Glance at a(n) mfx 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.

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

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

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

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

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

Arguments

x

A logitmfx, negbinmfx, poissonmfx, or probitmfx object. (Note that betamfx objects receive their own set of tidiers.)

...

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 generic glance method wraps glance.glm() for applicable objects from the mfx package.

See also

glance.glm(), mfx::logitmfx(), mfx::negbinmfx(), mfx::poissonmfx(), mfx::probitmfx()

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

deviance

Deviance of 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.

null.deviance

Deviance of the null model.

Examples

# load libraries for models and data
library(mfx)

# get the marginal effects from a logit regression
mod_logmfx <- logitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars)

tidy(mod_logmfx, conf.int = TRUE)
#> # A tibble: 3 × 8
#>   term  atmean estimate std.error statistic p.value conf.low conf.high
#>   <chr> <lgl>     <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 cyl   TRUE    0.0538    0.113       0.475   0.635 -0.178     0.286  
#> 2 hp    TRUE    0.00359   0.00290     1.24    0.216 -0.00236   0.00954
#> 3 wt    TRUE   -1.01      0.668      -1.51    0.131 -2.38      0.359  

# compare with the naive model coefficients of the same logit call
tidy(
  glm(am ~ cyl + hp + wt, family = binomial, data = mtcars),
  conf.int = TRUE
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 4 × 7
#>   term        estimate std.error statistic p.value  conf.low conf.high
#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>     <dbl>     <dbl>
#> 1 (Intercept)  19.7       8.12       2.43   0.0152   8.56      44.3   
#> 2 cyl           0.488     1.07       0.455  0.649   -1.53       3.12  
#> 3 hp            0.0326    0.0189     1.73   0.0840   0.00332    0.0884
#> 4 wt           -9.15      4.15      -2.20   0.0276 -21.4       -3.48  

augment(mod_logmfx)
#> # A tibble: 32 × 11
#>    .rownames          am   cyl    hp    wt .fitted  .resid   .hat .sigma .cooksd
#>    <chr>           <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>
#>  1 Mazda RX4           1     6   110  2.62  2.24    0.449  0.278   0.595 1.42e-2
#>  2 Mazda RX4 Wag       1     6   110  2.88 -0.0912  1.22   0.352   0.529 2.30e-1
#>  3 Datsun 710          1     4    93  2.32  3.46    0.249  0.0960  0.602 9.26e-4
#>  4 Hornet 4 Drive      0     6   110  3.22 -3.20   -0.282  0.0945  0.601 1.17e-3
#>  5 Hornet Sportab…     0     8   175  3.44 -2.17   -0.466  0.220   0.595 1.03e-2
#>  6 Valiant             0     6   105  3.46 -5.61   -0.0856 0.0221  0.604 2.12e-5
#>  7 Duster 360          0     8   245  3.57 -1.07   -0.766  0.337   0.576 6.55e-2
#>  8 Merc 240D           0     4    62  3.19 -5.51   -0.0897 0.0376  0.603 4.10e-5
#>  9 Merc 230            0     4    95  3.15 -4.07   -0.184  0.122   0.603 6.76e-4
#> 10 Merc 280            0     6   123  3.44 -4.84   -0.126  0.0375  0.603 8.02e-5
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .std.resid <dbl>
glance(mod_logmfx)
#> # A tibble: 1 × 8
#>   null.deviance df.null logLik   AIC   BIC deviance df.residual  nobs
#>           <dbl>   <int>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
#> 1          43.2      31  -4.92  17.8  23.7     9.84          28    32

# another example, this time using probit regression
mod_probmfx <- probitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

tidy(mod_probmfx, conf.int = TRUE)
#> # A tibble: 3 × 8
#>   term  atmean estimate std.error statistic p.value conf.low conf.high
#>   <chr> <lgl>     <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 cyl   TRUE    0.0616    0.112       0.548  0.583  -0.169     0.292  
#> 2 hp    TRUE    0.00383   0.00282     1.36   0.174  -0.00194   0.00960
#> 3 wt    TRUE   -1.06      0.594      -1.78   0.0753 -2.27      0.160  
augment(mod_probmfx)
#> # A tibble: 32 × 11
#>    .rownames          am   cyl    hp    wt .fitted  .resid   .hat .sigma .cooksd
#>    <chr>           <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>
#>  1 Mazda RX4           1     6   110  2.62   1.21   0.490  0.308   0.585 2.05e-2
#>  2 Mazda RX4 Wag       1     6   110  2.88  -0.129  1.27   0.249   0.526 1.36e-1
#>  3 Datsun 710          1     4    93  2.32   1.85   0.256  0.134   0.594 1.48e-3
#>  4 Hornet 4 Drive      0     6   110  3.22  -1.92  -0.237  0.116   0.594 1.05e-3
#>  5 Hornet Sportab…     0     8   175  3.44  -1.25  -0.474  0.236   0.587 1.20e-2
#>  6 Valiant             0     6   105  3.46  -3.30  -0.0312 0.0111  0.596 1.39e-6
#>  7 Duster 360          0     8   245  3.57  -0.595 -0.804  0.285   0.567 5.32e-2
#>  8 Merc 240D           0     4    62  3.19  -3.31  -0.0304 0.0179  0.596 2.15e-6
#>  9 Merc 230            0     4    95  3.15  -2.47  -0.116  0.130   0.596 2.89e-4
#> 10 Merc 280            0     6   123  3.44  -2.85  -0.0662 0.0315  0.596 1.84e-5
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .std.resid <dbl>
glance(mod_probmfx)
#> # A tibble: 1 × 8
#>   null.deviance df.null logLik   AIC   BIC deviance df.residual  nobs
#>           <dbl>   <int>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
#> 1          43.2      31  -4.80  17.6  23.5     9.59          28    32