如何将回归的输出保存在R的表中

Question

I want to save my output regression of lmer() from lme4 R package. Is there any good way for this to get the output below in a table eg .csv or .txt or .html etc?

     Fixed effects:
               Estimate  Std. Error  df         t value     Pr(>|t|)
(Intercept)   103.989    5.617      139.000    18.52        < 2e‐16 ***
age           ‐0.172     0.177      139.000   ‐1.03          0.304
bmi           0.597      0.229      139.000    2.56          0.012 *
gender        1.019      0.325      139.000    3.15          0.002 **

I tried, tab_model() from library sjplot in R, but it does not give the SE, df and t values. I would like to save the output above. I appreciate any advice.

Answer 1

Make sure the class of your model object is lmerMod and it will work with stargazer , which exports beautiful formatted regression tables to plain text, html, latex, etc. and has all sort of options to customize those tables (see the docs).

# class(mod)<- "lmerMod"

mod <- lme4::lmer(Ozone ~ Temp + (1|Month), 
                  data = airquality)
stargazer::stargazer(mod)
stargazer::stargazer(mod, type = "html")

Answer 2

Update:to write to textfile:

library(lme4)

m1 <- lmer(drat ~ wt + (1 + wt|cyl), data=mtcars)

library(broom.mixed)
library(dplyr)

df<- m1 %>% 
  tidy()

write.table(df,"filename.txt",sep="\t",row.names=FALSE)

OR

m1 %>% 
  tidy() %>% 
  write.table(.,"filename.txt",sep="\t",row.names=FALSE)

"effect"    "group" "term"  "estimate"  "std.error" "statistic"
"fixed" NA  "(Intercept)"   4.67281034450577    0.344833957358875   13.5508996280279
"fixed" NA  "wt"    -0.344238767944164  0.0911701519816392  -3.77578363600283
"ran_pars"  "cyl"   "sd__(Intercept)"   0.374914148920673   NA  NA
"ran_pars"  "cyl"   "cor__(Intercept).wt"   -1  NA  NA
"ran_pars"  "cyl"   "sd__wt"    0.0839046849277359  NA  NA
"ran_pars"  "Residual"  "sd__Observation"   0.370192153038516   NA  NA

One way could be using broom.mixed package as suggested by @ user63230 in the comments section:

Here is an example:

library(lme4)

m1 <- lmer(drat ~ wt + (1 + wt|cyl), data=mtcars)

library(broom.mixed)
library(dplyr)
m1 %>% 
  tidy()

  effect   group    term                estimate std.error statistic
  <chr>    <chr>    <chr>                  <dbl>     <dbl>     <dbl>
1 fixed    NA       (Intercept)           4.67      0.345      13.6 
2 fixed    NA       wt                   -0.344     0.0912     -3.78
3 ran_pars cyl      sd__(Intercept)       0.375    NA          NA   
4 ran_pars cyl      cor__(Intercept).wt  -1        NA          NA   
5 ran_pars cyl      sd__wt                0.0839   NA          NA   
6 ran_pars Residual sd__Observation       0.370    NA          NA