Greybox main vignette
Ivan Svetunkov
2024-08-27
There are three well-known notions of “boxes” in modelling: 1. White
box - the model that is completely transparent and does not have any
randomness. One can see how the inputs are transformed into the specific
outputs. 2. Black box - the model which does not have an apparent
structure. One can only observe inputs and outputs but does not know
what happens inside. 3. Grey box - the model that is in between the
first two. We observe inputs and outputs plus have some information
about the structure of the model, but there is still a part of
unknown.
The white boxes are usually used in optimisations (e.g. linear
programming), while black boxes are popular in machine learning. As for
the grey box models, they are more often used in analysis and
forecasting. So the package greybox
contains models that
are used for these purposes.
At the moment the package contains augmented linear model function
and several basic functions that implement model selection and
combinations using information criteria (IC). You won’t find statistical
tests in this package - there’s plenty of them in the other packages.
Here we try using the modern techniques and methods that do not rely on
hypothesis testing. This is the main philosophical point of
greybox
.
Main functions
The package includes the following functions for models
construction:
- alm() - Augmented Linear Model. This is
something similar to GLM, but with a focus on forecasting and the
information criteria usage for time series. It also supports mixture
distribution models for the intermittent data and allows adding trend to
the data via the formula.
- stepwise() - select the linear model with
the lowest IC from all the possible in the provided data. Uses partial
correlations. Works fast;
- lmCombine() - combine the linear models
into one using IC weights;
- lmDynamic() - produce model with dynamic
weights and time varying parameters based on point IC weight.
See discussion of some of these functions in this vignette below.
Models evaluation functions
- ro() - produce forecasts with a specified
function using rolling origin.
measures()
- function, returning a bunch of error
measures for the provided forecast and the holdout sample.
rmcb()
- regression on ranks of forecasting methods.
This is a fast alternative to the classical nemenyi / MCB test.
Methods
The following methods can be applied to the models, produced by
alm()
, stepwise()
, lmCombine()
and lmDynamic()
:
logLik()
- extracts log-likelihood.
AIC()
, AICc()
, BIC()
,
BICc()
- calculates the respective information
criteria.
pointLik()
- extracts the point likelihood.
pAIC()
, pAICc()
, pBIC()
,
pBICc()
- calculates the respective point information
criteria, based on pointLik.
actuals()
- extracts the actual values of the response
variable.
coefbootstrap()
- produces bootstrapped values of
parameters, taking nsim
samples of the size
size
from the data and reapplying the model.
coef()
, coefficients()
- extract the
parameters of the model.
confint()
- extracts the confidence intervals for the
parameters.
vcov()
- extracts the variance-covariance matrix of the
parameters.
sigma()
- extracts the standard deviation of the
residuals.
nobs()
- the number of the in-sample observations of
the model.
nparam()
- the number of all the estimated parameters
in the model.
nvariate()
- the number of variates (columns /
dimensions) of the resposne variable.
summary()
- produces the summary of the model.
predict()
- produces the predictions based on the model
and the provided newdata
. If the newdata
is
not provided, then it uses the already available data in the model. Can
also produce confidence
and prediction
intervals.
forecast()
- acts similarly to predict()
with few differences. It has a parameter h
- forecast
horizon - which is NULL
by default and is set to be equal
to the number of rows in newdata
. However, if the
newdata
is not provided, then it will produce forecasts of
the explanatory variables to the horizon h
and use them as
newdata
. Finally, if h
and
newdata
are provided, then the number of rows to use will
be regulated by h
.
plot()
- produces several plots for the analysis of the
residuals. This includes: Fitted over time, Standardised residuals vs
Fitted, Absolute residuals vs Fitted, Q-Q plot with the specified
distribution, Squared residuals vs Fitted, ACF of the residuals and PACF
of the residuals, which is regulated by which
parameter.
See documentation for more info: ?plot.greybox
.
detectdst()
and detectleap()
- methods
that return the ids of the hour / date for the DST / Leap year
change.
extract()
method, needed in order to produce printable
regression outputs using texreg()
function from the
texreg
package.
Distribution functions
qlaplace()
, dlaplace()
,
rlaplace()
, plaplace()
- functions for Laplace
distribution.
qalaplace()
, dalaplace()
,
ralaplace()
, palaplace()
- functions for
Asymmetric Laplace distribution.
qs()
, ds()
, rs()
,
ps()
- functions for S distribution.
qgnorm()
, dgnorm()
, rgnorm()
,
pgnorm()
- functions for the Generalised normal
distribution.
qfnorm()
, dfnorm()
, rfnorm()
,
pfnorm()
- functions for folded normal distribution.
qtplnorm()
, dtplnorm()
,
rtplnorm()
, ptplnorm()
- functions for three
parameter log normal distribution.
qbcnorm()
, dbcnorm()
,
rbcnorm()
, pbcnorm()
- functions for the
Box-Cox normal distribution.
qlogitnorm()
, dlogitnorm()
,
rlogitnorm()
, plogitnorm()
- functions for the
Logit-normal distribution.
Additional functions
graphmaker()
- produces linear plots for the variable,
its forecasts and fitted values.
xregExpander
The function xregExpander()
is useful in cases when the
exogenous variable may influence the response variable either via some
lags or leads. As an example, consider BJsales.lead
series
from the datasets
package. Let’s assume that the
BJsales
variable is driven by the today’s value of the
indicator, the value five and 10 days ago. This means that we need to
produce lags of BJsales.lead
. This can be done using
xregExpander()
:
BJxreg <- xregExpander(BJsales.lead,lags=c(-5,-10))
The BJxreg
is a matrix, which contains the original
data, the data with the lag 5 and the data with the lag 10. However, if
we just move the original data several observations ahead or backwards,
we will have missing values in the beginning / end of series, so
xregExpander()
fills in those values with the forecasts
using es()
and iss()
functions from
smooth
package (depending on the type of variable we are
dealing with). This also means that in cases of binary variables you may
have weird averaged values as forecasts (e.g. 0.7812), so beware and
look at the produced matrix. Maybe in your case it makes sense to just
substitute these weird numbers with zeroes…
You may also need leads instead of lags. This is regulated with the
same lags
parameter but with positive values:
BJxreg <- xregExpander(BJsales.lead,lags=c(7,-5,-10))
Once again, the values are shifted, and now the first 7 values are
backcasted. In order to simplify things we can produce all the values
from 10 lags till 10 leads, which returns the matrix with 21
variables:
BJxreg <- xregExpander(BJsales.lead,lags=c(-10:10))
stepwise
The function stepwise() does the selection based on an information
criterion (specified by user) and partial correlations. In order to run
this function the response variable needs to be in the first column of
the provided matrix. The idea of the function is simple, it works
iteratively the following way:
- The basic model of the first variable and the constant is
constructed (this corresponds to simple mean). An information criterion
is calculated;
- The correlations of the residuals of the model with all the original
exogenous variables are calculated;
- The regression model of the response variable and all the variables
in the previous model plus the new most correlated variable from (2) is
constructed using
lm()
function;
- An information criterion is calculated and is compared with the one
from the previous model. If it is greater or equal to the previous one,
then we stop and use the previous model. Otherwise we go to step 2.
This way we do not do a blind search, going forward or backwards, but
we follow some sort of “trace” of a good model: if the residuals contain
a significant part of variance that can be explained by one of the
exogenous variables, then that variable is included in the model.
Following partial correlations makes sure that we include only
meaningful (from technical point of view) variables in the model. In
general the function guarantees that you will have the model with the
lowest information criterion. However this does not guarantee that you
will end up with a meaningful model or with a model that produces the
most accurate forecasts. So analyse what you get as a result.
Let’s see how the function works with the Box-Jenkins data. First we
expand the data and form the matrix with all the variables:
BJxreg <- as.data.frame(xregExpander(BJsales.lead,lags=c(-10:10)))
BJxreg <- cbind(as.matrix(BJsales),BJxreg)
colnames(BJxreg)[1] <- "y"
ourModel <- stepwise(BJxreg)
This way we have a nice data frame with nice names, not something
weird with strange long names. It is important to note that the response
variable should be in the first column of the resulting matrix. After
that we use stepwise function:
ourModel <- stepwise(BJxreg)
And here’s what it returns (the object of class lm
):
ourModel
#> Time elapsed: 0.08 seconds
#> Call:
#> alm(formula = y ~ xLag4 + xLag9 + xLag3 + xLag10 + xLag5 + xLag6 +
#> xLead9 + xLag7 + xLag8, data = data, distribution = "dnorm")
#>
#> Coefficients:
#> (Intercept) xLag4 xLag9 xLag3 xLag10 xLag5
#> 17.6448055 3.3712175 1.3724166 4.6781051 1.5412071 2.3213097
#> xLag6 xLead9 xLag7 xLag8
#> 1.7075130 0.3766692 1.4024772 1.3370199
The values in the function are listed in the order of most correlated
with the response variable to the least correlated ones. The function
works very fast because it does not need to go through all the variables
and their combinations in the dataset.
All the basic methods can be used together with the final model
(e.g. predict()
, forecast()
,
summary()
etc).
Furthermore, the greybox
package implements
extract()
method from texreg
package for the
production of printable outputs from the regression, here is an
example:
texreg::htmlreg(ourModel)
Statistical models
|
Model 1
|
(Intercept)
|
17.64*
|
|
[16.05; 19.24]
|
xLag4
|
3.37*
|
|
[ 2.75; 3.99]
|
xLag9
|
1.37*
|
|
[ 0.75; 2.00]
|
xLag3
|
4.68*
|
|
[ 4.10; 5.26]
|
xLag10
|
1.54*
|
|
[ 0.98; 2.11]
|
xLag5
|
2.32*
|
|
[ 1.68; 2.96]
|
xLag6
|
1.71*
|
|
[ 1.06; 2.35]
|
xLead9
|
0.38*
|
|
[ 0.12; 0.63]
|
xLag7
|
1.40*
|
|
[ 0.75; 2.05]
|
xLag8
|
1.34*
|
|
[ 0.69; 1.98]
|
Num. obs.
|
150.00
|
Num. param.
|
11.00
|
Num. df
|
139.00
|
AIC
|
416.74
|
AICc
|
418.66
|
BIC
|
449.86
|
BICc
|
454.65
|
* 0 outside the confidence interval.
|
Similarly, you can produce pdf tables via texreg()
function from that package. Alternatively, you can use
kable()
function from knitr
package on the
summary to get a table for LaTeX / HTML.
lmCombine
lmCombine()
function creates a pool of linear models
using lm()
, writes down the parameters, standard errors and
information criteria and then combines the models using IC weights. The
resulting model is of the class “lm.combined”. The speed of the function
deteriorates exponentially with the increase of the number of variables
\(k\) in the dataset, because the
number of combined models is equal to \(2^k\). The advanced mechanism that uses
stepwise()
and removes a large chunk of redundant models is
also implemented in the function and can be switched using
bruteforce
parameter.
Here’s an example of the reduced data with combined model and the
parameter bruteforce=TRUE
:
ourModel <- lmCombine(BJxreg[,-c(3:7,18:22)],bruteforce=TRUE)
summary(ourModel)
#> The AICc combined model
#> Response variable: y
#> Distribution used in the estimation: Normal
#> Coefficients:
#> Estimate Std. Error Importance Lower 2.5% Upper 97.5%
#> (Intercept) 20.9029 0.2327 1.0000 20.4429 21.3629 *
#> x -0.0432 0.0286 0.2591 -0.0998 0.0134
#> xLag5 6.3973 0.0840 1.0000 6.2313 6.5633 *
#> xLag4 5.8467 0.0900 1.0000 5.6688 6.0245 *
#> xLag3 5.6857 0.0901 1.0000 5.5076 5.8638 *
#> xLag2 0.1251 0.0382 0.2876 0.0495 0.2006 *
#> xLag1 -0.0843 0.0342 0.2716 -0.1520 -0.0166 *
#> xLead1 -0.0906 0.0323 0.2780 -0.1545 -0.0267 *
#> xLead2 -0.0354 0.0257 0.2599 -0.0863 0.0154
#> xLead3 -0.1193 0.0342 0.2967 -0.1868 -0.0517 *
#> xLead4 -0.0067 0.0229 0.2585 -0.0520 0.0385
#> xLead5 0.1161 0.0300 0.3032 0.0568 0.1754 *
#>
#> Error standard deviation: 2.2077
#> Sample size: 150
#> Number of estimated parameters: 7.2146
#> Number of degrees of freedom: 142.7854
#> Approximate combined information criteria:
#> AIC AICc BIC BICc
#> 670.6810 671.5170 692.4015 694.4959
summary()
function provides the table with the
parameters, their standard errors, their relative importance and the 95%
confidence intervals. Relative importance indicates in how many cases
the variable was included in the model with high weight. So, in the
example above variables xLag5, xLag4, xLag3 were included in the models
with the highest weights, while all the others were in the models with
lower ones. This may indicate that only these variables are needed for
the purposes of analysis and forecasting.
The more realistic situation is when the number of variables is high.
In the following example we use the data with 21 variables. So if we use
brute force and estimate every model in the dataset, we will end up with
\(2^{21}\) = 2^21
combinations of models, which is not possible to estimate in the
adequate time. That is why we use bruteforce=FALSE
:
ourModel <- lmCombine(BJxreg,bruteforce=FALSE)
summary(ourModel)
#> The AICc combined model
#> Response variable: y
#> Distribution used in the estimation: Normal
#> Coefficients:
#> Estimate Std. Error Importance Lower 2.5% Upper 97.5%
#> (Intercept) 17.6704 0.7766 1.0000 16.1349 19.2060 *
#> xLag4 3.3755 0.3023 1.0000 2.7779 3.9732 *
#> xLag9 1.3709 0.3031 0.9998 0.7717 1.9702 *
#> xLag3 4.6859 0.2811 1.0000 4.1302 5.2417 *
#> xLag10 1.5420 0.2751 1.0000 0.9981 2.0859 *
#> xLag5 2.3225 0.3120 1.0000 1.7056 2.9394 *
#> xLag6 1.7076 0.3147 1.0000 1.0854 2.3299 *
#> xLead9 0.3639 0.1248 0.9661 0.1172 0.6106 *
#> xLag7 1.4014 0.3154 0.9997 0.7778 2.0250 *
#> xLag8 1.3362 0.3135 0.9994 0.7164 1.9559 *
#>
#> Error standard deviation: 0.9369
#> Sample size: 150
#> Number of estimated parameters: 10.965
#> Number of degrees of freedom: 139.035
#> Approximate combined information criteria:
#> AIC AICc BIC BICc
#> 416.9994 418.9003 450.0110 454.7733
In this case first, the stepwise()
function is used,
which finds the best model in the pool. Then each variable that is not
in the model is added to the model and then removed iteratively. IC,
parameters values and standard errors are all written down for each of
these expanded models. Finally, in a similar manner each variable is
removed from the optimal model and then added back. As a result the pool
of combined models becomes much smaller than it could be in case of the
brute force, but it contains only meaningful models, that are close to
the optimal. The rationale for this is that the marginal contribution of
variables deteriorates with the increase of the number of parameters in
case of the stepwise function, and the IC weights become close to each
other around the optimal model. So, whenever the models are combined,
there is a lot of redundant models with very low weights. By using the
mechanism described above we remove those redundant models.
There are several methods for the lm.combined
class,
including:
predict.greybox()
- returns the point and interval
predictions.
forecast.greybox()
- wrapper around
predict()
The forecast horizon is defined by the length of
the provided sample of newdata
.
plot.lm.combined()
- plots actuals and fitted
values.
plot.predict.greybox()
- which uses
graphmaker()
function from smooth
in order to
produce graphs of actuals and forecasts.
As an example, let’s split the whole sample with Box-Jenkins data
into in-sample and the holdout:
BJInsample <- BJxreg[1:130,];
BJHoldout <- BJxreg[-(1:130),];
ourModel <- lmCombine(BJInsample,bruteforce=FALSE)
A summary and a plot of the model:
summary(ourModel)
#> The AICc combined model
#> Response variable: y
#> Distribution used in the estimation: Normal
#> Coefficients:
#> Estimate Std. Error Importance Lower 2.5% Upper 97.5%
#> (Intercept) 19.3889 0.8562 1.0000 17.6936 21.0843 *
#> xLag4 3.3491 0.2967 1.0000 2.7617 3.9366 *
#> xLag9 1.3338 0.2984 0.9990 0.7430 1.9246 *
#> xLag3 4.7577 0.2788 1.0000 4.2057 5.3098 *
#> xLag10 1.5362 0.2702 1.0000 1.0013 2.0712 *
#> xLag5 2.3213 0.3064 1.0000 1.7146 2.9280 *
#> xLag6 1.6612 0.3091 1.0000 1.0492 2.2732 *
#> xLead9 0.2944 0.1261 0.8910 0.0447 0.5442 *
#> xLag8 1.3690 0.3085 0.9989 0.7582 1.9799 *
#> xLag7 1.3270 0.3094 0.9982 0.7145 1.9396 *
#>
#> Error standard deviation: 0.9554
#> Sample size: 130
#> Number of estimated parameters: 10.887
#> Number of degrees of freedom: 119.113
#> Approximate combined information criteria:
#> AIC AICc BIC BICc
#> 368.1614 370.3528 399.3803 404.7136
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)
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)
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)
Importance tells us how important the respective variable is in the
combination. 1 means 100% important, 0 means not important at all.
And the forecast using the holdout sample:
ourForecast <- predict(ourModel,BJHoldout)
plot(ourForecast)
![](data:image/png;base64,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)
These are the main functions implemented in the package for now. If
you want to read more about IC model selection and combinations, I would
recommend (Burnham and Anderson 2004)
textbook.
References
Burnham, Kenneth P, and David R Anderson. 2004.
Model Selection and Multimodel Inference.
Edited by Kenneth P Burnham and David R Anderson. Springer New York.
https://doi.org/10.1007/b97636.