Tidier methods for Julia regression models

Tidier methods for Julia regression models

Usage

# S3 method for class 'jlmer_mod'
tidy(x, effects = c("var_model", "ran_pars", "fixed"), ...)

# S3 method for class 'jlmer_mod'
glance(x, ...)

Arguments

x

An object of class jlmer_mod

effects

One of "var_model", "ran_pars", or "fixed"

...

Unused

Value

A data frame

Examples

# \donttest{

# Fixed-effects only model
mod1 <- to_jlmer(weight ~ 1 + Diet, ChickWeight)
tidy(mod1)
#> # A tibble: 4 × 5
#>   term        estimate std.error statistic   p.value
#>   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
#> 1 (Intercept)    103.       4.67     22.0  6.77e-107
#> 2 Diet2           20.0      7.87      2.54 1.11e-  2
#> 3 Diet3           40.3      7.87      5.12 3.01e-  7
#> 4 Diet4           32.6      7.91      4.12 3.73e-  5
glance(mod1)
#> # A tibble: 1 × 8
#>    nobs    df sigma logLik   AIC   BIC deviance df.residual
#>   <dbl> <int> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <dbl>
#> 1   578     5  69.3 -3268. 6546. 6568. 2758693.         573

# Mixed model
mod2 <- to_jlmer(weight ~ 1 + Diet + (1 | Chick), ChickWeight)
tidy(mod2)
#> # A tibble: 6 × 7
#>   effect   group    term            estimate std.error statistic   p.value
#>   <chr>    <chr>    <chr>              <dbl>     <dbl>     <dbl>     <dbl>
#> 1 fixed    NA       (Intercept)        102.       5.78     17.6   2.74e-69
#> 2 fixed    NA       Diet2               20.8      9.79      2.13  3.34e- 2
#> 3 fixed    NA       Diet3               41.2      9.79      4.20  2.63e- 5
#> 4 fixed    NA       Diet4               33.3      9.83      3.39  7.08e- 4
#> 5 ran_pars Chick    sd__(Intercept)     15.7     NA        NA    NA       
#> 6 ran_pars Residual sd__Observation     67.3     NA        NA    NA       
glance(mod2)
#> # A tibble: 1 × 8
#>    nobs    df sigma logLik   AIC   BIC deviance df.residual
#>   <int> <int> <dbl>  <dbl> <dbl> <dbl>    <dbl>       <int>
#> 1   578     6  67.3 -3265. 6542. 6568.    6530.         572

# Select which of fixed/random effects to return
tidy(mod2, effects = "fixed")
#> # A tibble: 4 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)    102.       5.78     17.6  2.74e-69
#> 2 Diet2           20.8      9.79      2.13 3.34e- 2
#> 3 Diet3           41.2      9.79      4.20 2.63e- 5
#> 4 Diet4           33.3      9.83      3.39 7.08e- 4
tidy(mod2, effects = "ran_pars")
#> # A tibble: 2 × 3
#>   group    term            estimate
#>   <chr>    <chr>              <dbl>
#> 1 Chick    sd__(Intercept)     15.7
#> 2 Residual sd__Observation     67.3

# }