Predicted values and intervals based on a fitted model object.
Usage
# S3 method for splm
predict(
object,
newdata,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
local,
...
)
# S3 method for spautor
predict(
object,
newdata,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
local,
...
)
# S3 method for splm_list
predict(
object,
newdata,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
local,
...
)
# S3 method for spautor_list
predict(
object,
newdata,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
local,
...
)
# S3 method for splmRF
predict(object, newdata, local, ...)
# S3 method for spautorRF
predict(object, newdata, local, ...)
# S3 method for splmRF_list
predict(object, newdata, local, ...)
# S3 method for spautorRF_list
predict(object, newdata, local, ...)
# S3 method for spglm
predict(
object,
newdata,
type = c("link", "response"),
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
newdata_size,
level = 0.95,
local,
var_correct = TRUE,
...
)
# S3 method for spgautor
predict(
object,
newdata,
type = c("link", "response"),
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
newdata_size,
level = 0.95,
local,
var_correct = TRUE,
...
)
# S3 method for spglm_list
predict(
object,
newdata,
type = c("link", "response"),
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
newdata_size,
level = 0.95,
local,
var_correct = TRUE,
...
)
# S3 method for spgautor_list
predict(
object,
newdata,
type = c("link", "response"),
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
newdata_size,
level = 0.95,
local,
var_correct = TRUE,
...
)Arguments
- object
A fitted model object.
- newdata
A data frame or
sfobject in which to look for variables with which to predict. If a data frame,newdatamust contain all variables used byformula(object)and all variables representing coordinates. If ansfobject,newdatamust contain all variables used byformula(object)and coordinates are obtained from the geometry ofnewdata. If omitted, missing data from the fitted model object are used.- se.fit
A logical indicating if standard errors are returned. The default is
FALSE.- interval
Type of interval calculation. The default is
"none". Other options are"confidence"(for confidence intervals) and"prediction"(for prediction intervals).- level
Tolerance/confidence level. The default is
0.95.- local
A optional logical or list controlling the big data approximation. If omitted,
localis set toTRUEorFALSEbased on the sample size of the fitted model object and/or the prediction size ofnewdata-- if the sample size or prediction size exceeds 5000,localis set toTRUE, otherwise it is set toFALSE. IfFALSE, no big data approximation is implemented. If a list is provided, the following arguments detail the big data approximation:method: The big data approximation method. Ifmethod = "all", all observations are used andsizeis ignored. Ifmethod = "distance", thesizedata observations closest (in terms of Euclidean distance) to the observation requiring prediction are used. Ifmethod = "covariance", thesizedata observations with the highest covariance with the observation requiring prediction are used. If random effects and partition factors are not used in estimation and the spatial covariance function is monotone decreasing,"distance"and"covariance"are equivalent. The default is"covariance". Only used with models fit usingsplm()orspglm().size: The number of data observations to use whenmethodis"distance"or"covariance". The default is 100. Only used with models fit usingsplm()orspglm().parallel: IfTRUE, parallel processing via the parallel package is automatically used. The default isFALSE.ncores: Ifparallel = TRUE, the number of cores to parallelize over. The default is the number of available cores on your machine.
When
localis a list, at least one list element must be provided to initialize default arguments for the other list elements. IflocalisTRUE, defaults forlocalare chosen such thatlocalis transformed intolist(size = 100, method = "covariance", parallel = FALSE).- ...
Other arguments. Only used for models fit using
splmRF()orspautorRF()where...indicates other arguments toranger::predict.ranger().- type
The scale (
responseorlink) of predictions obtained usingspglm()orspgautorobjects.- newdata_size
The
sizevalue for each observation innewdataused 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()orspgautor(). The default isTRUE.
Value
For splm or spautor objects, if se.fit is FALSE, predict() returns
a vector of predictions or a matrix of predictions with column names
fit, lwr, and upr if interval is "confidence"
or "prediction". If se.fit is TRUE, a list with the following components is returned:
fit: vector or matrix as abovese.fit: standard error of each fit
For splm_list or spautor_list objects, a list that contains relevant quantities for each
list element.
For splmRF or spautorRF objects, a vector of predictions. For splmRF_list
or spautorRF_list objects, a list that contains relevant quantities for each list element.
Details
For splm and spautor objects, the (empirical) best linear unbiased predictions (i.e., Kriging
predictions) at each site are returned when interval is "none"
or "prediction" alongside standard errors. Prediction intervals
are also returned if interval is "prediction". When
interval is "confidence", the estimated mean is returned
alongside standard errors and confidence intervals for the mean. For splm_list
and spautor_list objects, predictions and associated intervals and standard errors are returned
for each list element.
For splmRF or spautorRF objects, random forest spatial residual
model predictions are computed by combining the random forest prediction with
the (empirical) best linear unbiased prediction for the residual. Fox et al. (2020)
call this approach random forest regression Kriging. For splmRF_list
or spautorRF objects,
predictions are returned for each list element.
References
Fox, E.W., Ver Hoef, J. M., & Olsen, A. R. (2020). Comparing spatial regression to random forests for large environmental data sets. PloS one, 15(3), e0229509.
Examples
spmod <- splm(sulfate ~ 1,
data = sulfate,
spcov_type = "exponential", xcoord = x, ycoord = y
)
predict(spmod, sulfate_preds)
#> 1 2 3 4 5 6 7 8
#> 1.618435 24.371803 8.945102 16.452377 4.925139 26.795441 2.869041 14.255129
#> 9 10 11 12 13 14 15 16
#> 1.527998 14.278871 1.201078 17.125020 1.320418 20.139591 21.295541 11.237529
#> 17 18 19 20 21 22 23 24
#> 18.683866 9.663971 1.788953 23.631754 3.275535 2.350008 2.048598 2.010150
#> 25 26 27 28 29 30 31 32
#> 3.201960 1.493974 18.623392 7.817861 18.797743 5.125182 1.807167 4.075101
#> 33 34 35 36 37 38 39 40
#> 12.427273 3.152303 1.293478 6.957413 18.939770 7.297365 14.674697 15.520246
#> 41 42 43 44 45 46 47 48
#> 12.405048 24.918442 17.301734 26.864540 1.603280 8.549257 3.784855 14.937188
#> 49 50 51 52 53 54 55 56
#> 24.494371 1.956122 27.204132 13.574770 2.576245 1.914563 7.487318 17.136559
#> 57 58 59 60 61 62 63 64
#> 17.014448 1.337634 18.683246 12.688687 15.393346 23.574345 1.579663 17.036054
#> 65 66 67 68 69 70 71 72
#> 28.544693 1.288539 1.140777 6.171537 3.437062 10.789035 3.615948 17.829018
#> 73 74 75 76 77 78 79 80
#> 8.908869 7.301336 2.126375 1.660542 19.373718 1.280324 16.307787 15.288219
#> 81 82 83 84 85 86 87 88
#> 17.793902 3.636393 9.731890 1.843879 1.673207 7.792596 2.326289 1.614190
#> 89 90 91 92 93 94 95 96
#> 2.694977 19.431798 4.044955 7.952300 12.423394 25.946812 15.137847 16.654989
#> 97 98 99 100
#> 21.603763 20.798900 16.737467 16.730626
predict(spmod, sulfate_preds, interval = "prediction")
#> fit lwr upr
#> 1 1.618435 -6.4458691 9.682739
#> 2 24.371803 16.8475856 31.896020
#> 3 8.945102 1.0255301 16.864674
#> 4 16.452377 8.6427128 24.262041
#> 5 4.925139 -2.7823585 12.632637
#> 6 26.795441 19.2836818 34.307199
#> 7 2.869041 -5.0546545 10.792737
#> 8 14.255129 6.7727705 21.737488
#> 9 1.527998 -6.3288119 9.384809
#> 10 14.278871 6.6143954 21.943347
#> 11 1.201078 -6.7412477 9.143403
#> 12 17.125020 9.6129332 24.637107
#> 13 1.320418 -6.8475567 9.488393
#> 14 20.139591 12.7287130 27.550469
#> 15 21.295541 13.6157754 28.975307
#> 16 11.237529 3.2332125 19.241846
#> 17 18.683866 11.1027931 26.264939
#> 18 9.663971 1.7928039 17.535138
#> 19 1.788953 -6.1854294 9.763336
#> 20 23.631754 16.0720133 31.191496
#> 21 3.275535 -4.7284202 11.279490
#> 22 2.350008 -5.1135227 9.813539
#> 23 2.048598 -6.0317412 10.128936
#> 24 2.010150 -5.7631079 9.783408
#> 25 3.201960 -4.5302411 10.934161
#> 26 1.493974 -6.3460895 9.334038
#> 27 18.623392 11.1134630 26.133320
#> 28 7.817861 -0.3524618 15.988184
#> 29 18.797743 11.0398095 26.555676
#> 30 5.125182 -2.6982901 12.948653
#> 31 1.807167 -6.3415178 9.955852
#> 32 4.075101 -3.5663732 11.716575
#> 33 12.427273 4.6769673 20.177579
#> 34 3.152303 -4.4463365 10.750943
#> 35 1.293478 -6.8488219 9.435778
#> 36 6.957413 -0.9683646 14.883191
#> 37 18.939770 11.6346562 26.244884
#> 38 7.297365 -0.5228443 15.117575
#> 39 14.674697 6.7673697 22.582024
#> 40 15.520246 7.7948540 23.245638
#> 41 12.405048 4.4262734 20.383823
#> 42 24.918442 17.5864895 32.250394
#> 43 17.301734 9.6310247 24.972444
#> 44 26.864540 19.4240569 34.305024
#> 45 1.603280 -6.2004973 9.407057
#> 46 8.549257 0.4298523 16.668662
#> 47 3.784855 -3.6490992 11.218809
#> 48 14.937188 7.1596394 22.714737
#> 49 24.494371 16.8717764 32.116966
#> 50 1.956122 -6.0097993 9.922043
#> 51 27.204132 19.8157834 34.592480
#> 52 13.574770 6.0621552 21.087384
#> 53 2.576245 -5.1022238 10.254714
#> 54 1.914563 -6.2911949 10.120320
#> 55 7.487318 -0.3755634 15.350200
#> 56 17.136559 9.4820700 24.791047
#> 57 17.014448 9.2249276 24.803968
#> 58 1.337634 -6.4385724 9.113841
#> 59 18.683246 11.3039468 26.062546
#> 60 12.688687 4.7747305 20.602644
#> 61 15.393346 7.8685304 22.918162
#> 62 23.574345 16.0955211 31.053169
#> 63 1.579663 -6.2038712 9.363198
#> 64 17.036054 9.2258934 24.846214
#> 65 28.544693 21.0374571 36.051929
#> 66 1.288539 -6.8107592 9.387838
#> 67 1.140777 -6.9519175 9.233472
#> 68 6.171537 -1.7667540 14.109829
#> 69 3.437062 -4.6208291 11.494953
#> 70 10.789035 3.0605613 18.517508
#> 71 3.615948 -4.3318041 11.563701
#> 72 17.829018 10.5010624 25.156974
#> 73 8.908869 1.3774432 16.440295
#> 74 7.301336 -0.7182311 15.320904
#> 75 2.126375 -6.0821512 10.334901
#> 76 1.660542 -5.8572805 9.178365
#> 77 19.373718 11.6821400 27.065296
#> 78 1.280324 -6.5594020 9.120049
#> 79 16.307787 8.5576064 24.057967
#> 80 15.288219 7.3523742 23.224064
#> 81 17.793902 10.2290340 25.358770
#> 82 3.636393 -3.8379555 11.110742
#> 83 9.731890 1.9565364 17.507243
#> 84 1.843879 -5.9288922 9.616651
#> 85 1.673207 -6.0232268 9.369641
#> 86 7.792596 -0.3486329 15.933825
#> 87 2.326289 -5.6078937 10.260471
#> 88 1.614190 -6.0053643 9.233745
#> 89 2.694977 -4.9535614 10.343516
#> 90 19.431798 12.0731376 26.790459
#> 91 4.044955 -4.0206587 12.110569
#> 92 7.952300 0.1344633 15.770137
#> 93 12.423394 4.4963234 20.350464
#> 94 25.946812 18.4522301 33.441395
#> 95 15.137847 7.2417146 23.033980
#> 96 16.654989 9.0689535 24.241024
#> 97 21.603763 13.8886420 29.318883
#> 98 20.798900 13.5068048 28.090995
#> 99 16.737467 9.3086051 24.166329
#> 100 16.730626 8.7188384 24.742414
augment(spmod, newdata = sulfate_preds, interval = "prediction")
#> Simple feature collection with 100 features and 3 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -2283774 ymin: 582930.5 xmax: 1985906 ymax: 3037173
#> Projected CRS: NAD83 / Conus Albers
#> # A tibble: 100 × 4
#> .fitted .lower .upper geometry
#> * <dbl> <dbl> <dbl> <POINT [m]>
#> 1 1.62 -6.45 9.68 (-1771413 1752976)
#> 2 24.4 16.8 31.9 (1018112 1867127)
#> 3 8.95 1.03 16.9 (-291256.8 1553212)
#> 4 16.5 8.64 24.3 (1274293 1267835)
#> 5 4.93 -2.78 12.6 (-547437.6 1638825)
#> 6 26.8 19.3 34.3 (1445080 1981278)
#> 7 2.87 -5.05 10.8 (-1629090 3037173)
#> 8 14.3 6.77 21.7 (1302757 1039534)
#> 9 1.53 -6.33 9.38 (-1429838 2523494)
#> 10 14.3 6.61 21.9 (1131970 1096609)
#> # ℹ 90 more rows
# \donttest{
sulfate$var <- rnorm(NROW(sulfate)) # add noise variable
sulfate_preds$var <- rnorm(NROW(sulfate_preds)) # add noise variable
sprfmod <- splmRF(sulfate ~ var, data = sulfate, spcov_type = "exponential")
predict(sprfmod, sulfate_preds)
#> 1 2 3 4 5 6
#> 7.28544220 24.06196502 6.73831801 13.76553348 6.53309799 37.20423705
#> 7 8 9 10 11 12
#> 15.19754595 17.48183865 -0.48782804 12.61161059 -2.22200207 14.44480717
#> 13 14 15 16 17 18
#> 3.60624975 24.93869340 15.20192026 4.41391967 16.94469576 14.69172064
#> 19 20 21 22 23 24
#> 1.29248434 29.81383434 -6.18222761 6.75563225 -1.26360347 -0.39738355
#> 25 26 27 28 29 30
#> -3.80423214 5.26541767 15.73272540 7.49388632 12.47643188 8.03754998
#> 31 32 33 34 35 36
#> 1.43039988 -2.65579891 12.27719120 8.02234807 0.01512766 6.22199813
#> 37 38 39 40 41 42
#> 24.80218497 3.22244604 19.55763100 17.39116832 5.28875619 32.30030088
#> 43 44 45 46 47 48
#> 24.67412336 28.61053009 -2.25206358 2.42882956 14.16607487 14.40646529
#> 49 50 51 52 53 54
#> 33.90884556 8.09508007 27.59977195 12.11886530 8.02776461 3.04983231
#> 55 56 57 58 59 60
#> 11.97175662 13.64501730 16.85985951 -1.94381670 16.66081794 7.68182931
#> 61 62 63 64 65 66
#> 18.27721205 27.23727467 -7.05328205 22.84702795 36.46374415 3.68445317
#> 67 68 69 70 71 72
#> 1.30501898 9.17008976 -2.44595556 4.56106464 6.14652954 15.52342825
#> 73 74 75 76 77 78
#> 3.65812206 1.65009844 -2.31725434 -0.74632805 21.25936670 -0.59949167
#> 79 80 81 82 83 84
#> 19.91256105 18.88256333 9.46607904 3.03549162 16.80016658 9.20818876
#> 85 86 87 88 89 90
#> 9.15457910 20.20627290 1.13673263 -8.09381899 1.84304845 21.93241759
#> 91 92 93 94 95 96
#> -4.98701784 7.86957851 18.89053085 30.39253193 15.52633597 13.62202773
#> 97 98 99 100
#> 22.32962467 14.46414096 10.22414853 20.77387488
# }