predict
Predict indicator scores
Description
![[Maturing]](../help/figures/lifecycle-maturing.svg)
Usage
predict(
.object = NULL,
.benchmark = c("lm", "unit", "PLS-PM", "GSCA", "PCA", "MAXVAR"),
.cv_folds = 10,
.handle_inadmissibles = c("stop", "ignore", "set_NA"),
.r = 10,
.test_data = NULL
)Arguments
.object |
An R object of class cSEMResults resulting from a call to |
.benchmark |
Character string. The procedure to obtain benchmark predictions. One of "lm", "unit", "PLS-PM", "GSCA", "PCA", or "MAXVAR". Default to "lm". |
.cv_folds |
Integer. The number of cross-validation folds to use. Setting
|
.handle_inadmissibles |
Character string. How should inadmissible results
be treated? One of "stop", "ignore", or "set_NA". If "stop", |
.r |
Integer. The number of repetitions to use. Defaults to |
.test_data |
A matrix of test data with the same column names as the training data. |
Details
Predict the indicator scores of endogenous constructs.
Predict uses the procedure introduced by Shmueli et al. (2016) in the context of
PLS (commonly called: "PLSPredict" (Shmueli et al. 2019)).
Predict uses k-fold cross-validation to randomly
split the data into training and test data and subsequently predicts the
relevant values in the test data based on the model parameter estimates obtained
using the training data. The number of cross-validation folds is 10 by default but
may be changed using the .cv_folds argument.
By default, the procedure is repeated .r = 10 times to avoid irregularities
due to a particular split. See Shmueli et al. (2019) for
details.
Alternatively, users may supply a matrix or a data frame of .test_data with
the same column names as those in the data used to obtain .object (the training data).
In this case, arguments .cv_folds and .r are
ignored and predict uses the estimated coefficients from .object to
predict the values in the columns of .test_data.
In Shmueli et al. (2016) PLS-based predictions for indicator i
are compared to the predictions based on a multiple regression of indicator i
on all available exogenous indicators (.benchmark = "lm") and
a simple mean-based prediction summarized in the Q2_predict metric.
predict() is more general in that is allows users to compare the predictions
based on a so-called target model/specification to predictions based on an
alternative benchmark. Available benchmarks include predictions
based on a linear model, PLS-PM weights, unit weights (i.e. sum scores),
GSCA weights, PCA weights, and MAXVAR weights.
Each estimation run is checked for admissibility using verify(). If the
estimation yields inadmissible results, predict() stops with an error ("stop").
Users may choose to "ignore" inadmissible results or to simply set predictions
to NA ("set_NA") for the particular run that failed.
Value
An object of class cSEMPredict with print and plot methods.
Technically, cSEMPredict is a
named list containing the following list elements:
$Actual-
A matrix of the actual values/indicator scores of the endogenous constructs.
$Prediction_target-
A matrix of the predicted indicator scores of the endogenous constructs based on the target model. Target refers to procedure used to estimate the parameters in
.object. $Residuals_target-
A matrix of the residual indicator scores of the endogenous constructs based on the target model.
$Residuals_benchmark-
A matrix of the residual indicator scores of the endogenous constructs based on a model estimated by the procedure given to
.benchmark. $Prediction_metrics-
A data frame containing the predictions metrics MAE, RMSE, and Q2_predict.
$Information-
A list with elements
Target,Benchmark,Number_of_observations_training,Number_of_observations_test,Number_of_folds,Number_of_repetitions, andHandle_inadmissibles.
References
Shmueli G, Ray S, Estrada JMV, Chatla SB (2016).
“The Elephant in the Room: Predictive Performance of PLS Models.”
Journal of Business Research, 69(10), 4552–4564.
doi: 10.1016/j.jbusres.2016.03.049, https://doi.org/10.1016/j.jbusres.2016.03.049.
Shmueli G, Sarstedt M, Hair JF, Cheah J, Ting H, Vaithilingam S, Ringle CM (2019).
“Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict.”
European Journal of Marketing, 53(11), 2322–2347.
doi: 10.1108/ejm-02-2019-0189, https://doi.org/10.1108/ejm-02-2019-0189.
See Also
Examples
### Anime example taken from https://github.com/ISS-Analytics/pls-predict
# Load data
data(Anime) # data is similar to the Anime.csv found on
# https://github.com/ISS-Analytics/pls-predict but with irrelevant
# columns removed
# Split into training and data the same way as it is done on
# https://github.com/ISS-Analytics/pls-predict
set.seed(123)
index <- sample.int(dim(Anime)[1], 83, replace = FALSE)
dat_train <- Anime[-index, ]
dat_test <- Anime[index, ]
# Specify model
model <- "
# Structural model
ApproachAvoidance ~ PerceivedVisualComplexity + Arousal
# Measurement/composite model
ApproachAvoidance =~ AA0 + AA1 + AA2 + AA3
PerceivedVisualComplexity <~ VX0 + VX1 + VX2 + VX3 + VX4
Arousal <~ Aro1 + Aro2 + Aro3 + Aro4
"
# Estimate (replicating the results of the `simplePLS()` function)
res <- csem(dat_train,
model,
.disattenuate = FALSE, # original PLS
.iter_max = 300,
.tolerance = 1e-07,
.PLS_weight_scheme_inner = "factorial"
)
# Predict using a user-supplied training data set
pp <- predict(res, .test_data = dat_test)
pp$Predictions_target[1:6, ]
pp
### Compute prediction metrics ------------------------------------------------
res2 <- csem(Anime, # whole data set
model,
.disattenuate = FALSE, # original PLS
.iter_max = 300,
.tolerance = 1e-07,
.PLS_weight_scheme_inner = "factorial"
)
# Predict using 10-fold cross-validation with 5 repetitions
## Not run:
pp2 <- predict(res, .benchmark = "lm")
pp2
## There is a plot method available
plot(pp2)
## End(Not run)