qizhong

#01.

#模拟试验

set.seed(18200116)

test <- function(n,m,sd,a){

  c=0

  for (i in 1:1000)

  {

    s <- rnorm(n,mean = m,sd = sd)

    u <- qnorm(1-a/2)

    up <- mean(s)+sd(s)*u/sqrt(n)

    low <- mean(s)-sd(s)*u/sqrt(n)

    if(low<0 & 0<up)

      c=c+1

  }

1000-c

}

test(10,0,5,0.05)

test(10,0,5,0.1)

test(10,0,5,0.2)

test(20,0,5,0.05)

test(20,0,5,0.1)

test(20,0,5,0.2)

test(50,0,5,0.05)

test(50,0,5,0.1)

test(50,0,5,0.2)

#02.

#导入数据

data <- read.csv("C:/Users/Administrator/Desktop/2018级生物统计班R语言平时测试试题/test_data.csv")

#（1）年龄age变量分布的柱形图和核密度曲线图绘制

p1 <- barplot(table(data$age))

par(new=TRUE)

p2 <- density(data$age)

p1 <- plot(p2)

#（2）编制自定义函数并输出体重指数bmi和吸烟强度packyr的结果

function1 <- function(x){

  a <- quantile(x,0.5,na.rm=TRUE)

  b <- quantile(x,0.25,na.rm=TRUE)

  c <- quantile(x,0.75,na.rm=TRUE)

  result <- paste0(a,"(",b,"-",c,")")

  return(result)

}

function1(data$bmi)

function1(data$packyr)

#（3）年龄，教育程度，体重指数，吸烟，上述哪几个因素在不同性别间差异显著

t.test(age~sex,data)

#p<0.05,年龄在不同性别间有差异

wilcox.test(education~sex,data)

#p>0.05,教育程度在不同性别间无差异

t.test(bmi~sex,data)

#p>0.05,体重指数在不同性别间无差异

mytable1 <- xtabs(~sex+smoke,data = data)

chisq.test(mytable1)

#p<0.05,吸烟在不同性别间有差异

#（4）使用适当的广义线性模型评估肺癌风险与基线因素的关联强度

summary(data)

full <- glm(lung_ca~age+sex+education+bmi+family_ca+smoke+packyr+respdis+secsmoke+exposure+drink+exercise,data = data,family = binomial())

summary(full)