# VariableSelection Vignette

# 1 Performing variable selection

- Forward selection, backward elimination, 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 methods

- Forward selection and backward elimination are both stepwise variable selection methods.
- 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.
- These methods will only return 1 best model.
- Parallel computation can be used for the methods, but is generally only necessary for large datasets.

### 1.2.1 Forward selection example

    # 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 best value of AIC obtained was 142.2
    #> Number of models fit: 27
    #> Variables that were kept in each model:  (Intercept)
    #> Order the variables were added to the model:
    #>
    #> 1). wt
    #> 2). hp

    ## Getting final model
    fit(forwardVS, which = 1)
    #> Results from gamma regression with inverse link function
    #> Using the formula mpg ~ hp + wt
    #>
    #>              Estimate SE  t p.values
    #> (Intercept) 8.923e-03 NA NA       NA
    #> hp          8.887e-05 NA NA       NA
    #> wt          9.826e-03 NA NA       NA
    #>
    #> Dispersion parameter taken to be 0.0104
    #> 32 observations used to fit model
    #> (0 observations removed due to missingness)
    #>
    #> Residual Deviance: 0.33 on 29 degrees of freedom
    #> AIC: 142.2
    #> Algorithm converged in 3 iterations using Fisher's scoring

### 1.2.2 Backward elimination example

    # Backward elimination with mtcars
    backwardVS <- VariableSelection(GammaFit, type = "backward")
    backwardVS
    #> Variable Selection Info:
    #> ------------------------
    #> Variables were selected using backward elimination with AIC
    #> The best value of AIC obtained was 141.9
    #> Number of models fit: 49
    #> 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 model
    fit(backwardVS, which = 1)
    #> Results from gamma regression with inverse link function
    #> Using the formula mpg ~ hp + wt + qsec + gear
    #>
    #>               Estimate SE  t p.values
    #> (Intercept)  4.691e-02 NA NA       NA
    #> hp           6.284e-05 NA NA       NA
    #> wt           9.485e-03 NA NA       NA
    #> qsec        -1.299e-03 NA NA       NA
    #> gear        -2.662e-03 NA NA       NA
    #>
    #> Dispersion parameter taken to be 0.0091
    #> 32 observations used to fit model
    #> (0 observations removed due to missingness)
    #>
    #> Residual Deviance: 0.29 on 27 degrees of freedom
    #> AIC: 141.9
    #> Algorithm converged in 3 iterations using Fisher's scoring

## 1.3 Branch and bound

- The branch and bound methods can be much slower than the stepwise methods, but they are guaranteed to find the best models.
- The branch and bound methods 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 methods 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
    #> The best value of AIC obtained was 141.9
    #> Number of models fit: 63
    #> Variables that were kept in each model:  (Intercept)

    ## Getting final model
    fit(VS, which = 1)
    #> Results from gamma regression with inverse link function
    #> Using the formula mpg ~ hp + wt + qsec + gear
    #>
    #>               Estimate SE  t p.values
    #> (Intercept)  4.691e-02 NA NA       NA
    #> hp           6.284e-05 NA NA       NA
    #> wt           9.485e-03 NA NA       NA
    #> qsec        -1.299e-03 NA NA       NA
    #> gear        -2.662e-03 NA NA       NA
    #>
    #> Dispersion parameter taken to be 0.0091
    #> 32 observations used to fit model
    #> (0 observations removed due to missingness)
    #>
    #> Residual Deviance: 0.29 on 27 degrees of freedom
    #> AIC: 141.9
    #> Algorithm converged in 3 iterations using Fisher's scoring

- 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
    #> The best value of AIC obtained was 141.9
    #> Number of models fit: 63
    #> Variables that were kept in each model:  (Intercept)

    ## Getting final model
    fit(formulaVS, which = 1)
    #> Results from gamma regression with inverse link function
    #> Using the formula mpg ~ hp + wt + qsec + gear
    #>
    #>               Estimate SE  t p.values
    #> (Intercept)  4.691e-02 NA NA       NA
    #> hp           6.284e-05 NA NA       NA
    #> wt           9.485e-03 NA NA       NA
    #> qsec        -1.299e-03 NA NA       NA
    #> gear        -2.662e-03 NA NA       NA
    #>
    #> Dispersion parameter taken to be 0.0091
    #> 32 observations used to fit model
    #> (0 observations removed due to missingness)
    #>
    #> Residual Deviance: 0.29 on 27 degrees of freedom
    #> AIC: 141.9
    #> Algorithm converged in 3 iterations using Fisher's scoring

### 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
    #> Found the top 10 models
    #> The range of AIC values for the top 10 models is (141.9, 143.59)
    #> Number of models fit: 122
    #> Variables that were kept in each model:  (Intercept)

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

    ## Getting best model
    fit(formulaVS, which = 1)
    #> Results from gamma regression with inverse link function
    #> Using the formula mpg ~ hp + wt + qsec + gear
    #>
    #>               Estimate SE  t p.values
    #> (Intercept)  4.691e-02 NA NA       NA
    #> hp           6.284e-05 NA NA       NA
    #> wt           9.485e-03 NA NA       NA
    #> qsec        -1.299e-03 NA NA       NA
    #> gear        -2.662e-03 NA NA       NA
    #>
    #> Dispersion parameter taken to be 0.0091
    #> 32 observations used to fit model
    #> (0 observations removed due to missingness)
    #>
    #> Residual Deviance: 0.29 on 27 degrees of freedom
    #> AIC: 141.9
    #> Algorithm converged in 3 iterations using Fisher's scoring

### 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 the top 16 models
    #> The range of AIC values for the top 16 models is (141.9, 143.9)
    #> Number of models fit: 116
    #> 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
    #> Found the top 10 models
    #> The range of AIC values for the top 10 models is (143.17, 145.24)
    #> Number of models fit: 55
    #> Variables that were kept in each model:  (Intercept), hp, cyl

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

    ## Getting final model
    fit(keepVS, which = 1)
    #> Results from gamma regression with inverse link function
    #> Using the formula mpg ~ cyl + hp + wt + qsec + gear
    #>
    #>               Estimate SE  t p.values
    #> (Intercept)  6.464e-02 NA NA       NA
    #> cyl         -1.412e-03 NA NA       NA
    #> hp           7.523e-05 NA NA       NA
    #> wt           1.037e-02 NA NA       NA
    #> qsec        -1.816e-03 NA NA       NA
    #> gear        -3.861e-03 NA NA       NA
    #>
    #> Dispersion parameter taken to be 0.0089
    #> 32 observations used to fit model
    #> (0 observations removed due to missingness)
    #>
    #> Residual Deviance: 0.29 on 26 degrees of freedom
    #> AIC: 143.17
    #> Algorithm converged in 3 iterations using Fisher's scoring

## 1.5 Convergence issues

- It is not recommended to use branch and bound if the upper models do not converge since it can make the algorithm very slow.
- Sometimes when using backwards selection 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.