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.Rhistory
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summary(Model7)
# Add general health
Model8 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH, data=brfss)
summary(Model8)
# Health plan
Model9 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + NOPLAN, data=brfss)
summary(Model9)
# Add education levels
Model10 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + LOWED + SOMECOLL, data=brfss)
summary(Model10)
# somecol is significant, so Keep Some college
Model11 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL, data=brfss)
summary(Model11)
# Add income
Model12 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC1 + INC2 + INC3 + INC4 + INC5 + INC6 + INC7, data=brfss)
summary(Model12)
# remove insignificant income variables
Model13 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7, data=brfss)
summary(Model13)
# Add BMI
Model14 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + UNDWT + OVWT + OBESE, data=brfss)
summary(Model14)
# take out insignificant UNDWT
Model15 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE, data=brfss)
summary(Model15)
# Add exercise
Model16 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + NOEXER, data=brfss)
summary(Model16)
# Add back male
Model17 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + MALE, data=brfss)
summary(Model17)
# Add back AGE2 and AGE3
Model18 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE2 + AGE3, data=brfss)
summary(Model18)
Model19 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3, data=brfss)
summary(Model19)
# Add back UNDWT
Model20 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + UNDWT, data=brfss)
summary(Model20)
# Add back LOWED
Model21 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED, data=brfss)
summary(Model21)
# Add back NOEXER
Model22 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + NOEXER, data=brfss)
summary(Model22)
# Add back NOPLAN
Model23 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + NOPLAN, data=brfss)
summary(Model23)
# Add back AGE2
Model24 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + AGE2, data=brfss)
summary(Model24)
# Add back INC1
Model25 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + INC1, data=brfss)
summary(Model25)
#Add back INC3
Model26 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + INC3, data=brfss)
summary(Model26)
# Add back INC4
Model27 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + INC4, data=brfss)
summary(Model27)
# Add back INC5
Model28 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + INC5, data=brfss)
summary(Model28)
# Add back INC6
Model29 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + INC6, data=brfss)
summary(Model29)
# Add back MALE
Model30 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + AGE3 + LOWED + MALE, data=brfss)
summary(Model30)
Model31 = lm(SLEPTIM2 ~ DRKWEEKLY + AGE3 + AGE4 + AGE5 + AGE6
+ SMOKER + HISPANIC + BLACK + ASIAN + OTHRACE + NEVERMAR + FORMERMAR
+ FAIRHLTH + POORHLTH + LOWED + SOMECOLL
+ INC2 + INC7 + OVWT + OBESE + DRKMONTHLY, data=brfss)
summary(Model31)
### FINAL MODEL
FinalLinearRegressionModel = lm(SLEPTIM2 ~ DRKMONTHLY + DRKWEEKLY + AGE3 + AGE4 + AGE5 + AGE6
+ HISPANIC + BLACK + ASIAN + OTHRACE + FORMERMAR + NEVERMAR
+ LOWED + SOMECOLL + INC2 + INC7
+ OVWT + OBESE + SMOKER + FAIRHLTH + POORHLTH, data=brfss)
summary(FinalLinearRegressionModel)
# Output as CSV
Tidy_FinalModel <- tidy(FinalLinearRegressionModel)
write.csv(Tidy_FinalModel, file = "FinalLinearRegressionModel.csv")
# Coefficient plot
library(arm)
# Default plot
coefplot(FinalLinearRegressionModel)
# Final model diagnostics
layout(matrix(c(1,2,3,4),2,2)) # optional 4 graphs/page
plot(FinalLinearRegressionModel,
main = "Final Linear Regression Model")
# Model fit diagnostics
library(gvlma)
install.packages("arm")
install.packages("gblma")
# Default plot
coefplot(FinalLinearRegressionModel)
# Coefficient plot
library(arm)
# Model fit diagnostics
library(gvlma)
install.packages("gvlma")
# Model fit diagnostics
library(gvlma)
# Default plot
coefplot(FinalLinearRegressionModel)
# Final model diagnostics
layout(matrix(c(1,2,3,4),2,2)) # optional 4 graphs/page
plot(FinalLinearRegressionModel,
main = "Final Linear Regression Model")
gvmodel <- gvlma(FinalLinearRegressionModel)
summary(gvmodel)
# Model 1
LogModel1 <- glm(ASTHMA4 ~ DRKMONTHLY + DRKWEEKLY, data=brfss, family = "binomial")
summary(LogModel1)
# Save as tidy summary
Tidy_LogModel1 <- tidy(LogModel1)
Tidy_LogModel1
# Add calculations
Tidy_LogModel1$OR <- exp(Tidy_LogModel1$estimate)
Tidy_LogModel1$LL <- exp(Tidy_LogModel1$estimate - (1.96 * Tidy_LogModel1$std.error))
Tidy_LogModel1$UL <- exp(Tidy_LogModel1$estimate + (1.96 * Tidy_LogModel1$std.error))
Tidy_LogModel1
# Export the summary table
write.csv(Tidy_LogModel1, file = "LogisticRegressionModel1.csv")
# Model 2
LogModel2 <- glm(ASTHMA4 ~ DRKMONTHLY + DRKWEEKLY + MALE + AGE2 + AGE3 + AGE4 + AGE5 + AGE6, data=brfss, family = "binomial")
summary(LogModel2)
# Add calculations
Tidy_LogModel2 <- tidy(LogModel2)
Tidy_LogModel2$OR <- exp(Tidy_LogModel2$estimate)
Tidy_LogModel2$LL <- exp(Tidy_LogModel2$estimate - (1.96 * Tidy_LogModel2$std.error))
Tidy_LogModel2$UL <- exp(Tidy_LogModel2$estimate + (1.96 * Tidy_LogModel2$std.error))
# Export the summary table
write.csv(Tidy_LogModel2, file = "LogisticRegressionModel2.csv")
write.csv(Tidy_FinalModel, file="data/FinalLinearRegressionModel.csv")
# Export the summary table
write.csv(Tidy_LogModel1, file = "data/LogisticRegressionModel1.csv")
# Export the summary table
write.csv(Tidy_LogModel2, file = "data/LogisticRegressionModel2.csv")
# All variables are significant in model 2
LogModel3 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE, data=brfss, family = "binomial")
summary(LogModel3)
# Add smoker
LogModel4 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER, data=brfss, family = "binomial")
summary(LogModel4)
# Add Hispanic
LogModel5 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER + HISPANIC, data=brfss, family = "binomial")
summary(LogModel5)
#Add races
LogModel6 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ BLACK + ASIAN + OTHRACE, data=brfss, family = "binomial")
summary(LogModel6)
# Keep only OTHRACE
LogModel7 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE, data=brfss, family = "binomial")
summary(LogModel7)
# Add marital
LogModel8 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE + NEVERMAR + FORMERMAR, data=brfss, family = "binomial")
summary(LogModel8)
# Add generalhealth
LogModel9 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE + NEVERMAR + FORMERMAR + FAIRHLTH + POORHLTH, data=brfss, family = "binomial")
summary(LogModel9)
# FORMERMAR not significant, take out FORMERMAR
LogModel10 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH, data=brfss, family = "binomial")
summary(LogModel10)
# Add health plan
LogModel11 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH + NOPLAN, data=brfss, family = "binomial")
summary(LogModel11)
# Add education
LogModel12 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ LOWED + SOMECOLL, data=brfss, family = "binomial")
summary(LogModel12)
# Add income
LogModel13 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4 + INC5 + INC6 + INC7, data=brfss, family = "binomial")
summary(LogModel13)
# Take out INC5 through INC7
LogModel14 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE + SMOKER
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4, data=brfss, family = "binomial")
summary(LogModel14)
# Take out SMOKER
LogModel15 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4, data=brfss, family = "binomial")
summary(LogModel15)
# Add obesity
LogModel16 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ UNDWT + OVWT + OBESE, data=brfss, family = "binomial")
summary(LogModel16)
# Remove UNDWT
LogModel17 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OVWT + OBESE, data=brfss, family = "binomial")
summary(LogModel17)
# Remove OVWT
LogModel18 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE, data=brfss, family = "binomial")
summary(LogModel18)
# Add exercise
LogModel19 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER, data=brfss, family = "binomial")
summary(LogModel19)
# Add back DRKMONTHLY
LogModel20 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY, data=brfss, family = "binomial")
summary(LogModel20)
# Try to add back SMOKER
LogModel21 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER, data=brfss, family = "binomial")
summary(LogModel21)
# Add back HISPANIC
LogModel22 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ HISPANIC, data=brfss, family = "binomial")
summary(LogModel22)
# Add back BLACK
LogModel23 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ BLACK, data=brfss, family = "binomial")
summary(LogModel23)
# Add back ASIAN
LogModel24 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ ASIAN, data=brfss, family = "binomial")
summary(LogModel24)
# Add back LOWED
LogModel25 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED, data=brfss, family = "binomial")
summary(LogModel25)
# Add back SOMECOLL
LogModel26 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + SOMECOLL, data=brfss, family = "binomial")
summary(LogModel26)
# Add back INC5
LogModel27 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + INC5, data=brfss, family = "binomial")
summary(LogModel27)
# Add back INC6
LogModel28 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + INC6, data=brfss, family = "binomial")
summary(LogModel28)
# Add back INC7
LogModel29 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + INC7, data=brfss, family = "binomial")
summary(LogModel29)
# Add back FORMERMAR
LogModel30 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + FORMERMAR, data=brfss, family = "binomial")
summary(LogModel30)
# Add back NOPLAN
LogModel31 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + NOPLAN, data=brfss, family = "binomial")
summary(LogModel31)
# Add back UNDWT
LogModel32 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + UNDWT, data=brfss, family = "binomial")
summary(LogModel32)
# Add back OVWT
LogModel33 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT, data=brfss, family = "binomial")
summary(LogModel33)
# Add back AGE2
LogModel34 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2, data=brfss, family = "binomial")
summary(LogModel34)
# Add back AGE3
LogModel35 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE3, data=brfss, family = "binomial")
summary(LogModel35)
# Add back AGE4
LogModel36 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE4, data=brfss, family = "binomial")
summary(LogModel36)
#Add back AGE5
LogModel37 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5, data=brfss, family = "binomial")
summary(LogModel37)
# Add back AGE6
LogModel38 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + AGE6, data=brfss, family = "binomial")
summary(LogModel38)
# Add back HISPANIC
LogModel39 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + HISPANIC, data=brfss, family = "binomial")
summary(LogModel39)
# Add back SOMECOLL
LogModel40 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + SOMECOLL, data=brfss, family = "binomial")
summary(LogModel40)
# Add back UNDWT
LogModel41 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + UNDWT, data=brfss, family = "binomial")
summary(LogModel41)
# Add back health plan
LogModel42 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + NOPLAN, data=brfss, family = "binomial")
summary(LogModel42)
# Add back INC5
LogModel43 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + INC5, data=brfss, family = "binomial")
summary(LogModel43)
# Add back INC6
LogModel44 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + INC6, data=brfss, family = "binomial")
summary(LogModel44)
# Add back INC7
LogModel45 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + INC7, data=brfss, family = "binomial")
summary(LogModel45)
# Add back BLACK
LogModel46 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + BLACK, data=brfss, family = "binomial")
summary(LogModel46)
# Add back ASIAN
LogModel47 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + ASIAN, data=brfss, family = "binomial")
summary(LogModel47)
# Add back FORMERMAR
LogModel48 <- glm(ASTHMA4 ~ DRKWEEKLY + MALE
+ OTHRACE + NEVERMAR + FAIRHLTH + POORHLTH
+ INC1 + INC2 + INC3 + INC4
+ OBESE + NOEXER + DRKMONTHLY + SMOKER
+ LOWED + OVWT + AGE2 + AGE5 + FORMERMAR, data=brfss, family = "binomial")
summary(LogModel48)
#FINAL MODEL
#arrange covariates in order of table 1
FinalLogisticRegressionModel <- glm(ASTHMA4 ~ DRKMONTHLY + DRKWEEKLY
+ AGE2 + AGE5 + MALE
+ OTHRACE + NEVERMAR + LOWED
+ INC1 + INC2 + INC3 + INC4
+ OVWT + OBESE + SMOKER
+ NOEXER + FAIRHLTH + POORHLTH, data=brfss, family = "binomial")
summary(FinalLogisticRegressionModel)
# Write out CSV of final model
Tidy_LogModel_a <- tidy(FinalLogisticRegressionModel)
Tidy_LogModel3 <- subset(Tidy_LogModel_a, term != "(Intercept)")
# Add calculations
Tidy_LogModel3$OR <- exp(Tidy_LogModel3$estimate)
Tidy_LogModel3$LL <- exp(Tidy_LogModel3$estimate - (1.96 * Tidy_LogModel3$std.error))
Tidy_LogModel3$UL <- exp(Tidy_LogModel3$estimate + (1.96 * Tidy_LogModel3$std.error))
# Export the summary as CSV file
write.csv(Tidy_LogModel3, file = "data/LogisticRegressionModel3.csv")
#visualize to help interpretation
library(ggplot2)
ggplot(Tidy_LogModel3,
aes(x = term, y = OR, ymin = LL, ymax = UL)) +
geom_pointrange(aes(col = factor(term)),
position=position_dodge(width=0.30)) +
ylab("Odds ratio & 95% CI") +
geom_hline(aes(yintercept = 1)) +
scale_color_discrete(name = "Term") +
xlab("") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))