Perform univariable and multivariable regression and stepwise backward regression automatically
Source:R/autoReg.R, R/autoReg.coxph.R, R/autoReg.survreg.R
autoReg.RdPerform univariable and multivariable regression and stepwise backward regression automatically
Usage
autoReg(x, ...)
# S3 method for lm
autoReg(x, ...)
# S3 method for glm
autoReg(x, ...)
# S3 method for coxph
autoReg(x, ...)
# S3 method for survreg
autoReg(x, ...)Value
autoReg returns an object of class "autoReg" which inherits from the class "data.frame" with at least the following attributes:
- attr(*,"yvars)
character. name of dependent variable
- attr(*,"model")
name of model. One of "lm","glm" or "coxph"
Methods (by class)
autoReg(lm): S3 method for a class lmautoReg(glm): S3 method for a class glmautoReg(coxph): S3 method for a class coxphautoReg(survreg): S3 method for a class survreg
Examples
data(cancer,package="survival")
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
autoReg(fit)
#> ——————————————————————————————————————————————————————————————————————————————————
#> Dependent: status 0 (N=938) 1 (N=920) OR (multivariable)
#> ——————————————————————————————————————————————————————————————————————————————————
#> rx Obs 285 (30.4%) 345 (37.5%)
#> Lev 287 (30.6%) 333 (36.2%) 0.94 (0.74-1.18, p=.576)
#> Lev+5FU 366 (39%) 242 (26.3%) 0.53 (0.42-0.68, p<.001)
#> sex Mean ± SD 0.5 ± 0.5 0.5 ± 0.5 0.95 (0.78-1.15, p=.589)
#> age Mean ± SD 60.0 ± 11.5 59.5 ± 12.4 1.00 (0.99-1.01, p=.583)
#> obstruct Mean ± SD 0.2 ± 0.4 0.2 ± 0.4 1.34 (1.05-1.72, p=.018)
#> nodes Mean ± SD 2.7 ± 2.4 4.6 ± 4.2 1.21 (1.17-1.25, p<.001)
#> ——————————————————————————————————————————————————————————————————————————————————
autoReg(fit,uni=FALSE,final=TRUE)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Dependent: status 0 (N=938) 1 (N=920) OR (multivariable) OR (final)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> rx Obs 285 (30.4%) 345 (37.5%)
#> Lev 287 (30.6%) 333 (36.2%) 0.94 (0.74-1.18, p=.576) 0.94 (0.74-1.18, p=.575)
#> Lev+5FU 366 (39%) 242 (26.3%) 0.53 (0.42-0.68, p<.001) 0.54 (0.42-0.68, p<.001)
#> sex Mean ± SD 0.5 ± 0.5 0.5 ± 0.5 0.95 (0.78-1.15, p=.589)
#> age Mean ± SD 60.0 ± 11.5 59.5 ± 12.4 1.00 (0.99-1.01, p=.583)
#> obstruct Mean ± SD 0.2 ± 0.4 0.2 ± 0.4 1.34 (1.05-1.72, p=.018) 1.34 (1.05-1.71, p=.019)
#> nodes Mean ± SD 2.7 ± 2.4 4.6 ± 4.2 1.21 (1.17-1.25, p<.001) 1.21 (1.17-1.25, p<.001)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
autoReg(fit,uni=FALSE,imputed=TRUE)
#> Warning: Number of logged events: 1
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Dependent: status 0 (N=938) 1 (N=920) OR (multivariable) OR (imputed)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> rx Obs 285 (30.4%) 345 (37.5%)
#> Lev 287 (30.6%) 333 (36.2%) 0.94 (0.74-1.18, p=.576) 0.95 (0.76-1.20, p=.692)
#> Lev+5FU 366 (39%) 242 (26.3%) 0.53 (0.42-0.68, p<.001) 0.54 (0.43-0.68, p<.001)
#> sex Mean ± SD 0.5 ± 0.5 0.5 ± 0.5 0.95 (0.78-1.15, p=.589) 0.97 (0.80-1.17, p=.736)
#> age Mean ± SD 60.0 ± 11.5 59.5 ± 12.4 1.00 (0.99-1.01, p=.583) 1.00 (0.99-1.01, p=.642)
#> obstruct Mean ± SD 0.2 ± 0.4 0.2 ± 0.4 1.34 (1.05-1.72, p=.018) 1.36 (1.06-1.73, p=.014)
#> nodes Mean ± SD 2.7 ± 2.4 4.6 ± 4.2 1.21 (1.17-1.25, p<.001) 1.21 (1.17-1.25, p<.001)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
fit=lm(mpg~wt*hp+am+I(wt^2),data=mtcars)
autoReg(fit,final=TRUE)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Dependent: mpg unit value Coefficient (multivariable) Coefficient (final)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> wt [1.5,5.4] Mean ± SD 3.2 ± 1.0 -7.67 (-14.54 to -0.81, p=.030) -8.22 (-10.82 to -5.62, p<.001)
#> hp [52,335] Mean ± SD 146.7 ± 68.6 -0.13 (-0.25 to -0.01, p=.040) -0.12 (-0.17 to -0.07, p<.001)
#> am [0,1] Mean ± SD 0.4 ± 0.5 0.17 (-2.68 to 3.01, p=.905)
#> I(wt^2) interpretation -0.11 (-1.57 to 1.36, p=.880)
#> wt:hp 0.03 (-0.01 to 0.07, p=.100) 0.03 (0.01 to 0.04, p<.001)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
autoReg(fit,imputed=TRUE)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Dependent: mpg unit value Coefficient (multivariable) Coefficients (imputed)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> wt [1.5,5.4] Mean ± SD 3.2 ± 1.0 -7.67 (-14.54 to -0.81, p=.030) -7.67 (-14.57 to -0.78, p=.031)
#> hp [52,335] Mean ± SD 146.7 ± 68.6 -0.13 (-0.25 to -0.01, p=.040) -0.13 (-0.25 to -0.01, p=.041)
#> am [0,1] Mean ± SD 0.4 ± 0.5 0.17 (-2.68 to 3.01, p=.905) 0.17 (-2.69 to 3.02, p=.905)
#> I(wt^2) interpretation -0.11 (-1.57 to 1.36, p=.880) -0.11 (-1.58 to 1.36, p=.880)
#> wt:hp 0.03 (-0.01 to 0.07, p=.100) 0.03 (-0.01 to 0.07, p=.101)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————