How can I convert these lmer coefficients into a data frame?-CodePudding

Data

This is my dput:

structure(list(Mins_Work = c(435L, 350L, 145L, 135L, 15L, 60L, 
60L, 390L, 395L, 395L, 315L, 80L, 580L, 175L, 545L, 230L, 435L, 
370L, 255L, 515L, 330L, 65L, 115L, 550L, 420L, 45L, 266L, 196L, 
198L, 220L, 17L, 382L, 0L, 180L, 343L, 207L, 263L, 332L, 0L, 
0L, 259L, 417L, 282L, 685L, 517L, 111L, 64L, 466L, 499L, 460L, 
269L, 300L, 427L, 301L, 436L, 342L, 229L, 379L, 102L, 146L, NA, 
94L, 345L, 73L, 204L, 512L, 113L, 135L, 458L, 493L, 552L, 108L, 
335L, 395L, 508L, 546L, 396L, 159L, 325L, 747L, 650L, 377L, 461L, 
669L, 186L, 220L, 410L, 708L, 409L, 515L, 413L, 166L, 451L, 660L, 
177L, 192L, 191L, 461L, 637L, 297L), Coffee_Cups = c(3L, 0L, 
2L, 6L, 4L, 5L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 3L, 2L, 2L, 0L, 1L, 
1L, 4L, 4L, 3L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 2L, 3L, 
2L, 2L, 4L, 3L, 6L, 6L, 3L, 4L, 6L, 8L, 3L, 5L, 0L, 2L, 2L, 8L, 
6L, 4L, 6L, 4L, 4L, 2L, 6L, 6L, 5L, 1L, 3L, 1L, 5L, 4L, 6L, 5L, 
0L, 6L, 6L, 4L, 4L, 2L, 2L, 6L, 6L, 7L, 3L, 3L, 0L, 5L, 7L, 6L, 
3L, 5L, 3L, 3L, 1L, 9L, 9L, 3L, 3L, 6L, 6L, 6L, 3L, 0L, 7L, 6L, 
6L, 3L), Work_Environment = c("Office", "Office", "Office", "Home", 
"Home", "Office", "Office", "Office", "Office", "Office", "Home", 
"Home", "Office", "Office", "Office", "Home", "Office", "Home", 
"Home", "Office", "Office", "Home", "Office", "Home", "Home", 
"Home", "Office", "Office", "Office", "Office", "Home", "Home", 
"Home", "Office", "Office", "Office", "Office", "Office", "Home", 
"Home", "Office", "Office", "Home", "Home", "Office", "Home", 
"Home", "Office", "Office", "Home", "Home", "Office", "Home", 
"Home", "Office", "Office", "Home", "Office", "Home", "Home", 
"Home", "Home", "Office", "Home", "Office", "Office", "Home", 
"Home", "Office", "Office", "Home", "Home", "Office", "Office", 
"Home", "Office", "Office", "Home", "Office", "Office", "Home", 
"Home", "Office", "Office", "Home", "Home", "Office", "Home", 
"Home", "Office", "Office", "Home", "Office", "Office", "Home", 
"Home", "Office", "Home", "Home", "Home")), class = "data.frame", row.names = c(NA, 
-100L))

Problem

I have built this model and have written a printout for the fixed effect coefficients below:

lmer.work <- lmer(Mins_Work
                  ~ Coffee_Cups
                    (1|Work_Environment),
                  data = work)
sum.work <- summary(lmer.work)
sum.work$coefficients

Which gives me a pretty standard read of the coefficients:

             Estimate Std. Error        df  t value     Pr(>|t|)
(Intercept) 210.17185   71.55028  1.306848 2.937401 1.594028e-01
Coffee_Cups  29.93377    7.28184 96.286964 4.110743 8.297325e-05

However, I would like to convert this into a data frame with the coefficient terms on the right with their own column name. However, converting this into a matrix or a data frame doesn't seem to work in assigning a default column name to the terms column:

as.matrix(sum.work$coefficients)
as.data.frame(sum.work$coefficients)

Which gives the same result:

             Estimate Std. Error        df  t value     Pr(>|t|)
(Intercept) 210.17185   71.55028  1.306848 2.937401 1.594028e-01
Coffee_Cups  29.93377    7.28184 96.286964 4.110743 8.297325e-05

I've also tried to just force column names in with this code, but it doesn't seem to work:

colnames(sum.work$coefficients) <- c("Term",
                                     "Estimate",
                                     "Standard.Error",
                                     "DF",
                                     "T Value",
                                     "P Value")

The class function seems to indicate it is both a matrix and an array. I'm not entirely sure how to change the column names in then if this is the case, but anybody with a solution would be helpful. I'm trying to later include this into a flextable for a presentation.

CodePudding user response：

I'm not sure, if I understand you correctly. However, this might be a solution:

df <- as.data.frame(sum.work$coefficients)
df <- df %>%
  rownames_to_column(var="Term")

CodePudding user response：

You can use the broom.mixed package.

library("broom.mixed")

tidy(lmer.work, "fixed")
#> # A tibble: 2 × 5
#>   effect term        estimate std.error statistic
#>   <chr>  <chr>          <dbl>     <dbl>     <dbl>
#> 1 fixed  (Intercept)    210.      71.6       2.94
#> 2 fixed  Coffee_Cups     29.9      7.28      4.11

It's easy to extract fixed and random effects, so it's a quite flexible solution.

tidy(lmer.work)
#> # A tibble: 4 × 6
#>   effect   group            term            estimate std.error statistic
#>   <chr>    <chr>            <chr>              <dbl>     <dbl>     <dbl>
#> 1 fixed    <NA>             (Intercept)        210.      71.6       2.94
#> 2 fixed    <NA>             Coffee_Cups         29.9      7.28      4.11
#> 3 ran_pars Work_Environment sd__(Intercept)     91.6     NA        NA   
#> 4 ran_pars Residual         sd__Observation    163.      NA        NA