均值计算

• 自编函数进行均值和五数计算

function(x,na.omit=FALSE){
    if(na.omit)
        x <- x[!is.na(x)]
    m <- mean(x)
    sd <- sd(x)
    min <- min(x)
    max <- max(x)
    Q <- quantile(x)
    return(c(mean=m,std=sd,min=min,max=max,quantile=Q))
}
attach(df)
stata <- c("X","Y","D")	#写入需要进行计算的变量
outcome <- sapply(df[stata],describestat)
detach(df)

• 自编函数进行均值和标准差计算

function(x,parametric=TRUE,print=FALSE){
    if(parametric){
        center <- mean(x);spread <- sd(x)
    }else{
        center <- median(x);spread <- mad(x)
    }
    if(print & parametric){
        cat("Median=",center,"\n","MAD=",spread,"\n")
    }
    result <- list(center=center,spread=spread)
    return(result)
}

• 使用R自带包进行均值计算

单因素logistic回归

logitUniVar <- function(dat, group, var, digit = 3){
    formu <- as.formula(paste0(group, " ~ ", var))
    dat[[group]] <- as.factor(dat[[group]])
    subgroup <- levels(as.factor(dat[[group]]))
    subgroup1 <- paste0(subgroup[2], " vs ", subgroup[1])
    fit <- glm(formu, data = dat, family = binomial())
    unisum <- summary(fit)
    OR <- exp(coef(fit))[2]
    OR <- round(OR, digit)
    ci <- exp(confint(fit))[2,]
    ci <- round(ci, digit)
    cito <- paste0(ci[1], " - ", ci[2])
    p <- unisum$coefficients[2, "Pr(>|z|)"]
    p <- ifelse(p < 0.001, "< 0.001", round(p, 3))
    var1 <- names(exp(coef(fit)))[2]
    result <- c(var1, group,subgroup1, OR, cito, p)
    names(result) <- c("var", "group","subgroup", "OR", "95%CI", "p.val")
    return(result)
}

#y1 进行分析
multivar <- c("X", "Y", "D")
logitRes <- lapply(multivar, function(x) logitUniVar(df, group = "y1", var = x))
logitResDat <- do.call(rbind, logitRes)
logitResDat