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 margins
glance(x, ...)Arguments
- x
A
marginsobject returned frommargins::margins().- ...
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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
Value
A tibble::tibble() with exactly one row and columns:
- adj.r.squared
Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.
- df
Degrees of freedom used by the model.
- df.residual
Residual degrees of freedom.
- nobs
Number of observations used.
- p.value
P-value corresponding to the test statistic.
- r.squared
R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.
- sigma
Estimated standard error of the residuals.
- statistic
Test statistic.
Examples
# load libraries for models and data
library(margins)
# example 1: logit model
mod_log <- glm(am ~ cyl + hp + wt, data = mtcars, family = binomial)
# get tidied "naive" model coefficients
tidy(mod_log)
#> # A tibble: 4 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 19.7 8.12 2.43 0.0152
#> 2 cyl 0.488 1.07 0.455 0.649
#> 3 hp 0.0326 0.0189 1.73 0.0840
#> 4 wt -9.15 4.15 -2.20 0.0276
# convert to marginal effects with margins()
marg_log <- margins(mod_log)
# get tidied marginal effects
tidy(marg_log)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 0.0215 0.0470 0.457 0.648
#> 2 hp 0.00143 0.000618 2.32 0.0204
#> 3 wt -0.403 0.115 -3.49 0.000487
tidy(marg_log, conf.int = TRUE)
#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 0.0215 0.0470 0.457 0.648 -0.0706 0.114
#> 2 hp 0.00143 0.000618 2.32 0.0204 0.000222 0.00265
#> 3 wt -0.403 0.115 -3.49 0.000487 -0.629 -0.176
# requires running the underlying model again. quick for this example
glance(marg_log)
#> # 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
# augmenting `margins` outputs isn't supported, but
# you can get the same info by running on the underlying model
augment(mod_log)
#> # 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 RX… 1 6 110 2.88 -0.0912 1.22 0.352 0.529 2.30e-1
#> 3 Datsun 7… 1 4 93 2.32 3.46 0.249 0.0960 0.602 9.26e-4
#> 4 Hornet 4… 0 6 110 3.22 -3.20 -0.282 0.0945 0.601 1.17e-3
#> 5 Hornet S… 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 3… 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>
# example 2: threeway interaction terms
mod_ie <- lm(mpg ~ wt * cyl * disp, data = mtcars)
# get tidied "naive" model coefficients
tidy(mod_ie)
#> # A tibble: 8 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 108. 23.3 4.62 0.000109
#> 2 wt -24.8 8.47 -2.92 0.00744
#> 3 cyl -10.8 4.34 -2.49 0.0201
#> 4 disp -0.593 0.213 -2.79 0.0102
#> 5 wt:cyl 2.91 1.42 2.05 0.0514
#> 6 wt:disp 0.184 0.0685 2.69 0.0127
#> 7 cyl:disp 0.0752 0.0268 2.81 0.00979
#> 8 wt:cyl:disp -0.0233 0.00861 -2.71 0.0123
# convert to marginal effects with margins()
marg_ie0 <- margins(mod_ie)
# get tidied marginal effects
tidy(marg_ie0)
#> # A tibble: 3 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl -3.85 1.46 -2.65 0.00812
#> 2 disp -0.0295 0.0174 -1.70 0.0900
#> 3 wt -2.01 1.17 -1.72 0.0860
glance(marg_ie0)
#> # A tibble: 1 × 12
#> r.squared adj.r.squared sigma statistic p.value df logLik AIC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.896 0.865 2.21 29.4 2.75e-10 7 -66.2 150.
#> # ℹ 4 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>,
#> # nobs <int>
# marginal effects evaluated at specific values of a variable (here: cyl)
marg_ie1 <- margins(mod_ie, at = list(cyl = c(4,6,8)))
# summarize model fit with tidiers
tidy(marg_ie1)
#> # A tibble: 9 × 7
#> term at.variable at.value estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl cyl 4 -3.85 1.46 -2.65 0.00808
#> 2 cyl cyl 6 -3.85 1.46 -2.65 0.00814
#> 3 cyl cyl 8 -3.85 1.46 -2.65 0.00812
#> 4 disp cyl 4 0.000978 0.0314 0.0312 0.975
#> 5 disp cyl 6 0.00134 0.0182 0.0737 0.941
#> 6 disp cyl 8 0.00170 0.0120 0.141 0.888
#> 7 wt cyl 4 7.91 5.06 1.56 0.118
#> 8 wt cyl 6 2.96 2.52 1.18 0.239
#> 9 wt cyl 8 -1.98 2.40 -0.825 0.409
# marginal effects of one interaction variable (here: wt), modulated at
# specific values of the two other interaction variables (here: cyl and drat)
marg_ie2 <- margins(mod_ie,
variables = "wt",
at = list(cyl = c(4,6,8), drat = c(3, 3.5, 4)))
# summarize model fit with tidiers
tidy(marg_ie2)
#> # A tibble: 18 × 7
#> term at.variable at.value estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 wt cyl 4 7.91 5.06 1.56 0.118
#> 2 wt drat 3 7.91 5.06 1.56 0.118
#> 3 wt cyl 4 7.91 5.06 1.56 0.118
#> 4 wt drat 3.5 7.91 5.06 1.56 0.118
#> 5 wt cyl 4 7.91 5.06 1.56 0.118
#> 6 wt drat 4 7.91 5.06 1.56 0.118
#> 7 wt cyl 6 2.96 2.52 1.18 0.239
#> 8 wt drat 3 2.96 2.52 1.18 0.239
#> 9 wt cyl 6 2.96 2.52 1.18 0.239
#> 10 wt drat 3.5 2.96 2.52 1.18 0.239
#> 11 wt cyl 6 2.96 2.52 1.18 0.239
#> 12 wt drat 4 2.96 2.52 1.18 0.239
#> 13 wt cyl 8 -1.98 2.40 -0.825 0.409
#> 14 wt drat 3 -1.98 2.40 -0.825 0.409
#> 15 wt cyl 8 -1.98 2.40 -0.825 0.409
#> 16 wt drat 3.5 -1.98 2.40 -0.825 0.409
#> 17 wt cyl 8 -1.98 2.40 -0.825 0.409
#> 18 wt drat 4 -1.98 2.40 -0.825 0.409