Logistic Regression Analysis of Borrelia Data Considering Age and Gender
This analysis utilizes logistic regression to explore the relationship between age, gender, and the likelihood of testing positive for IgG antibodies against Borrelia. Two models are compared: one including both age and gender, and the other focusing solely on age. The ANOVA results reflect a non-significant p-value, indicating no major differences between genders. However, the model suggests a significant increase in the probability of a positive IgG result with age. The findings underscore the importance of considering both gender and age in assessing Borrelia infection risks.
Logistic Regression Analysis of Borrelia Data Considering Age and Gender
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Presentation Transcript
Logistic regression on borrelia data including age Start model4<- glm(IgG~gender+age,family=binomial,data=data1) model6<- glm(IgG~age,family=binomial,data=data1) anova(model4,model6, test="Chisq") # P-value 0.306 Possible But will work with model4<- glm(IgG~gender+age,family=binomial,data=data1)
Question 2 library(multcomp) data.comp1<-glht(model4,linfct=mcp(gender="Tukey")) exp(confint(data.comp1,calpha=1.96)$confint) Estimate lwr upr M - F 1.225709 0.8310079 1.807881 Odds ratio
Question 3 and 4 predict(model4,new=data.frame(gender=c("F","M"), age=c(40,60), type="response")) Female age 40: 0.026 Male age 40: 0.032 Male age 60: 0.047 Female age 60: 0.039
model4<- glm(IgG~gender+age,family=binomial,data=data1) • summary(model4) • # SUMMARY IS LOG ODDS. • # Estimate Std. Error z value Pr(>|z|) • #(Intercept) -4.436081 0.336540 -13.181 < 2e-16 *** • #genderM 0.203520 0.198284 1.026 0.304700 • #age 0.020554 0.005618 3.659 0.000254 ***
Question 5 Man have a higher probability of getting a positive IgG than women. The probability is though not significant between gender. The probability of getting a positive IgG result is increasing significantly with age
Question 6 No. There are too many different ages. Which will result in too many age factors with no or few repeats model23<- glm(IgG~gender*factor(age),family=binomial,data=data)
