Tidy a fitted model object

Tidy a fitted model object into a summarized tibble.

# S3 method for ssn_lm
tidy(x, conf.int = FALSE, conf.level = 0.95, effects = "fixed", ...)

# S3 method for ssn_glm
tidy(x, conf.int = FALSE, conf.level = 0.95, effects = "fixed", ...)

Arguments

x

A fitted model object from ssn_lm() or ssn_glm().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. The default is FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int is TRUE. Must be strictly greater than 0 and less than 1. The default is 0.95, which corresponds to a 95 percent confidence interval.

effects

The type of effects to tidy. Available options are "fixed" (fixed effects), "tailup" (tailup covariance parameters), "taildown" (taildown covariance parameters), "euclid" (Euclidean covariance parameters), "nugget" (nugget covariance parameter), "dispersion" (dispersion parameter if relevant), "ssn" for all of "tailup", "taildown", "euclid", "nugget", and "dispersion", and "randcov" (random effect variances). The default is "fixed".

...

Other arguments. Not used (needed for generic consistency).

Value

A tidy tibble of summary information effects.

See also

glance.SSN2() augment.SSN2()

Examples

# Copy the mf04p .ssn data to a local directory and read it into R
# When modeling with your .ssn object, you will load it using the relevant
# path to the .ssn data on your machine
copy_lsn_to_temp()
temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
mf04p <- ssn_import(temp_path, overwrite = TRUE)

ssn_mod <- ssn_lm(
  formula = Summer_mn ~ ELEV_DEM,
  ssn.object = mf04p,
  tailup_type = "exponential",
  additive = "afvArea"
)
tidy(ssn_mod)
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)  80.9      8.81         9.17 0       
#> 2 ELEV_DEM     -0.0341   0.00438     -7.80 6.22e-15