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Augment data with information from a(n) mfx object

Source: R/mfx-tidiers.R
augment.mfx.Rd

Augment accepts a model object and a dataset and adds information about each observation in the dataset. Most commonly, this includes predicted values in the .fitted column, residuals in the .resid column, and standard errors for the fitted values in a .se.fit column. New columns always begin with a . prefix to avoid overwriting columns in the original dataset.

Users may pass data to augment via either the data argument or the newdata argument. If the user passes data to the data argument, it must be exactly the data that was used to fit the model object. Pass datasets to newdata to augment data that was not used during model fitting. This still requires that at least all predictor variable columns used to fit the model are present. If the original outcome variable used to fit the model is not included in newdata, then no .resid column will be included in the output.

Augment will often behave differently depending on whether data or newdata is given. This is because there is often information associated with training observations (such as influences or related) measures that is not meaningfully defined for new observations.

For convenience, many augment methods provide default data arguments, so that augment(fit) will return the augmented training data. In these cases, augment tries to reconstruct the original data based on the model object with varying degrees of success.

The augmented dataset is always returned as a tibble::tibble with the same number of rows as the passed dataset. This means that the passed data must be coercible to a tibble. If a predictor enters the model as part of a matrix of covariates, such as when the model formula uses splines::ns(), stats::poly(), or survival::Surv(), it is represented as a matrix column.

We are in the process of defining behaviors for models fit with various na.action arguments, but make no guarantees about behavior when data is missing at this time.

Usage

# S3 method for class 'mfx'
augment(
  x,
  data = model.frame(x$fit),
  newdata = NULL,
  type.predict = c("link", "response", "terms"),
  type.residuals = c("deviance", "pearson"),
  se_fit = FALSE,
  ...
)

# S3 method for class 'logitmfx'
augment(
  x,
  data = model.frame(x$fit),
  newdata = NULL,
  type.predict = c("link", "response", "terms"),
  type.residuals = c("deviance", "pearson"),
  se_fit = FALSE,
  ...
)

# S3 method for class 'negbinmfx'
augment(
  x,
  data = model.frame(x$fit),
  newdata = NULL,
  type.predict = c("link", "response", "terms"),
  type.residuals = c("deviance", "pearson"),
  se_fit = FALSE,
  ...
)

# S3 method for class 'poissonmfx'
augment(
  x,
  data = model.frame(x$fit),
  newdata = NULL,
  type.predict = c("link", "response", "terms"),
  type.residuals = c("deviance", "pearson"),
  se_fit = FALSE,
  ...
)

# S3 method for class 'probitmfx'
augment(
  x,
  data = model.frame(x$fit),
  newdata = NULL,
  type.predict = c("link", "response", "terms"),
  type.residuals = c("deviance", "pearson"),
  se_fit = FALSE,
  ...
)

Arguments

x

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

data

A base::data.frame or tibble::tibble() containing the original data that was used to produce the object x. Defaults to stats::model.frame(x) so that augment(my_fit) returns the augmented original data. Do not pass new data to the data argument. Augment will report information such as influence and cooks distance for data passed to the data argument. These measures are only defined for the original training data.

newdata

A base::data.frame() or tibble::tibble() containing all the original predictors used to create x. Defaults to NULL, indicating that nothing has been passed to newdata. If newdata is specified, the data argument will be ignored.

type.predict

Passed to stats::predict.glm() type argument. Defaults to "link".

type.residuals

Passed to stats::residuals.glm() and to stats::rstandard.glm() type arguments. Defaults to "deviance".

se_fit

Logical indicating whether or not a .se.fit column should be added to the augmented output. For some models, this calculation can be somewhat time-consuming. Defaults to FALSE.

...

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

See also

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

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

Value

A tibble::tibble() with columns:

.cooksd

Cooks distance.

.fitted

Fitted or predicted value.

.hat

Diagonal of the hat matrix.

.resid

The difference between observed and fitted values.

.se.fit

Standard errors of fitted values.

.sigma

Estimated residual standard deviation when corresponding observation is dropped from model.

.std.resid

Standardised residuals.

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 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>
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 RX…     1     6   110  2.88  -0.129  1.27   0.249   0.526 1.36e-1
#>  3 Datsun 7…     1     4    93  2.32   1.85   0.256  0.134   0.594 1.48e-3
#>  4 Hornet 4…     0     6   110  3.22  -1.92  -0.237  0.116   0.594 1.05e-3
#>  5 Hornet S…     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 3…     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