how to save the output of regression in a table in R-CodePudding

I want to save my output regression of lmer() from lme4 R package. Is there any good way for this to get the out put below in a table e.g .csv or 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 value. I would like to save the output above. I appreciate any advice.

CodePudding user response：

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")

CodePudding user response：

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)

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