3_2f 2 "False", modify
label def q3_2f 3 "Do not know", modify
label values Transmission_1 q3_1b
label def q3_1b 1 "True", modify
label def q3_1b 2 "False", modify
label def q3_1b 3 "Do not know", modify
label values Transmission_2 q3_1c
label def q3_1c 1 "True", modify
label def q3_1c 2 "False", modify
label def q3_1c 3 "Do not know", modify
label values Cure q3_3e
label def q3_3e 1 "True", modify
label def q3_3e 2 "False", modify
label def q3_3e 3 "Do not know", modify
label values Coinfection q3_4
label def q3_4 1 "True", modify
label def q3_4 2 "False", modify
label def q3_4 3 "Do not know", modify
label values Age_Recode Age_Recode
label def Age_Recode 0 "Not_In_The-Study", modify
label def Age_Recode 1 "15-19", modify
label def Age_Recode 2 "20-24", modify
label def Age_Recode 3 "25-29", modify
label def Age_Recode 4 "30-34", modify
label def Age_Recode 5 "35-39", modify
label def Age_Recode 6 "40-44", modify
label def Age_Recode 7 "45-49", modify
label def Age_Recode 8 "50-54", modify
label def Age_Recode 9 "55-59", modify
label def Age_Recode 10 "60+", modify
label values Sex sex_q
label def sex_q 1 "Male", modify
label def sex_q 2 "Female", modify
label values Residence_Type Residence_Type
label def Residence_Type 1 "Urban", modify
label def Residence_Type 2 "Rural", modify
label values Region province
label def province 1 "Western Cape", modify
label def province 2 "Eastern Cape", modify
label def province 3 "Northern Cape", modify
label def province 4 "Free State", modify
label def province 5 "KwaZulu-Natal", modify
label def province 7 "Gauteng", modify
label def province 8 "Mpumalanga", modify
label values Marital_Status Marital_Status
label def Marital_Status 1 "Married", modify
label def Marital_Status 2 "Never Married", modify
label def Marital_Status 3 "No longer Married", modify
label values Employment q1_7
label def q1_7 1 "Unemployed", modify
label def q1_7 2 "Sick/disabled and unable to work", modify
label def q1_7 3 "Student/pupil/learner", modify
label def q1_7 4 "Employed / Self Employed", modify
label values Gross_Income2 Gross_Income2
label def Gross_Income2 1 "Poor >R3500", modify
label def Gross_Income2 3 "Working Class R8100-R22000", modify
    Tags: None
  • #2
    your question is not completely clear to me but I think the following will do what you want
    Code:
    qui regress TB_HIV_Knowledge
gen byte keep=e(sample)
    now you can have the same sample as in your main variable by just adding "if keep" to you later commands; or, if you want to make a new data of the correct size:
    Code:
    keep if keep
save newdata

    Comment

    • #3
      Thank you very much Rich Goldstein! The codes worked perfectly well. This is exactly what I wanted.

      Comment

      • #4
        Originally posted by Sonwabile Mbuma View Post
        Hi. I created a composite variable "TBHIV_Knowledge" consisting of a total frequency of 39165 (which is people who answered all the 5 questions in my composite variable, which named: Prevention, Transmission1, Transmission2, Cure, coinfection). 39165 is my study sample. However, when I table my predictor variables, they show higher frequencies (e.g. Age 66200, Sex 65000, Employment 39,468 and so on). How can I generate these variables in a way that only shows a total frequencies of 39165 (i.e. each predictor variable has the same frequency as the outcome variable, TBHIV_Knowledge)?

        My data looks like this
        Code:
        * Example generated by -dataex-. To install: ssc install dataex
clear
input float TB_HIV_Knowledge byte(Prevention Transmission_1 Transmission_2 Cure Coinfection) int Age_Recode byte(Sex Residence_Type Region Marital_Status Employment) long Gross_Income2
. . . . . . 1 1 1 1 . . .
. . . . . . 0 2 2 5 . . .
. . . . . . 0 1 1 7 . . .
. . . . . . 0 2 1 7 . . .
. . . . . . 0 2 1 7 . . .
. . . . . . 0 2 1 7 . . .
. . . . . . 2 2 1 5 . . .
. . . . . . 1 1 1 7 . . .
. . . . . . 3 2 1 1 . . .
. . . . . . 10 2 1 7 . . .
. . . . . . 7 2 1 7 . . .
. . . . . . 3 2 1 7 . . .
. . . . . . 10 1 1 7 . . .
. . . . . . 0 2 1 7 . . .
. . . . . . 0 1 1 7 . . .
. . . . . . 0 2 1 7 . . .
. . . . . . 3 2 1 1 . . .
. . . . . . 3 1 2 3 . . .
. . . . . . 0 1 2 4 . . .
. . . . . . 5 2 2 4 . . .
. . . . . . 10 . 2 5 . . .
. . . . . . 0 2 2 5 . . .
. . . . . . 10 2 2 5 . . .
. . . . . . 10 2 1 5 . . .
. . . . . . 0 2 1 8 . . .
. . . . . . 1 1 1 8 . . .
. . . . . . 0 2 1 8 . . .
. . . . . . 0 2 2 8 . . .
. . . . . . 2 1 1 8 . . .
. . . . . . 3 1 1 8 . . .
. . . . . . 0 1 1 8 . . .
. . . . . . 0 1 1 8 . . .
1 1 1 1 2 1 9 1 2 1 1 4 .
. . . . . . 7 1 2 1 1 . .
1 1 1 1 3 2 7 2 2 1 2 4 .
1 1 1 1 1 2 2 1 2 1 2 4 .
3 3 3 3 3 3 1 1 2 1 2 1 .
. . . . . . 1 1 2 1 2 . .
. . . . . . 0 1 2 1 . . .
. . . . . . 0 2 2 1 . . .
1 1 1 1 1 2 1 2 2 1 2 3 1
. . . . . . 0 2 2 5 . . .
1 1 1 1 1 2 4 2 2 1 1 3 .
1 1 1 1 1 2 10 2 2 1 1 1 .
1 1 1 1 1 2 10 1 2 1 1 1 .
. . . . . . 0 1 2 1 . . .
. . . . . . 2 1 2 1 2 1 .
1 2 1 1 2 2 10 1 2 1 2 4 1
2 2 3 1 3 2 4 2 2 1 2 4 .
2 1 1 3 3 2 4 1 2 1 2 4 1
2 2 2 2 2 2 3 2 2 1 2 1 .
1 1 1 1 2 2 7 2 2 1 2 4 1
1 1 1 1 3 2 3 1 2 1 2 1 .
1 1 1 1 1 2 1 2 2 1 2 3 .
1 1 1 1 1 2 6 2 2 1 2 4 .
1 1 1 1 1 2 5 1 2 1 2 4 1
. . . . . . 0 2 2 1 . . .
. . . . . . 0 1 2 1 . . .
. . . . . . 0 1 2 1 . . .
2 3 2 1 2 3 2 2 2 1 1 4 1
2 1 3 1 3 3 8 1 2 1 1 4 .
2 1 3 3 3 3 10 2 2 1 1 1 .
1 1 1 1 1 3 2 1 2 1 2 4 1
. . . . . . 0 1 2 1 . . .
1 1 1 1 3 2 5 1 2 1 1 4 1
1 1 1 1 1 2 4 2 2 1 1 4 1
1 1 1 1 2 2 1 1 2 1 2 4 1
1 1 1 1 3 2 1 2 2 1 2 3 .
. . . . . . 0 2 2 1 . . .
. . . . . . 0 1 2 1 . . .
1 1 1 1 1 2 7 1 2 1 2 2 1
1 1 2 1 1 2 4 1 2 2 3 1 .
1 1 1 1 2 2 4 2 2 1 2 1 .
. . . . . . 0 1 2 1 . . .
1 1 2 1 1 2 4 1 2 2 3 1 .
. . . . . . 0 1 2 1 . . .
. . . . . . 0 2 2 1 . . .
. 3 3 . 3 3 9 1 2 1 3 1 .
1 2 1 1 1 2 6 1 2 1 2 4 1
1 1 1 1 1 2 5 2 2 1 2 4 1
. . . . . . 0 1 2 1 . . .
1 1 1 1 1 2 2 2 2 1 2 4 .
3 3 3 3 3 3 9 1 2 1 1 4 1
3 3 3 3 3 3 10 2 2 1 1 1 1
2 1 3 1 3 2 1 2 2 1 2 3 .
1 1 1 1 2 2 8 1 2 1 1 1 .
1 1 1 1 3 2 10 1 2 1 1 4 3
. . . . . . 0 1 2 1 . . .
. . . . . . 0 1 2 1 . . .
1 1 1 1 1 2 5 2 2 2 2 1 .
. . . . . . 0 2 2 1 . . .
. . . . . . 0 1 2 1 . . .
1 1 1 1 1 1 4 1 1 1 2 4 1
1 1 1 1 3 1 2 1 1 1 2 4 1
1 1 1 1 1 2 2 1 1 1 2 4 1
2 1 3 3 1 2 2 1 1 1 2 4 1
2 2 2 3 2 2 2 1 1 1 2 4 1
1 1 1 1 1 2 3 1 1 1 2 1 .
. . . . . . 0 1 1 1 . . .
1 1 1 1 1 2 9 2 1 1 2 1 1
end
label values TB_HIV_Knowledge TB_HIV_Knowledge
label def TB_HIV_Knowledge 1 "True", modify
label def TB_HIV_Knowledge 2 "False", modify
label def TB_HIV_Knowledge 3 "Do Not Know", modify
label values Prevention q3_2f
label def q3_2f 1 "True", modify
label def q3_2f 2 "False", modify
label def q3_2f 3 "Do not know", modify
label values Transmission_1 q3_1b
label def q3_1b 1 "True", modify
label def q3_1b 2 "False", modify
label def q3_1b 3 "Do not know", modify
label values Transmission_2 q3_1c
label def q3_1c 1 "True", modify
label def q3_1c 2 "False", modify
label def q3_1c 3 "Do not know", modify
label values Cure q3_3e
label def q3_3e 1 "True", modify
label def q3_3e 2 "False", modify
label def q3_3e 3 "Do not know", modify
label values Coinfection q3_4
label def q3_4 1 "True", modify
label def q3_4 2 "False", modify
label def q3_4 3 "Do not know", modify
label values Age_Recode Age_Recode
label def Age_Recode 0 "Not_In_The-Study", modify
label def Age_Recode 1 "15-19", modify
label def Age_Recode 2 "20-24", modify
label def Age_Recode 3 "25-29", modify
label def Age_Recode 4 "30-34", modify
label def Age_Recode 5 "35-39", modify
label def Age_Recode 6 "40-44", modify
label def Age_Recode 7 "45-49", modify
label def Age_Recode 8 "50-54", modify
label def Age_Recode 9 "55-59", modify
label def Age_Recode 10 "60+", modify
label values Sex sex_q
label def sex_q 1 "Male", modify
label def sex_q 2 "Female", modify
label values Residence_Type Residence_Type
label def Residence_Type 1 "Urban", modify
label def Residence_Type 2 "Rural", modify
label values Region province
label def province 1 "Western Cape", modify
label def province 2 "Eastern Cape", modify
label def province 3 "Northern Cape", modify
label def province 4 "Free State", modify
label def province 5 "KwaZulu-Natal", modify
label def province 7 "Gauteng", modify
label def province 8 "Mpumalanga", modify
label values Marital_Status Marital_Status
label def Marital_Status 1 "Married", modify
label def Marital_Status 2 "Never Married", modify
label def Marital_Status 3 "No longer Married", modify
label values Employment q1_7
label def q1_7 1 "Unemployed", modify
label def q1_7 2 "Sick/disabled and unable to work", modify
label def q1_7 3 "Student/pupil/learner", modify
label def q1_7 4 "Employed / Self Employed", modify
label values Gross_Income2 Gross_Income2
label def Gross_Income2 1 "Poor >R3500", modify
label def Gross_Income2 3 "Working Class R8100-R22000", modify
        Hi, ma you please help. I need further assistance. Is there something more I can do besides dropping lower frequencies (I need all the below listed variables)? The code was able to equalize Prevention, transmission1, Transmission2, Cure, Coinfection, Age, sex, residence, and Province to 39165 (frequency of TBHIV_Knowledge).
        However, observations were deleted, variables that had lower frequencies were not changed to values lower than 39165.

        Different Sample/frequencies
        Marital Status = 39157
        Employment = 39116
        Highest Education = 28119
        Gross Income = 19569

        Comment

        • #5
          Originally posted by Rich Goldstein View Post
          your question is not completely clear to me but I think the following will do what you want
          Code:
          qui regress TB_HIV_Knowledge
gen byte keep=e(sample)
          now you can have the same sample as in your main variable by just adding "if keep" to you later commands; or, if you want to make a new data of the correct size:
          Code:
          keep if keep
save newdata
          Hi, ma you please help. I need further assistance. Is there something more I can do besides dropping lower frequencies (I need all the below listed variables)? The code was able to equalize Prevention, transmission1, Transmission2, Cure, Coinfection, Age, sex, residence, and Province to 39165 (frequency of TBHIV_Knowledge).
          However, observations were deleted, variables that had lower frequencies were not changed to values lower than 39165.

          Different Sample/frequencies
          Marital Status = 39157
          Employment = 39116
          Highest Education = 28119
          Gross Income = 19569

          Comment

          • #6
            Use all relevant variables to generate the estimation sample. Assuming these are all numerical variables in the dataset:

            Code:
            qui ds, has(type numeric)
qui regress `r(varlist)'
gen sample= e(sample)
            • 1 like

            Comment

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