R/uv_linear.R

In fastStat: Faster for Statistic Work

#' Looping for Univariable Logistic Regression
#'
#' @param data data
#' @param y y
#' @param variable variable names for univariable logistic regression. If missing, it will be column names of data except y and adjust
#' @param adjust adjust variable names for univariable logistic regression
#' @param round digital round, 3 is defaulted
#' @param p_threshold threshold for p value to show star. 0.05 is defaulted
#' @param order_by.beta logical. TRUE means order in or by decreasing. FLASE is defaulted
#' @importFrom stats lm
#' @return univariable logistic regression results
#' @export
#'
#' @examples
#' uv_linear(data = mtcars,y = 'vs')
uv_linear <- function(data,y,variable,adjust,round=3,
                     p_threshold=0.05,order_by.beta=TRUE){
    #delet na
    data=delet_na_df.mtr(data)
    if (missing(adjust)){
        if (missing(variable)) variable=colnames(data) %not% y
        for (i in 1:length(variable)) {
            if (i==1){
                res=NULL
                class=NULL
            }
            if (is.factor(data[,variable[i]])){
                rep_len=length(levels(data[,variable[i]]))-1
                if (rep_len>1){
                    rep_class=rep(i+1,rep_len)
                    class=c(class,rep_class)
                }else{
                    class=c(class,0)
                }
            }else{
                class=c(class,0)
            }
            formu=paste0(y,'~',variable[i])
            line.i=lm(as.formula(formu),data = data)
            line.sum=summary(line.i)
            line_coef=as.data.frame(line.sum$coefficients)
            line_var=line_coef[-1,]
            confint=as.data.frame(suppressWarnings(suppressMessages(confint(line.i))))[-1,]
            res.cbind=cbind(beta=line_var[,'Estimate'],
                            confint,
                            line_var[,c('Std. Error','t value','Pr(>|t|)')])
            res.i=round(res.cbind,round)
            res=rbind(res,res.i)
        }
        res2=cbind(res,class)
        if (order_by.beta){
            res3=res2[order(res2$class, res2$beta,decreasing = TRUE),-ncol(res2)]
        }else{
            res3=res2[,-ncol(res2)]
        }
        star=ifelse(res3$beta>=3,'   |***',
                    ifelse(res3$beta>=1 & res3$beta <3 , '   |** ',
                           ifelse(res3$beta>0 & res3$beta <1, '   |*  ',
                                  ifelse(res3$beta>=-1 & res3$beta<0,"  *|   ",
                                         ifelse(res3$beta>= -3 & res3$beta < -1,' **|   ',
                                                ifelse(res3$beta==0,'   |   ','***|   '))))))
        res3$star=ifelse(res3$`Pr(>|t|)`<=p_threshold,star,"")
        return(res3)
    }else{
        if (missing(variable)) variable=colnames(data) %not% c(y,adjust)
        for (i in 1:length(variable)) {
            if (i==1){
                res=NULL
                class=NULL
            }
            if (is.factor(data[,variable[i]])){
                rep_len=length(levels(data[,variable[i]]))-1
                if (rep_len>1){
                    rep_class=rep(i+1,rep_len)
                    class=c(class,rep_class)
                }else{
                    class=c(class,0)
                }
            }else{
                class=c(class,0)
            }
            #logistic for adjust
            if (i==1){
                formu=paste0(y,'~',paste0(c(adjust),collapse = '+'))
                line.i=lm(as.formula(formu),data = data)
                line.sum=summary(line.i)
                line_coef=as.data.frame(line.sum$coefficients)
                nub_row=nrow(line_coef)
            }
            #logistci for variable
            formu=paste0(y,'~',paste0(c(adjust,variable[i]),collapse = '+'))
            line.i=lm(as.formula(formu),data = data)
            line.sum=summary(line.i)
            line_coef=as.data.frame(line.sum$coefficients)
            line_var=line_coef[-c(1:nub_row),]
            confint=as.data.frame(suppressWarnings(suppressMessages(confint(line.i))))[-c(1:nub_row),]
            res.cbind=cbind(beta=exp(line_var[,'Estimate']),
                            confint,
                            line_var[,c('Std. Error','t value','Pr(>|t|)')])
            res.i=round(res.cbind,round)
            res=rbind(res,res.i)
            res.i
        }
        res2=cbind(res,class)
        if (order_by.beta){
            res3=res2[order(res2$class, res2$beta,decreasing = TRUE),-ncol(res2)]
        }else{
            res3=res2[,-ncol(res2)]
        }
        star=ifelse(res3$beta>=6,'   |***',
                    ifelse(res3$beta>=3 & res3$beta <6 , '   |** ',
                           ifelse(res3$beta>=1 & res3$beta <3, '   |*  ',
                                  ifelse(res3$beta>=1/3 & res3$beta<1,"  *|   ",
                                         ifelse(res3$beta>= 1/6 & res3$beta < 1/3,' **|   ','***|   ')))))
        res3$star=ifelse(res3$`Pr(>|t|)`<=p_threshold,star,"")
        return(res3)
    }
}