1 Performing variable selection

Stepwise variable selection, and branch and bound selection can be done using VariableSelection().
VariableSelection() can accept either a BranchGLM object or a formula along with the data and the desired family and link to perform the variable selection.
Available metrics are AIC, BIC and HQIC, which are used to compare models and to select the best models.
VariableSelection() returns some information about the search, more detailed information about the best models can be seen by using the summary() function.
Note that VariableSelection() will properly handle interaction terms and categorical variables.
keep can also be specified if any set of variables are desired to be kept in every model.

1.1 Metrics

The 3 different metrics available for comparing models are the following
- Akaike information criterion (AIC), which typically results in models that are useful for prediction
  - \(AIC = -2logLik + 2 \times p\)
- Bayesian information criterion (BIC), which results in models that are more parsimonious than those selected by AIC
  - \(BIC = -2logLik + \log{(n)} \times p\)
- Hannan-Quinn information criterion (HQIC), which is in the middle of AIC and BIC
  - \(HQIC = -2logLik + 2 * \log({\log{(n)})} \times p\)

1.2 Stepwise algorithms

Forward selection, backward elimination, fast backward elimination, double backward elimination, and fast double backward elimination are all stepwise variable selection algorithms.
They are not guaranteed to find the best model or even a good model, but they are very fast.
Forward selection is recommended if the number of variables is greater than the number of observations or if many of the larger models don’t converge.
Parallel computation can be used for these algorithms, but is generally only necessary for large datasets.
The plot function can be used to see the path taken by the stepwise algorithms

1.2.1 Forward selection

# Loading BranchGLM package
library(BranchGLM)

# Fitting gamma regression model
cars <- mtcars

# Fitting gamma regression with inverse link
GammaFit <- BranchGLM(mpg ~ ., data = cars, family = "gamma", link = "inverse")

# Forward selection with mtcars
forwardVS <- VariableSelection(GammaFit, type = "forward")
forwardVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using forward selection with AIC
#> The top model found had AIC = 142.2
#> Number of models fit: 28
#> Variables that were kept in each model:  (Intercept)
#> Order the variables were added to the model:
#> 
#> 1). wt
#> 2). hp

## Getting final coefficients
coef(forwardVS, which = 1)
#>                   Model1
#> (Intercept) 8.922600e-03
#> cyl         0.000000e+00
#> disp        0.000000e+00
#> hp          8.887336e-05
#> drat        0.000000e+00
#> wt          9.826436e-03
#> qsec        0.000000e+00
#> vs          0.000000e+00
#> am          0.000000e+00
#> gear        0.000000e+00
#> carb        0.000000e+00

## Plotting path
plot(forwardVS)

1.2.2 Backward elimination

1.2.2.1 Traditional variant

# Backward elimination with mtcars
backwardVS <- VariableSelection(GammaFit, type = "backward")
backwardVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using backward elimination with AIC
#> The top model found had AIC = 141.9
#> Number of models fit: 50
#> Variables that were kept in each model:  (Intercept)
#> Order the variables were removed from the model:
#> 
#> 1). vs
#> 2). drat
#> 3). am
#> 4). disp
#> 5). carb
#> 6). cyl

## Getting final coefficients
coef(backwardVS, which = 1)
#>                    Model1
#> (Intercept)  4.691154e-02
#> cyl          0.000000e+00
#> disp         0.000000e+00
#> hp           6.284129e-05
#> drat         0.000000e+00
#> wt           9.484597e-03
#> qsec        -1.298959e-03
#> vs           0.000000e+00
#> am           0.000000e+00
#> gear        -2.662008e-03
#> carb         0.000000e+00

## Plotting path
plot(backwardVS)

1.2.2.2 Fast variant

Fast backward elimination is equivalent to traditional backward elimination, except much faster. The results from the two algorithms may differ if many of the larger models in the candidate set of models are difficult to fit numerically.

# Fast backward elimination with mtcars
fastbackwardVS <- VariableSelection(GammaFit, type = "fast backward")
fastbackwardVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using fast backward elimination with AIC
#> The top model found had AIC = 141.9
#> Number of models fit: 21
#> Variables that were kept in each model:  (Intercept)
#> Order the variables were removed from the model:
#> 
#> 1). vs
#> 2). drat
#> 3). am
#> 4). disp
#> 5). carb
#> 6). cyl

## Getting final coefficients
coef(fastbackwardVS, which = 1)
#>                    Model1
#> (Intercept)  4.691154e-02
#> cyl          0.000000e+00
#> disp         0.000000e+00
#> hp           6.284129e-05
#> drat         0.000000e+00
#> wt           9.484597e-03
#> qsec        -1.298959e-03
#> vs           0.000000e+00
#> am           0.000000e+00
#> gear        -2.662008e-03
#> carb         0.000000e+00

## Plotting path
plot(fastbackwardVS)

We got the same model that we got from traditional backward elimination, but we only had to fit 21 models while we had to fit 50 models using traditional backward elimination.

1.2.3 Double backward elimination

One of the double backward elimination variants could also be used to (potentially) find higher quality models than what is found by traditional backward elimination.

# Fast double backward elimination with mtcars
fastdoublebackwardVS <- VariableSelection(GammaFit, type = "fast double backward")
fastdoublebackwardVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using fast double backward elimination with AIC
#> The top model found had AIC = 141.9
#> Number of models fit: 22
#> Variables that were kept in each model:  (Intercept)
#> Order the variables were removed from the model:
#> 
#> 1). drat and vs
#> 2). disp and am
#> 3). cyl and carb

## Getting final coefficients
coef(fastdoublebackwardVS, which = 1)
#>                    Model1
#> (Intercept)  4.691154e-02
#> cyl          0.000000e+00
#> disp         0.000000e+00
#> hp           6.284129e-05
#> drat         0.000000e+00
#> wt           9.484597e-03
#> qsec        -1.298959e-03
#> vs           0.000000e+00
#> am           0.000000e+00
#> gear        -2.662008e-03
#> carb         0.000000e+00

## Plotting path
plot(fastdoublebackwardVS)

However, in this case they get the same final model and the double backward elimination algorithm fits slightly more models.

1.3 Branch and bound

The branch and bound algorithms can be much slower than the stepwise algorithms, but they are guaranteed to find the best models.
The branch and bound algorithms are typically much faster than an exhaustive search and can also be made even faster if parallel computation is used.

1.3.1 Branch and bound example

If showprogress is true, then progress of the branch and bound algorithm will be reported occasionally.
Parallel computation can be used with these algorithms and can lead to very large speedups.

# Branch and bound with mtcars
VS <- VariableSelection(GammaFit, type = "branch and bound", showprogress = FALSE)
VS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using branch and bound selection with AIC
#> Found 1 model within 0 AIC of the best AIC(141.9)
#> Number of models fit: 50
#> Variables that were kept in each model:  (Intercept)

## Getting final coefficients
coef(VS, which = 1)
#>                    Model1
#> (Intercept)  4.691154e-02
#> cyl          0.000000e+00
#> disp         0.000000e+00
#> hp           6.284129e-05
#> drat         0.000000e+00
#> wt           9.484597e-03
#> qsec        -1.298959e-03
#> vs           0.000000e+00
#> am           0.000000e+00
#> gear        -2.662008e-03
#> carb         0.000000e+00

A formula with the data and the necessary BranchGLM fitting information can also be used instead of supplying a BranchGLM object.

# Can also use a formula and data
formulaVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma", 
                               link = "inverse", type = "branch and bound",
                               showprogress = FALSE, metric = "AIC")
formulaVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using branch and bound selection with AIC
#> Found 1 model within 0 AIC of the best AIC(141.9)
#> Number of models fit: 50
#> Variables that were kept in each model:  (Intercept)

## Getting final coefficients
coef(formulaVS, which = 1)
#>                    Model1
#> (Intercept)  4.691154e-02
#> cyl          0.000000e+00
#> disp         0.000000e+00
#> hp           6.284129e-05
#> drat         0.000000e+00
#> wt           9.484597e-03
#> qsec        -1.298959e-03
#> vs           0.000000e+00
#> am           0.000000e+00
#> gear        -2.662008e-03
#> carb         0.000000e+00

1.3.2 Using bestmodels

The bestmodels argument can be used to find the top k models according to the metric.

# Finding top 10 models
formulaVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma", 
                               link = "inverse", type = "branch and bound",
                               showprogress = FALSE, metric = "AIC", 
                               bestmodels = 10)
formulaVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using branch and bound selection with AIC
#> The range of AIC values for the top 10 models is (141.9, 143.59)
#> Number of models fit: 98
#> Variables that were kept in each model:  (Intercept)

## Plotting results
plot(formulaVS, type = "b")


## Getting all coefficients
coef(formulaVS, which = "all")
#>                    Model1       Model2        Model3        Model4
#> (Intercept)  4.691154e-02 8.922600e-03  2.896032e-02  1.972585e-02
#> cyl          0.000000e+00 0.000000e+00  0.000000e+00  0.000000e+00
#> disp         0.000000e+00 0.000000e+00  0.000000e+00  0.000000e+00
#> hp           6.284129e-05 8.887336e-05  5.590216e-05  9.987066e-05
#> drat         0.000000e+00 0.000000e+00  0.000000e+00  0.000000e+00
#> wt           9.484597e-03 9.826436e-03  1.105126e-02  8.373894e-03
#> qsec        -1.298959e-03 0.000000e+00 -1.071038e-03  0.000000e+00
#> vs           0.000000e+00 0.000000e+00  0.000000e+00  0.000000e+00
#> am           0.000000e+00 0.000000e+00  0.000000e+00  0.000000e+00
#> gear        -2.662008e-03 0.000000e+00  0.000000e+00 -2.092484e-03
#> carb         0.000000e+00 0.000000e+00  0.000000e+00  0.000000e+00
#>                    Model5       Model6        Model7       Model8        Model9
#> (Intercept)  6.463732e-02 7.472237e-03  4.763986e-02  0.048676830  2.578977e-02
#> cyl         -1.412185e-03 1.087312e-03  0.000000e+00  0.000000000  0.000000e+00
#> disp         0.000000e+00 0.000000e+00  0.000000e+00  0.000000000  0.000000e+00
#> hp           7.523221e-05 7.196928e-05  5.802906e-05  0.000000000  8.564089e-05
#> drat         0.000000e+00 0.000000e+00  0.000000e+00  0.000000000  0.000000e+00
#> wt           1.037319e-02 8.958479e-03  8.858489e-03  0.013362765  7.595784e-03
#> qsec        -1.815606e-03 0.000000e+00 -1.147336e-03 -0.002136415  0.000000e+00
#> vs           0.000000e+00 0.000000e+00  0.000000e+00  0.000000000  0.000000e+00
#> am           0.000000e+00 0.000000e+00  0.000000e+00  0.000000000  0.000000e+00
#> gear        -3.860851e-03 0.000000e+00 -3.408010e-03  0.000000000 -3.358384e-03
#> carb         0.000000e+00 0.000000e+00  7.249656e-04  0.000000000  1.140652e-03
#>                   Model10
#> (Intercept)  4.213154e-02
#> cyl          0.000000e+00
#> disp         0.000000e+00
#> hp           6.607566e-05
#> drat         1.326266e-03
#> wt           9.761600e-03
#> qsec        -1.298089e-03
#> vs           0.000000e+00
#> am           0.000000e+00
#> gear        -3.029601e-03
#> carb         0.000000e+00

1.3.3 Using cutoff

The cutoff argument can be used to find all models that have a metric value that is within cutoff of the minimum metric value found.

# Finding all models with an AIC within 2 of the best model
formulaVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma", 
                               link = "inverse", type = "branch and bound",
                               showprogress = FALSE, metric = "AIC", 
                               cutoff = 2)
formulaVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using branch and bound selection with AIC
#> Found 16 models within 2 AIC of the best AIC(141.9)
#> Number of models fit: 93
#> Variables that were kept in each model:  (Intercept)

## Plotting results
plot(formulaVS, type = "b")

1.4 Using keep

Specifying variables via keep will ensure that those variables are kept through the selection process.

# Example of using keep
keepVS <- VariableSelection(mpg ~ . ,data = cars, family = "gamma", 
                               link = "inverse", type = "branch and bound",
                               keep = c("hp", "cyl"), metric = "AIC",
                               showprogress = FALSE, bestmodels = 10)
keepVS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using branch and bound selection with AIC
#> The range of AIC values for the top 10 models is (143.17, 145.24)
#> Number of models fit: 45
#> Variables that were kept in each model:  (Intercept), hp, cyl

## Getting summary and plotting results
plot(keepVS, type = "b")


## Getting coefficients for top 10 models
coef(keepVS, which = "all")
#>                    Model1       Model2        Model3       Model4        Model5
#> (Intercept)  6.463732e-02 7.472237e-03  0.0256340835  0.068289331  1.716490e-02
#> cyl         -1.412185e-03 1.087312e-03  0.0004708918 -0.001632036  5.216391e-04
#> disp         0.000000e+00 0.000000e+00  0.0000000000  0.000000000  0.000000e+00
#> hp           7.523221e-05 7.196928e-05  0.0000530224  0.000071603  8.984711e-05
#> drat         0.000000e+00 0.000000e+00  0.0000000000  0.000000000  0.000000e+00
#> wt           1.037319e-02 8.958479e-03  0.0105114095  0.009728921  8.209926e-03
#> qsec        -1.815606e-03 0.000000e+00 -0.0009269894 -0.001707464  0.000000e+00
#> vs           0.000000e+00 0.000000e+00  0.0000000000  0.000000000  0.000000e+00
#> am           0.000000e+00 0.000000e+00  0.0000000000  0.000000000  0.000000e+00
#> gear        -3.860851e-03 0.000000e+00  0.0000000000 -0.004973355 -1.732000e-03
#> carb         0.000000e+00 0.000000e+00  0.0000000000  0.000886622  0.000000e+00
#>                    Model6        Model7        Model8        Model9
#> (Intercept)  5.958530e-02  6.575356e-02  6.604896e-02  0.0648442871
#> cyl         -1.320709e-03 -1.277298e-03 -1.444133e-03 -0.0013699334
#> disp         0.000000e+00 -8.059797e-06  0.000000e+00  0.0000000000
#> hp           7.707172e-05  7.872558e-05  7.501653e-05  0.0000745678
#> drat         1.089999e-03  0.000000e+00  0.000000e+00  0.0000000000
#> wt           1.054618e-02  1.074571e-02  1.025472e-02  0.0104217585
#> qsec        -1.782401e-03 -1.870329e-03 -1.867512e-03 -0.0018571564
#> vs           0.000000e+00  0.000000e+00  0.000000e+00  0.0002736861
#> am           0.000000e+00  0.000000e+00 -4.739251e-04  0.0000000000
#> gear        -4.088866e-03 -4.086160e-03 -3.775475e-03 -0.0038351284
#> carb         0.000000e+00  0.000000e+00  0.000000e+00  0.0000000000
#>                   Model10
#> (Intercept)  0.0092037639
#> cyl          0.0008526030
#> disp         0.0000000000
#> hp           0.0000703095
#> drat         0.0000000000
#> wt           0.0090861507
#> qsec         0.0000000000
#> vs          -0.0010182533
#> am           0.0000000000
#> gear         0.0000000000
#> carb         0.0000000000

1.5 Categorical variables

Categorical variables are automatically grouped together, if this behavior is not desired, then the indicator variables for that categorical variable should be created before using VariableSelection()
First we show an example of the default behavior of the function with a categorical variable. In this example the categorical variable of interest is Species.

# Variable selection with grouped beta parameters for species
Data <- iris
VS <- VariableSelection(Sepal.Length ~ ., data = Data, family = "gaussian", 
                           link = "identity", metric = "AIC", bestmodels = 10, 
                           showprogress = FALSE)
VS
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using switch branch and bound selection with AIC
#> The range of AIC values for the top 10 models is (79.12, 183.94)
#> Number of models fit: 16
#> Variables that were kept in each model:  (Intercept)

## Plotting results
plot(VS, cex.names = 0.75, type = "b")

Next we show an example where the beta parameters for each level for Species are handled separately

# Treating categorical variable beta parameters separately
## This function automatically groups together parameters from a categorical variable
## to avoid this, you need to create the indicator variables yourself
x <- model.matrix(Sepal.Length ~ ., data = iris)
Sepal.Length <- iris$Sepal.Length
Data <- cbind.data.frame(Sepal.Length, x[, -1])
VSCat <- VariableSelection(Sepal.Length ~ ., data = Data, family = "gaussian", 
                           link = "identity", metric = "AIC", bestmodels = 10, 
                           showprogress = FALSE)
VSCat
#> Variable Selection Info:
#> ------------------------
#> Variables were selected using switch branch and bound selection with AIC
#> The range of AIC values for the top 10 models is (79.12, 108.23)
#> Number of models fit: 21
#> Variables that were kept in each model:  (Intercept)

## Plotting results
plot(VSCat, cex.names = 0.75, type = "b")

1.6 Convergence issues

It is not recommended to use the branch and bound algorithms if many of the upper models do not converge since it can make the algorithms very slow.
Sometimes when using one of the backward elimination algorithms and all the upper models that are tested do not converge, no final model can be selected.
For these reasons, if there are convergence issues it is recommended to use forward selection.

VariableSelection Vignette