Summarize statistics with a model
Arguments
- fit
An object of class lm or glm or coxph or survreg
- method
character choices are one of the c("likelihood","wald")
- digits
integer indicating the number of decimal places
- mode
integer
Examples
library(survival)
data(cancer)
fit=glm(status~rx+sex+age+obstruct+nodes,data=colon,family="binomial")
fit2stats(fit)
#> OR lower upper p id stats
#> (Intercept) 0.53 0.30 0.92 0.0246 (Intercept) 0.53 (0.30-0.92, p=.025)
#> rxLev 0.94 0.74 1.18 0.5757 rxLev 0.94 (0.74-1.18, p=.576)
#> rxLev+5FU 0.53 0.42 0.68 0.0000 rxLev+5FU 0.53 (0.42-0.68, p<.001)
#> sex 0.95 0.78 1.15 0.5885 sex 0.95 (0.78-1.15, p=.589)
#> age 1.00 0.99 1.01 0.5826 age 1.00 (0.99-1.01, p=.583)
#> obstruct 1.34 1.05 1.72 0.0178 obstruct 1.34 (1.05-1.72, p=.018)
#> nodes 1.21 1.17 1.25 0.0000 nodes 1.21 (1.17-1.25, p<.001)
fit=lm(mpg~wt*hp+am,data=mtcars)
fit2stats(fit)
#> id Estimate lower upper
#> (Intercept) (Intercept) 49.45224079 38.61707633 60.28740526
#> wt wt -8.10055755 -11.77194963 -4.42916547
#> hp hp -0.11930318 -0.17377926 -0.06482709
#> am am 0.12510693 -2.61086742 2.86108128
#> wt:hp wt:hp 0.02748826 0.01010407 0.04487245
#> stats
#> (Intercept) 49.45 (38.62 to 60.29, p<.001)
#> wt -8.10 (-11.77 to -4.43, p<.001)
#> hp -0.12 (-0.17 to -0.06, p<.001)
#> am 0.13 (-2.61 to 2.86, p=.926)
#> wt:hp 0.03 (0.01 to 0.04, p=.003)
fit=survreg(Surv(time,status)~rx+sex+age+obstruct+nodes,data=colon)
fit2stats(fit)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Value Std. Error z p ETR LB UB HR lower upper p1 stats
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> (Intercept) 8.487 0.225 37.769 <.001 4851.758 3123.393 7536.535 0.001 0.001 0.001 p<.001 4851.76 (3123.39-7536.53, p<.001)
#> rxLev 0.088 0.092 0.950 .342 1.092 0.911 1.309 0.929 0.797 1.082 p=.342 1.09 (0.91-1.31, p=.342)
#> rxLev+5FU 0.577 0.102 5.656 <.001 1.780 1.458 2.174 0.615 0.520 0.728 p<.001 1.78 (1.46-2.17, p<.001)
#> sex 0.081 0.080 1.011 .312 1.084 0.927 1.268 0.934 0.819 1.066 p=.312 1.08 (0.93-1.27, p=.312)
#> age -0.002 0.003 -0.614 .539 0.998 0.991 1.005 1.002 0.996 1.007 p=.539 1.00 (0.99-1.00, p=.539)
#> obstruct -0.281 0.098 -2.868 .004 0.755 0.624 0.915 1.266 1.078 1.488 p=.004 0.76 (0.62-0.92, p=.004)
#> nodes -0.111 0.008 -14.398 <.001 0.895 0.881 0.908 1.098 1.084 1.112 p<.001 0.90 (0.88-0.91, p<.001)
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Weibull distribution
#> Loglik(model)= -7989.4 Loglik(intercept only)= -8088
#> Chisq= 197.36 on 6 degrees of freedom, p= <2e-16
#> n=1822 (36 observations deleted due to missingness)
#>