Glance at a(n) betamfx object

Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

Usage

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

Arguments

x

A betamfx object.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

This glance method wraps glance.betareg() for mfx::betamfx() objects.

See also

glance.betareg(), mfx::betamfx()

Other mfx tidiers: augment.betamfx(), augment.mfx(), glance.mfx(), tidy.betamfx(), tidy.mfx()

Value

A tibble::tibble() with exactly one row and columns:

AIC

Akaike's Information Criterion for the model.

BIC

Bayesian Information Criterion for the model.

df.null

Degrees of freedom used by the null model.

df.residual

Residual degrees of freedom.

logLik

The log-likelihood of the model. [stats::logLik()] may be a useful reference.

nobs

Number of observations used.

pseudo.r.squared

Like the R squared statistic, but for situations when the R squared statistic isn't defined.

Examples

library(mfx)

# Simulate some data
set.seed(12345)
n <- 1000
x <- rnorm(n)

# Beta outcome
y <- rbeta(n, shape1 = plogis(1 + 0.5 * x), shape2 = (abs(0.2 * x)))
# Use Smithson and Verkuilen correction
y <- (y * (n - 1) + 0.5) / n

d <- data.frame(y, x)
mod_betamfx <- betamfx(y ~ x | x, data = d)

tidy(mod_betamfx, conf.int = TRUE)
#> # A tibble: 1 × 8
#>   term  atmean estimate std.error statistic p.value conf.low conf.high
#>   <chr> <lgl>     <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 x     TRUE     0.0226   0.00801      2.82 0.00483  0.00686    0.0383

# Compare with the naive model coefficients of the equivalent betareg call (not run)
# tidy(betamfx(y ~ x | x, data = d), conf.int = TRUE)

augment(mod_betamfx)
#> # A tibble: 1,000 × 4
#>        y      x .fitted   .cooksd
#>    <dbl>  <dbl>   <dbl>     <dbl>
#>  1 0.951  0.586   0.809 0.000189 
#>  2 0.714  0.709   0.811 0.0000993
#>  3 0.999 -0.109   0.793 0.000273 
#>  4 0.998 -0.453   0.785 0.000334 
#>  5 0.999  0.606   0.809 0.000342 
#>  6 0.562 -1.82    0.751 0.000878 
#>  7 0.999  0.630   0.810 0.000348 
#>  8 0.999 -0.276   0.789 0.000294 
#>  9 0.744 -0.284   0.789 0.0000134
#> 10 0.999 -0.919   0.774 0.000551 
#> # ℹ 990 more rows
glance(mod_betamfx)
#> # A tibble: 1 × 7
#>   pseudo.r.squared df.null logLik    AIC    BIC df.residual  nobs
#>              <dbl>   <dbl>  <dbl>  <dbl>  <dbl>       <int> <int>
#> 1          0.00726     998  1897. -3787. -3767.         996  1000