Augment data with information from fitted model objects

Augment accepts a fitted model object and a data set and adds information about each observation in the data set. New columns always begin with a . prefix to avoid overwriting columns in the original data set.

Augment behaves differently depending on whether the original data or new data requires augmenting. Typically, when augmenting the original data, only the fitted model object is specified, and when augmenting new data, the fitted model object and newdata is specified. When augmenting the original data, diagnostic statistics are augmented to each row in the data set. When augmenting new data, predictions and optional intervals or standard errors are augmented to each row in the new data set.

Usage

# S3 method for splm
augment(
  x,
  drop = TRUE,
  newdata = NULL,
  se_fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  local,
  ...
)

# S3 method for spautor
augment(
  x,
  drop = TRUE,
  newdata = NULL,
  se_fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  local,
  ...
)

# S3 method for spglm
augment(
  x,
  drop = TRUE,
  newdata = NULL,
  type = c("link", "response"),
  se_fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  newdata_size,
  level = 0.95,
  local = local,
  var_correct = TRUE,
  ...
)

# S3 method for spgautor
augment(
  x,
  drop = TRUE,
  newdata = NULL,
  type = c("link", "response"),
  se_fit = FALSE,
  interval = c("none", "confidence", "prediction"),
  newdata_size,
  level = 0.95,
  local,
  var_correct = TRUE,
  ...
)

Arguments

x: A fitted model object from splm() or spautor().
drop: A logical indicating whether to drop extra variables in the fitted model object x when augmenting. The default for drop is TRUE. drop is ignored if augmenting newdata.
newdata: A data frame or tibble containing observations requiring prediction. All of the original explanatory variables used to create the fitted model object x must be present in newdata. Defaults to NULL, which indicates that nothing has been passed to newdata.
se_fit: Logical indicating whether or not a .se.fit column should be added to augmented output. Passed to predict() and defaults to FALSE.
interval: Character indicating the type of confidence interval columns to add to the augmented newdata output. Passed to predict() and defaults to "none".
level: Tolerance/confidence level. The default is 0.95.
local: A list or logical. If a list, specific list elements described in predict.spmodel() control the big data approximation behavior. If a logical, TRUE chooses default list elements for the list version of local as specified in predict.spmodel(). Defaults to FALSE, which performs exact computations.
...: Other arguments. Not used (needed for generic consistency).
type: The scale (response or link) of predictions obtained using spglm() or spgautor objects.
newdata_size: The size value for each observation in newdata used when predicting for the binomial family.
var_correct: A logical indicating whether to return the corrected prediction variances when predicting via models fit using spglm() or spgautor(). The default is TRUE.

Value

When augmenting the original data set, a tibble with additional columns

.fitted Fitted value
.resid Response residual (the difference between observed and fitted values)
.hat Leverage (diagonal of the hat matrix)
.cooksd Cook's distance
.std.resid Standardized residuals
.se.fit Standard error of the fitted value.

When augmenting a new data set, a tibble with additional columns

.fitted Predicted (or fitted) value
.lower Lower bound on interval
.upper Upper bound on interval
.se.fit Standard error of the predicted (or fitted) value

Details

augment() returns a tibble with the same class as data. That is, if data is an sf object, then the augmented object (obtained via augment(x)) will be an sf object as well. When augmenting newdata, the augmented object has the same class as data.

Missing response values from the original data can be augmented as if they were a newdata object by providing x$newdata to the newdata argument (where x is the name of the fitted model object). This is the only way to compute predictions for spautor() and spgautor() fitted model objects.

Examples

spmod <- splm(z ~ water + tarp,
  data = caribou,
  spcov_type = "exponential", xcoord = x, ycoord = y
)
augment(spmod)
#> # A tibble: 30 × 10
#>        z water tarp      x     y .fitted  .resid  .hat .cooksd .std.resid
#>    <dbl> <fct> <fct> <dbl> <dbl>   <dbl>   <dbl> <dbl>   <dbl>      <dbl>
#>  1  2.42 Y     clear     1     6    1.97  0.454  0.116 0.209        2.53 
#>  2  2.44 Y     shade     2     6    2.25  0.190  0.137 0.0468       1.09 
#>  3  1.81 Y     none      3     6    2.05 -0.237  0.137 0.0752      -1.38 
#>  4  1.97 N     clear     4     6    2.05 -0.0838 0.174 0.00211     -0.200
#>  5  2.38 N     shade     5     6    2.34  0.0407 0.153 0.0159       0.594
#>  6  2.22 Y     none      1     5    2.05  0.177  0.147 0.0434       1.00 
#>  7  2.10 N     clear     2     5    2.05  0.0512 0.156 0.00936      0.450
#>  8  1.80 Y     clear     3     5    1.97 -0.163  0.122 0.0135      -0.624
#>  9  1.96 Y     shade     4     5    2.25 -0.290  0.119 0.0642      -1.38 
#> 10  2.10 Y     none      5     5    2.05  0.0522 0.131 0.0264       0.837
#> # ℹ 20 more rows
spmod_sulf <- splm(sulfate ~ 1, data = sulfate, spcov_type = "exponential")
augment(spmod_sulf)
#> Simple feature collection with 197 features and 6 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -2292550 ymin: 386181.1 xmax: 2173345 ymax: 3090370
#> Projected CRS: NAD83 / Conus Albers
#> # A tibble: 197 × 7
#>    sulfate .fitted .resid    .hat   .cooksd .std.resid           geometry
#>  *   <dbl>   <dbl>  <dbl>   <dbl>     <dbl>      <dbl>        <POINT [m]>
#>  1  12.9      5.92   7.00 0.00334 0.00161      -0.694  (817738.8 1080571)
#>  2  20.2      5.92  14.2  0.00256 0.00192       0.865  (914593.6 1295545)
#>  3  16.8      5.92  10.9  0.00259 0.000395      0.390  (359574.1 1178228)
#>  4  16.2      5.92  10.3  0.00239 0.000363      0.390  (265331.9 1239089)
#>  5   7.86     5.92   1.93 0.00202 0.00871      -2.07   (304528.8 1453636)
#>  6  15.4      5.92   9.43 0.00201 0.000240      0.345  (162932.8 1451625)
#>  7   0.986    5.92  -4.94 0.00380 0.000966     -0.503  (-1437776 1568022)
#>  8   0.425    5.92  -5.50 0.0138  0.00584      -0.646  (-1572878 1125529)
#>  9   3.58     5.92  -2.34 0.00673 0.0000148    -0.0467 (-1282009 1204889)
#> 10   2.38     5.92  -3.54 0.0123  0.0000139    -0.0335 (-1972775 1464991)
#> # ℹ 187 more rows
augment(spmod_sulf, newdata = sulfate_preds)
#> Simple feature collection with 100 features and 1 field
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -2283774 ymin: 582930.5 xmax: 1985906 ymax: 3037173
#> Projected CRS: NAD83 / Conus Albers
#> # A tibble: 100 × 2
#>    .fitted            geometry
#>  *   <dbl>         <POINT [m]>
#>  1    1.62  (-1771413 1752976)
#>  2   24.4    (1018112 1867127)
#>  3    8.95 (-291256.8 1553212)
#>  4   16.5    (1274293 1267835)
#>  5    4.93 (-547437.6 1638825)
#>  6   26.8    (1445080 1981278)
#>  7    2.87  (-1629090 3037173)
#>  8   14.3    (1302757 1039534)
#>  9    1.53  (-1429838 2523494)
#> 10   14.3    (1131970 1096609)
#> # ℹ 90 more rows
# missingness in original data
spmod_seal <- spautor(log_trend ~ 1, data = seal, spcov_type = "car")
augment(spmod_seal)
#> Simple feature collection with 34 features and 6 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 980001.5 ymin: 1010815 xmax: 1116002 ymax: 1145054
#> Projected CRS: NAD83 / Alaska Albers
#> # A tibble: 34 × 7
#>    log_trend .fitted  .resid   .hat .cooksd .std.resid                  geometry
#>  *     <dbl>   <dbl>   <dbl>  <dbl>   <dbl>      <dbl>             <POLYGON [m]>
#>  1  -0.282   -0.0709 -0.211  0.0161 0.0209      -1.13  ((1037002 1039492, 10370…
#>  2  -0.00121 -0.0709  0.0697 0.0473 0.0256       0.718 ((1070158 1030216, 10701…
#>  3   0.0354  -0.0709  0.106  0.0290 0.0183       0.782 ((1054906 1034826, 10549…
#>  4  -0.0160  -0.0709  0.0549 0.0228 0.00157      0.260 ((1025142 1056940, 10251…
#>  5   0.0872  -0.0709  0.158  0.0276 0.0383       1.16  ((1026035 1044623, 10260…
#>  6  -0.266   -0.0709 -0.195  0.0287 0.0530      -1.34  ((1100345 1060709, 11002…
#>  7   0.0743  -0.0709  0.145  0.0491 0.0901       1.32  ((1030247 1029637, 10302…
#>  8  -0.00961 -0.0709  0.0613 0.0122 0.00242      0.442 ((1116002 1024542, 11160…
#>  9  -0.182   -0.0709 -0.111  0.0225 0.0224      -0.986 ((1079864 1025088, 10798…
#> 10   0.00351 -0.0709  0.0744 0.0316 0.0100       0.555 ((1110363 1037056, 11103…
#> # ℹ 24 more rows
augment(spmod_seal, newdata = spmod_seal$newdata)
#> Simple feature collection with 28 features and 2 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 913618.8 ymin: 1007542 xmax: 1115097 ymax: 1132682
#> Projected CRS: NAD83 / Alaska Albers
#> # A tibble: 28 × 3
#>    log_trend  .fitted                                                   geometry
#>  *     <dbl>    <dbl>                                              <POLYGON [m]>
#>  1        NA -0.115   ((1035002 1054710, 1035002 1054542, 1035002 1053542, 1035…
#>  2        NA -0.00908 ((1043093 1020553, 1043097 1020550, 1043101 1020550, 1043…
#>  3        NA -0.0602  ((1099737 1054310, 1099752 1054262, 1099788 1054278, 1099…
#>  4        NA -0.0359  ((1099002 1036542, 1099134 1036462, 1099139 1036431, 1099…
#>  5        NA -0.0723  ((1076902 1053189, 1076912 1053179, 1076931 1053179, 1076…
#>  6        NA -0.0548  ((1070501 1046969, 1070317 1046598, 1070308 1046542, 1070…
#>  7        NA -0.0976  ((1072995 1054942, 1072996 1054910, 1072997 1054878, 1072…
#>  8        NA -0.0714  ((960001.5 1127667, 960110.8 1127542, 960144.1 1127495, 9…
#>  9        NA -0.0825  ((1031308 1079817, 1031293 1079754, 1031289 1079741, 1031…
#> 10        NA -0.0592  ((998923.7 1053647, 998922.5 1053609, 998950 1053631, 999…
#> # ℹ 18 more rows