Greybox main vignette
Ivan Svetunkov
2023-09-15
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: 0.27 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
#> 17.6668913 3.3703773 1.3725674 4.6768101 1.5415708 2.3209845
#> xLag6 xLead9 xLag7 xLag8
#> 1.7074084 0.3765307 1.4027852 1.3371727
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)
|
17.67*
|
|
[16.07; 19.26]
|
xLag4
|
3.37*
|
|
[ 2.75; 3.99]
|
xLag9
|
1.37*
|
|
[ 0.75; 1.99]
|
xLag3
|
4.68*
|
|
[ 4.10; 5.25]
|
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*
|
|
[ 0.76; 2.05]
|
xLag8
|
1.34*
|
|
[ 0.69; 1.98]
|
Num. obs.
|
150.00
|
Num. param.
|
11.00
|
Num. df
|
139.00
|
AIC
|
416.45
|
AICc
|
418.36
|
BIC
|
449.57
|
BICc
|
454.36
|
* 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) 20.9213 0.2326 1.0000 20.4616 21.3811 *
#> x -0.0432 0.0286 0.2591 -0.0998 0.0134
#> xLag5 6.3977 0.0839 1.0000 6.2318 6.5636 *
#> xLag4 5.8466 0.0899 1.0000 5.6688 6.0244 *
#> xLag3 5.6854 0.0901 1.0000 5.5074 5.8635 *
#> xLag2 0.1251 0.0382 0.2876 0.0496 0.2006 *
#> xLag1 -0.0844 0.0342 0.2717 -0.1521 -0.0167 *
#> xLead1 -0.0908 0.0324 0.2782 -0.1547 -0.0268 *
#> xLead2 -0.0356 0.0257 0.2600 -0.0865 0.0152
#> xLead3 -0.1200 0.0343 0.2971 -0.1877 -0.0523 *
#> xLead4 -0.0068 0.0229 0.2585 -0.0520 0.0384
#> xLead5 0.1159 0.0300 0.3030 0.0566 0.1751 *
#>
#> Error standard deviation: 2.2075
#> Sample size: 150
#> Number of estimated parameters: 7.2151
#> Number of degrees of freedom: 142.7849
#> Approximate combined information criteria:
#> AIC AICc BIC BICc
#> 670.6603 671.4964 692.3824 694.4770
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) 17.6924 0.7760 1.0000 16.1580 19.2268 *
#> xLag4 3.3747 0.3020 1.0000 2.7775 3.9719 *
#> xLag9 1.3711 0.3029 0.9998 0.7723 1.9699 *
#> xLag3 4.6846 0.2809 1.0000 4.1292 5.2400 *
#> xLag10 1.5424 0.2749 1.0000 0.9989 2.0859 *
#> xLag5 2.3222 0.3118 1.0000 1.7058 2.9386 *
#> xLag6 1.7075 0.3145 1.0000 1.0858 2.3293 *
#> xLead9 0.3638 0.1247 0.9663 0.1174 0.6103 *
#> xLag7 1.4017 0.3152 0.9997 0.7786 2.0249 *
#> xLag8 1.3363 0.3132 0.9994 0.7170 1.9556 *
#>
#> Error standard deviation: 0.936
#> Sample size: 150
#> Number of estimated parameters: 10.9652
#> Number of degrees of freedom: 139.0348
#> Approximate combined information criteria:
#> AIC AICc BIC BICc
#> 416.7030 418.6040 449.7152 454.4777
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) 19.4134 0.8558 1.0000 17.7189 21.1079 *
#> xLag4 3.3480 0.2966 1.0000 2.7607 3.9353 *
#> xLag9 1.3340 0.2983 0.9990 0.7434 1.9247 *
#> xLag3 4.7559 0.2787 1.0000 4.2040 5.3078 *
#> xLag10 1.5364 0.2701 1.0000 1.0016 2.0713 *
#> xLag5 2.3209 0.3063 1.0000 1.7144 2.9274 *
#> xLag6 1.6611 0.3090 1.0000 1.0493 2.2730 *
#> xLead9 0.2948 0.1260 0.8920 0.0454 0.5443 *
#> xLag8 1.3692 0.3084 0.9989 0.7585 1.9799 *
#> xLag7 1.3274 0.3093 0.9982 0.7150 1.9397 *
#>
#> Error standard deviation: 0.9541
#> Sample size: 130
#> Number of estimated parameters: 10.8881
#> Number of degrees of freedom: 119.1119
#> Approximate combined information criteria:
#> AIC AICc BIC BICc
#> 367.7981 369.9900 399.0203 404.3546
plot(ourModel)
![](data:image/png;base64,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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.