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multiple_regression_models.R
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multiple_regression_models.R
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#read in analytic table
analytic <- read.csv("analytic.csv", header=TRUE, sep=",")
#make Model 1
Model1 = lm(SLEPTIM2 ~ DRANK_WEEKLY+DRANK_MONTHLY+ BINGE_DRINKING, data=analytic)
summary(Model1)
library (broom)
InitialModel <- tidy(Model1)
write.csv(FinalModel, file = "Initial Linear Regression Model.csv")
ConfidenceLevel<-confint(Model1, level = 0.95)
write.csv(ConfidenceLevel,file = "Initial Model Confidence Level.csv")
#model2
Model2 = lm(SLEPTIM2 ~ DRANK_MONTHLY + DRANK_WEEKLY + MALE + AGE2 + AGE3 + AGE4 + AGE5 + AGE6, data=analytic)
summary(Model2)
Model3 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6, data=analytic)
summary(Model3)
#plus smoker
Model4 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER, data=analytic)
summary(Model4)
#plus Hispanic
Model5 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC, data=analytic)
summary(Model5)
#plus race
Model6 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE, data=analytic)
summary(Model6)
#plus marital status
Model7 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED, data=analytic)
summary(Model7)
#plus Gen health
Model8 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH, data=analytic)
summary(Model8)
#plus health plan
Model9 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + NOPLAN, data=analytic)
summary(Model9)
Model10 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + LOW_EDUCATION + COLLEGE, data=analytic)
summary(Model10)
Model11 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE, data=analytic)
summary(Model11)
#add income
Model12 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC1 + INC2 + INC3 + INC4 + INC5 + INC6 + INC7, data=analytic)
summary(Model12)
#remove insignificant income variables
Model13 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7, data=analytic)
summary(Model13)
#plus BMI
Model14 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + UNDERWEIGHT + OVERWEIGHT + OBESE, data=analytic)
summary(Model14)
Model15 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE, data=analytic)
summary(Model15)
#plus exercise
Model16 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + NOEXER, data=analytic)
summary(Model16)
Model17 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + MALE, data=analytic)
summary(Model17)
Model18 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE2 + AGE3, data=analytic)
summary(Model18)
Model19 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3, data=analytic)
summary(Model19)
Model20 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + UNDERWEIGHT, data=analytic)
summary(Model20)
Model21 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION, data=analytic)
summary(Model21)
Model22 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + NOEXER, data=analytic)
summary(Model22)
Model23 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + NOPLAN, data=analytic)
summary(Model23)
Model24 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + AGE2, data=analytic)
summary(Model24)
Model25 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + INC1, data=analytic)
summary(Model25)
Model26 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + INC3, data=analytic)
summary(Model26)
Model27 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + INC4, data=analytic)
summary(Model27)
Model28 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + INC5, data=analytic)
summary(Model28)
Model29 = lm(SLEPTIM2 ~ DRANK_WEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + INC6, data=analytic)
summary(Model29)
Model30 = lm(SLEPTIM2 ~ DRANK_WEEKLY +BINGE_DRINKING + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVER_MARRIED + FORMERLY_MARRIED
+ FAIRHLTH + POORHLTH + COLLEGE
+ INC2 + INC7 + OVERWEIGHT + OBESE + AGE3 + LOW_EDUCATION + MALE, data=analytic)
summary(Model30)
#Remove the non significant variables
###FINAL MODEL
FinalLinearRegressionModel = lm(SLEPTIM2 ~ DRANK_MONTHLY + BINGE_DRINKING + DRANK_WEEKLY + AGE5 + AGE6
+ HISPANIC + BLACK + ASIAN + OTHRACE + FORMERLY_MARRIED + NEVER_MARRIED
+ COLLEGE+ OVERWEIGHT + OBESE + SMOKER + FAIRHLTH + POORHLTH, data=analytic)
summary(FinalLinearRegressionModel)
#output as CSV
library (broom)
FinalModel <- tidy(FinalLinearRegressionModel)
write.csv(FinalModel, file = "Final Linear Regression Model.csv")
ConfidenceLevel<-confint(FinalLinearRegressionModel, level = 0.95)
write.csv(ConfidenceLevel,file = "Final Model Confidence Level.csv")