Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for margins
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A margins object returned from margins::margins().

conf.int

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

conf.level

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

...

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.level = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Details

The margins package provides a way to obtain coefficient marginal effects for a variety of (non-linear) models, such as logit or models with multiway interaction terms. Note that the glance.margins() method requires rerunning the underlying model again, which can take some time. Similarly, an augment.margins() method is not currently supported, but users can simply run the underlying model to obtain the same information.

See also

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples

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 x 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::margins() marg_log <- margins(mod_log) # Get tidied marginal effects tidy(marg_log)
#> # A tibble: 3 x 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 x 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
glance(marg_log) ## Requires running the underlying model again. Quick for this example.
#> # A tibble: 1 x 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
if (FALSE) augment(marg_log) ## Not supported. augment(mod_log) ## But can get the same info by running on the underlying model.
#> # A tibble: 32 x 11 #> .rownames am cyl hp wt .fitted .resid .std.resid .hat .sigma #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 1 6 110 2.62 2.24 0.449 0.529 0.278 0.595 #> 2 Mazda RX… 1 6 110 2.88 -0.0912 1.22 1.51 0.352 0.529 #> 3 Datsun 7… 1 4 93 2.32 3.46 0.249 0.262 0.0960 0.602 #> 4 Hornet 4… 0 6 110 3.22 -3.20 -0.282 -0.297 0.0945 0.601 #> 5 Hornet S… 0 8 175 3.44 -2.17 -0.466 -0.527 0.220 0.595 #> 6 Valiant 0 6 105 3.46 -5.61 -0.0856 -0.0866 0.0221 0.604 #> 7 Duster 3… 0 8 245 3.57 -1.07 -0.766 -0.941 0.337 0.576 #> 8 Merc 240D 0 4 62 3.19 -5.51 -0.0897 -0.0915 0.0376 0.603 #> 9 Merc 230 0 4 95 3.15 -4.07 -0.184 -0.196 0.122 0.603 #> 10 Merc 280 0 6 123 3.44 -4.84 -0.126 -0.128 0.0375 0.603 #> # … with 22 more rows, and 1 more variable: .cooksd <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 x 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::margins() marg_ie0 <- margins(mod_ie) # Get tidied marginal effects tidy(marg_ie0)
#> # A tibble: 3 x 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 x 12 #> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0.896 0.865 2.21 29.4 2.75e-10 7 -66.2 150. 164. #> # … with 3 more variables: 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))) tidy(marg_ie1)
#> # A tibble: 9 x 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 0.00814 #> 2 cyl cyl 6 -3.85 1.46 -2.65 0.00812 #> 3 cyl cyl 8 -3.85 1.46 -2.65 0.00810 #> 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", ## Main var at = list(cyl = c(4,6,8), drat = c(3, 3.5, 4))) ## Modulating vars tidy(marg_ie2)
#> # A tibble: 18 x 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