reg_x {quickReg}

Build regression models only one dependent variable

Description

Build general linear model, generalized linear model, cox regression model with only one dependent variables.

Usage

reg_x(data = NULL, x = NULL, y = NULL, cov = NULL, factors = NULL,
  model = NULL, time = NULL, cov_show = FALSE, detail_show = FALSE,
  confint_glm = "default", save_to_file = NULL)

Arguments

data

A data.frame to build the regression model.

x

Integer column indices or names of the variables to be included in univariate analysis. If NULL, the default columns are all the variables except 'y', 'time' and 'cov'.

y

Integer column indice or name of dependent variable, only one integer or character

cov

Integer column indices or name of covariate variables

factors

Integer column indices or names of variables to be treated as factor

model

regression model, see lm, glm, coxph for more details

time

Integer column indices or name of survival time, used in cox regression, see coxph for more details

cov_show

A logical, whether to create covariates result, default FALSE

detail_show

A logical, whether to create each regression result, default FALSE. If TRUE, with many regressions, the return result could be very large.

confint_glm

A character, 'default' or 'profile'. The default method for 'glm' class to compute confidence intervals assumes asymptotic normality confint, you can also use profile likelihood method confint.glm, but it is pretty slow. In this case you could specify 'default' for speed.

save_to_file

A character, containing file name or path

Value

If detail_show is TRUE, the return result is a list including two components, the first part is a detailed analysis result, the second part is a concentrated result in a data.frame. Otherwise, only return concentrated result in a data.frame.

Examples

reg_glm<-reg_x(data = diabetes, x = c(1:4, 6), y = 5, factors = c(1, 3, 4), model = 'glm')
##  other methods
fit<-reg_x(data = diabetes, x = c(1, 3:6), y = "age", factors = c(1, 3, 4), model = 'lm')
fit<-reg_x(data = diabetes, x = c( "sex","education","BMI"), y = "diabetes",
time ="age", factors = c("sex","smoking","education"), model = 'coxph')