There are lots of packages to do this in R, actually. While you could certainly do it manually (selecting each element you want), it's a common enough task that several have presets to do what I think you want (I'm not a SAS user, so I don't know exactly what their tables look like).
If you're using R Markdown it will depend a bit on what output type you want. The Hmisc package does very nice summaries http://biostat.mc.vanderbilt.edu/wiki/Main/Hmisc.
There's a blog post (see link below) with examples from a package called finalfit
The sjPlot package does nice model summary tables as HTML:
http://www.strengejacke.de/sjPlot/articles/sjtlm.html
# load package
library(sjPlot)
#> Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!
library(sjmisc)
library(sjlabelled)
# sample data
data("efc")
efc <- as_factor(efc, c161sex, c172code)
efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1)
m4 <- glm(
neg_c_7d ~ c161sex + barthtot + c172code,
data = efc,
family = binomial(link = "logit")
)
tab_model(m4, show.se = TRUE, show.std = TRUE, show.stat = TRUE)
|
|
neg c 7 d
|
|
Predictors
|
Odds Ratios
|
std. Error
|
std. Beta
|
CI
|
standardized CI
|
Statistic
|
p
|
|
(Intercept)
|
6.54
|
0.30
|
-0.37
|
3.66 – 11.96
|
-0.80 – 0.06
|
-1.69
|
0.09
|
|
carer’s gender: Female
|
1.87
|
0.18
|
0.63
|
1.31 – 2.69
|
0.27 – 0.99
|
3.40
|
0.00
|
|
Total score BARTHEL INDEX
|
0.97
|
0.00
|
-1.03
|
0.96 – 0.97
|
-1.22 – -0.85
|
-10.72
|
0.00
|
|
carer’s level of education: intermediate level of education
|
1.23
|
0.20
|
0.21
|
0.84 – 1.82
|
-0.18 – 0.60
|
1.06
|
0.29
|
|
carer’s level of education: high level of education
|
1.37
|
0.25
|
0.31
|
0.84 – 2.23
|
-0.17 – 0.80
|
1.27
|
0.20
|
|
Observations
|
815
|
|
R2 Tjur
|
0.191
|
Created on 2019-11-21 by the reprex package (v0.3.0.9001)
See also the post here: