Heller Phar7381 Wk12 GLM2 11-13-24 title: “PHAR7381 week12” output: html_document — #lirbaries
library(dplyr)
library(ggplot2)#theme
my_theme<-function(x){theme_bw()+
theme(text = element_text(size=20))+
theme(axis.line.y = element_line(size = 2.0))+
theme(axis.line.x = element_line(size = 2.0))+
theme(axis.ticks = element_line(size = 1.5,colour="black"))+
theme(axis.ticks.length= unit(0.45, "cm"))+
theme(axis.title.y =element_text(vjust=1.2))+
theme(axis.title.x =element_text(vjust=-0.2))+
theme(axis.text=element_text(colour="black"))+
theme(panel.background = element_rect(fill ="white"))}#import data
mydt<-read.csv("C:\\Heller\\PHAR7381\\week12\\glm2.csv") #stringsASFactors = F)
#mydt<-read.csv("/work/ac0837/PHAR7381/week12/glm1.csv",stringsAsFactors = F)#Logistic regression models- Model 1
model1<-glm(nausea~1, data = mydt, family = binomial(link = "logit"))
summary(model1)##
## Call:
## glm(formula = nausea ~ 1, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5878 0.3220 -1.825 0.068 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 54.748 on 41 degrees of freedom
## AIC: 56.748
##
## Number of Fisher Scoring iterations: 4#Logistic regression models- Model 2
model2<-glm(nausea~age, data = mydt, family = binomial(link = "logit"))
summary(model2)##
## Call:
## glm(formula = nausea ~ age, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.82339 2.08626 -1.833 0.0669 .
## age 0.05671 0.03561 1.593 0.1113
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 51.936 on 40 degrees of freedom
## AIC: 55.936
##
## Number of Fisher Scoring iterations: 4#Logistic regression models- Model 3
#model1<-glm(nausea~1, data = mydt, family = binomial(link = "logit"))
model3<-glm(nausea ~ auc, data = mydt, family = binomial(link = "logit"))
summary(model3)##
## Call:
## glm(formula = nausea ~ auc, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4372688 0.6225920 -2.309 0.0210 *
## auc 0.0002172 0.0001283 1.693 0.0904 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 51.718 on 40 degrees of freedom
## AIC: 55.718
##
## Number of Fisher Scoring iterations: 4#Logistic regression models- Model 4
model4<-glm(nausea~sex, data = mydt, family = binomial(link = "logit"))
summary(model4)##
## Call:
## glm(formula = nausea ~ sex, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2809 0.5055 -2.534 0.0113 *
## sex 1.3863 0.6831 2.029 0.0424 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 50.372 on 40 degrees of freedom
## AIC: 54.372
##
## Number of Fisher Scoring iterations: 4model5<-glm(nausea~sex + auc, data = mydt, family = binomial(link = "logit"))
summary(model5)##
## Call:
## glm(formula = nausea ~ sex + auc, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6663116 0.9499150 -2.807 0.0050 **
## sex 1.7753142 0.7857733 2.259 0.0239 *
## auc 0.0003034 0.0001534 1.978 0.0479 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 45.782 on 39 degrees of freedom
## AIC: 51.782
##
## Number of Fisher Scoring iterations: 4model6<-glm(nausea~sex + age, data = mydt, family = binomial(link = "logit"))
summary(model6)##
## Call:
## glm(formula = nausea ~ sex + age, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.06831 2.16531 -1.879 0.0603 .
## sex 1.28987 0.70000 1.843 0.0654 .
## age 0.04984 0.03689 1.351 0.1767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 48.388 on 39 degrees of freedom
## AIC: 54.388
##
## Number of Fisher Scoring iterations: 4model7<-glm(nausea~sex + auc + age, data = mydt, family = binomial(link = "logit"))
summary(model7)##
## Call:
## glm(formula = nausea ~ sex + auc + age, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.1421805 2.3803321 -2.160 0.0308 *
## sex 1.6954373 0.8008377 2.117 0.0343 *
## auc 0.0002942 0.0001562 1.884 0.0596 .
## age 0.0448605 0.0381555 1.176 0.2397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 44.299 on 38 degrees of freedom
## AIC: 52.299
##
## Number of Fisher Scoring iterations: 4#test for completeness:
model8<-glm(nausea~ auc + age, data = mydt, family = binomial(link = "logit"))
summary(model8)##
## Call:
## glm(formula = nausea ~ auc + age, family = binomial(link = "logit"),
## data = mydt)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.3709737 2.1587190 -2.025 0.0429 *
## auc 0.0002070 0.0001333 1.553 0.1205
## age 0.0522730 0.0360045 1.452 0.1465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 54.748 on 41 degrees of freedom
## Residual deviance: 49.403 on 39 degrees of freedom
## AIC: 55.403
##
## Number of Fisher Scoring iterations: 4