# R/smruni.R In YuweiNi45/lng:

#### Documented in smruni

```#' Univariate analysis and summary table
#'
#' Generate summary table proving the univariate analysis for each independent variabls and dependent variable from the whole dataset
#'
#'
#' @param y   dependent variable which selected by user
#' @param data  a data frame, list the variables to be summarized (contain the variable Y)
#' @param alternative	  a character string specifying the alternative approaches, must be one of "linear" (default), "logistic"
#' @param digits     the digits of the data values (default is 3)
#' @param ...	  further arguments to be passed to or from methods
#'
#' @return  For univariate analysis, it returns a summary table based on the type of dependent variables. For continuous Y, the table contains parameters, coefficient, p-value, CI for coefficient; while for categorical variable, the table contains the parameters, Odds ratio, p-value, CI for odds ratio.
#'
#' @references Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
#'
#'
#'@import tidyverse
#'
#'@export

smruni<-function(y,
data,
alternative = c("linear","logistic"),
digit = 3,
...){
alternative <- match.arg(alternative)

if(!missing(alternative)){
if(alternative=="logistic"){
#logistic

#get the name and the number of the Y
y_length <- length(y)
data_names <- colnames(data)
name_length <- length(data_names)
result <- numeric(name_length)
for (i in 1:name_length) {
result[i] <- sum(y %in% factor(unlist(data[data_names[i]])))
}
ncol_y <- which(result == y_length)
y_group <- data_names[ncol_y]

#re-arrange the whole dataset
data1<-data[,-ncol_y]
data2<-cbind(data[,ncol_y],data1)
data2<-as.data.frame(data2)

#do the logistic regression and get the coefficient/p-value/standard error

coe <- numeric(ncol(data2)-1)
pva<-numeric(ncol(data2)-1)
se <-numeric(ncol(data2)-1)
for(i in 2:ncol(data2)){
se[i]<-summary(glm(data2[,1]~data2[,i],family=binomial))\$coefficients[2,2]
coe[i]<-summary(glm(data2[,1]~data2[,i],family=binomial))\$coefficients[2,1]
pva[i]<-summary(glm(data2[,1]~data2[,i],family=binomial))\$coefficients[2,4]
}

se <-se[-1]
coe <-coe[-1]
pva <-pva[-1]

#calculate the coffience interval and the or
ci1 <- numeric(length(se))
ci2 <-numeric(length(se))

for(i in 1:length(se)){
ci1[i]<- coe[i]-1.96*se[i]
ci2[i]<- coe[i]+1.96*se[i]
}

or<-exp(cbind(OR=coe, ci1,ci2))

#change the form of the data
test<-function(q){
if(q<0.001) q.txt<-"<0.001"
else if(q<0.05) q.txt<-"<0.05"
else q.txt <-format(q,digits = digit)
}

#change the form of p-value
pvalue <- numeric(nrow(or))
for(i in 1:nrow(or)){
pvalue[i]<-test(pva[i])
}

data_names <- colnames(data2)
names<-data_names[-1]
out <- data.frame(Parameters=names,
Coefficient = format(coe,digits = digit),
p.value = pvalue,
OR = format(or[,1],digits = digit),
CI= paste0("(",
format(or[,2],digits = digit),
",",
format(or[,3],digits = digit),
")"))
names(out)[5] <- "CI(95%)"
out

}else{
#linear

#get the name and the number of the Y
y_length <- length(y)
data_names <- colnames(data)
name_length <- length(data_names)
result <- numeric(name_length)
for (i in 1:name_length) {
result[i] <- sum(y %in% factor(unlist(data[data_names[i]])))
}
ncol_y <- which(result == y_length)
y_group <- data_names[ncol_y]

#re-arrange the whole dataset
data1<-data[,-ncol_y]
data2<-cbind(data[,ncol_y],data1)
data2<-as.data.frame(data2)

#do the logistic regression and get the coefficient/p-value/standard error
coe1 <- numeric(ncol(data2)-1)
pva1<-numeric(ncol(data2)-1)
se1 <-numeric(ncol(data2)-1)

for(i in 2:ncol(data2)){
se1[i]<-summary(lm(data2[,1]~data2[,i]))\$coefficients[2,2]
coe1[i]<-summary(lm(data2[,1]~data2[,i]))\$coefficients[2,1]
pva1[i]<-summary(lm(data2[,1]~data2[,i]))\$coefficients[2,4]
}
se1 <-se1[-1]
coe1 <-coe1[-1]
pva1 <-pva1[-1]

#calculate the coffience interval and the or
ci21 <- numeric(length(se1))
ci22 <-numeric(length(se1))

for(i in 1:length(se1)){
ci21[i]<- coe1[i]-1.96*se1[i]
ci22[i]<- coe1[i]+1.96*se1[i]
}

#change the form of the data
test<-function(q){
if(q<0.001) q.txt<-"<0.001"
else if(q<0.05) q.txt<-"<0.05"
else q.txt <-format(q,digits = digit)
}

#change the form of p-value
pvalue1 <- numeric(length(pva1))
for(i in 1:length(pva1)){
pvalue1[i]<-test(pva1[i])
}

data_names <- colnames(data2)
names<-data_names[-1]
out1 <- data.frame(Parameters=names,
Coefficient = format(coe1,digits = digit),
p.value = pvalue1,
CI= paste0("(",
format(ci21,digits = digit),
",",
format(ci22,digits = digit),
")"))
names(out1)[4] <- "CI(95%)"
out1
}
}else{

#linear

#get the name and the number of the Y
y_length <- length(y)
data_names <- colnames(data)
name_length <- length(data_names)
result <- numeric(name_length)
for (i in 1:name_length) {
result[i] <- sum(y %in% factor(unlist(data[data_names[i]])))
}
ncol_y <- which(result == y_length)
y_group <- data_names[ncol_y]

#re-arrange the whole dataset
data1<-data[,-ncol_y]
data2<-cbind(data[,ncol_y],data1)
data2<-as.data.frame(data2)

#do the logistic regression and get the coefficient/p-value/standard error
coe1 <- numeric(ncol(data2)-1)
pva1<-numeric(ncol(data2)-1)
se1 <-numeric(ncol(data2)-1)

for(i in 2:ncol(data2)){
se1[i]<-summary(lm(data2[,1]~data2[,i]))\$coefficients[2,2]
coe1[i]<-summary(lm(data2[,1]~data2[,i]))\$coefficients[2,1]
pva1[i]<-summary(lm(data2[,1]~data2[,i]))\$coefficients[2,4]
}
se1 <-se1[-1]
coe1 <-coe1[-1]
pva1 <-pva1[-1]

#calculate the coffience interval and the or
ci21 <- numeric(length(se1))
ci22 <-numeric(length(se1))

for(i in 1:length(se1)){
ci21[i]<- coe1[i]-1.96*se1[i]
ci22[i]<- coe1[i]+1.96*se1[i]
}

#change the form of the data
test<-function(q){
if(q<0.001) q.txt<-"<0.001"
else if(q<0.05) q.txt<-"<0.05"
else q.txt <-format(q,digits = digit)
}

#change the form of p-value
pvalue1 <- numeric(length(pva1))
for(i in 1:length(pva1)){
pvalue1[i]<-test(pva1[i])
}

data_names <- colnames(data2)
names<-data_names[-1]
out1 <- data.frame(Parameters=names,
Coefficient = format(coe1,digits = digit),
p.value = pvalue1,
CI= paste0("(",
format(ci21,digits = digit),
",",
format(ci22,digits = digit),
")"))
names(out1)[4] <- "CI(95%)"
out1

}

}
```
YuweiNi45/lng documentation built on May 30, 2018, 7:23 p.m.