A manual to show the R package quickReg
.
The quickReg
package provide a set of functions to display and pry a dataset. More precisely, the package can display statistical descriptions for a dataset, build regression models for lm, glm, and cox regressions based on specified independent variables. The package also provides functions to visualize the regression results. Some examples are shown below:
The example data is a toy dataset extracting from package PredictABEL only to be used to demonstrate the main idea of the package.
# download it from cran or github
install.packages("quickReg")
devtools::install_github("XikunHan/quickReg", build_vignettes = TRUE)
library(quickReg)
library(ggplot2)
library(rlang)
library(dplyr)
# Load the dataset
data(diabetes)
# Show the first 6 rows of the data
head(diabetes)
Functions display_table or display_table_group can be used to show statistical descriptions of the dataset.
display_1<-display_table(data=diabetes,variables=c("age","smoking","education"),group="CFHrs2230199")
display_1
# sub-group analysis by sex
display_2<-display_table_group(data=diabetes,variables=c("age","smoking"),group="CFHrs2230199",super_group = "sex")
display_2
# sub-group analysis by two variables
display_3<-display_table_group(data=diabetes,variables=c("age","smoking"),group="CFHrs2230199",super_group = c("sex","education"))
display_3
# Sub-group analysis can be a combination
display_4<-display_table_group(data=diabetes,variables=c("age","smoking"),group="CFHrs2230199",super_group = c("sex","education"),group_combine = TRUE)
display_4
# Apply univariate regression models
reg_1<-reg_x(data = diabetes, y = 5, factors = c(1, 3, 4), model = 'glm')
reg_1
# Or survival analysis
reg_2<-reg_x(data = diabetes, x = c(3:4, 6), y ="diabetes",time=2,factors = c(1, 3, 4), model = 'coxph')
reg_2
# adjusting for covariates
reg_3<-reg_x(data = diabetes, x = c("sex","age"), y ="diabetes" ,cov=c("CFBrs641153","CFHrs2230199"), factors ="sex", model = 'glm',cov_show = TRUE)
reg_3
# regression on several dependent variables
reg_4<-reg_y(data = diabetes, x = c("sex","age","CFHrs1061170"), y =c("systolic","diastolic","BMI") ,cov=c("CFBrs641153","CFHrs2230199"), factors ="sex", model = 'lm')
reg_4
# do a subgroup analysis
reg_5<-reg(data = diabetes, x = c("age","CFHrs1061170"), y =c("systolic","diastolic") ,cov=c("CFBrs641153","CFHrs2230199"), model = 'lm',group="sex")
reg_5
# or two subgroup analysis
reg_6<-reg(data = diabetes, x = c("age","CFHrs1061170"), y =c("systolic","diastolic") ,cov=c("CFBrs641153","CFHrs2230199"), model = 'lm',group=c("sex","smoking"))
reg_6
# or subgroup combination analysis
reg_7<-reg(data = diabetes, x = c("age","CFHrs1061170"), y =c("systolic","diastolic") ,cov=c("CFBrs641153","CFHrs2230199"), model = 'lm',group=c("sex","smoking"),group_combine = TRUE)
reg_7
plot(reg_1)
# if the OR value is very large, we can set a limit
plot(reg_1,limits=c(NA,3))
# Sort the variables alphabetically
plot(reg_1,limits=c(NA,3), sort ="alphabetical")
# Similarly, we can plot several dependent variables result
plot(reg_4)
# Subgroup and several dependent variables
plot(reg_5)+facet_grid(sex~y)
# modify the plot
library(ggplot2);library(ggthemes)
plot(reg_1,limits=c(0.5,2))+
labs(list(title = "Regression Model", x = "variables"))+
theme_classic() %+replace%
theme(legend.position ="none",axis.text.x=element_text(angle=45,size=rel(1.5)))
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