
Compare Regression Models with Likelihood Ratio Test, AIC, and BIC
regCompTest.RdThis function compares a given regression model to a base model using the Likelihood Ratio (LR) test, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC).
Usage
regCompTest(
model,
data = NULL,
basemodel = "Poisson",
variables = FALSE,
print = FALSE,
...
)Arguments
- model
A fitted regression model object.
- data
An options data frame containing the variables in the model. If not supplied, the original data used to estimate the model will be used.
- basemodel
A character string specifying the family of base model to compare against (options include the family from
countregor "Poisson"). Default is "Poisson".- variables
Logical. If
TRUE, the base model will include the same variables as the provided model. IfFALSE, the base model will be an intercept-only model. Default isFALSE.Logical. If
TRUE, a table of the results will be shown. IfFALSE, the table of results will not be printed to the console.- ...
Additional arguments to be passed to the base model fitting function - options are any argument from the
countregfunction.
Value
A list containing the following components:
- LL
Log-likelihood of the provided model.
- LLbase
Log-likelihood of the base model.
- LR
Likelihood Ratio statistic.
- LRdof
Degrees of freedom for the Likelihood Ratio test.
- AIC
Akaike Information Criterion for the provided model.
- AICbase
Akaike Information Criterion for the base model.
- BIC
Bayesian Information Criterion for the provided model.
- BICbase
Bayesian Information Criterion for the base model.
- LR_pvalue
P-value for the Likelihood Ratio test.
- PseudoR2
McFadden's Pseudo R^2.
- statistics
A tibble format summary of the results.
- gtTable
A gt table object summarizing the results.
- latexTable
Latex code for a table summarizing the results.
- htmlTable
HTML table summarizing the results.
Details
The function performs the following steps:
Fits the base model, either a Poisson regression or another specified model.
Computes the log-likelihoods of both the provided model and the base model.
Calculates the AIC and BIC for both models.
Conducts a Likelihood Ratio test to compare the models (if the provided model has more parameters than the base model).
Computes McFadden's Pseudo R^2.
The Likelihood-Ratio test is computed as $$LR = -2 (LL_{base \ model}-LL_{model})$$. The test is chi-squared with degrees of freedom $$dof=N_{model \ params}-N_{base \ mode \ params}$$. The AIC is calculated as $$AIC = -2 \cdot LL + 2 \cdot nparam$$, and the BIC is calculated as $$BIC = -2 \cdot LL + nparam \cdot \log(n)$$.
Examples
# Comparing the NBP model with the NB2 model
data("washington_roads")
washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
nbp.base <- countreg(Total_crashes ~ lnaadt + lnlength + speed50 +
ShouldWidth04 + AADTover10k,
data=washington_roads, family = 'NBP', method = 'NM',
max.iters=3000)
regCompTest(nbp.base, washington_roads, basemodel="NB2", print=TRUE)
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#> <tr><td headers="Statistic" class="gt_row gt_left">AIC</td>
#> <td headers="Model" class="gt_row gt_right">2,140.5327</td>
#> <td headers="BaseModel" class="gt_row gt_right">2,687.6073</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">BIC</td>
#> <td headers="Model" class="gt_row gt_right">2,183.0438</td>
#> <td headers="BaseModel" class="gt_row gt_right">2,698.2351</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR Test Statistic</td>
#> <td headers="Model" class="gt_row gt_right">559.0746</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR degrees of freedom</td>
#> <td headers="Model" class="gt_row gt_right">6.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR p-value</td>
#> <td headers="Model" class="gt_row gt_right">0.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">McFadden's Pseudo R^2</td>
#> <td headers="Model" class="gt_row gt_right">0.2083</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
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#>
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#> $LL
#> [1] -1062.266
#>
#> $LLbase
#> [1] -1341.804
#>
#> $LR
#> [1] 559.0746
#>
#> $LRdof
#> [1] 6
#>
#> $LR_pvalue
#> [1] 1.56117e-117
#>
#> $AIC
#> [1] 2140.533
#>
#> $AICbase
#> [1] 2687.607
#>
#> $BIC
#> [1] 2183.044
#>
#> $BICbase
#> [1] 2698.235
#>
#> $PseudoR2
#> [1] 0.2083295
#>
#> $statistics
#> # A tibble: 6 × 3
#> Statistic Model BaseModel
#> <chr> <dbl> <dbl>
#> 1 AIC 2141. 2688.
#> 2 BIC 2183. 2698.
#> 3 LR Test Statistic 559. NA
#> 4 LR degrees of freedom 6 NA
#> 5 LR p-value 0 NA
#> 6 McFadden's Pseudo R^2 0.208 NA
#>
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#> <thead>
#> <tr class="gt_heading">
#> <td colspan="3" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Model Comparison Statistics</td>
#> </tr>
#>
#> <tr class="gt_col_headings">
#> <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Statistic">Statistic</th>
#> <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Model">Model</th>
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#> <tr><td headers="Statistic" class="gt_row gt_left">AIC</td>
#> <td headers="Model" class="gt_row gt_right">2,140.5327</td>
#> <td headers="BaseModel" class="gt_row gt_right">2,687.6073</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">BIC</td>
#> <td headers="Model" class="gt_row gt_right">2,183.0438</td>
#> <td headers="BaseModel" class="gt_row gt_right">2,698.2351</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR Test Statistic</td>
#> <td headers="Model" class="gt_row gt_right">559.0746</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR degrees of freedom</td>
#> <td headers="Model" class="gt_row gt_right">6.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR p-value</td>
#> <td headers="Model" class="gt_row gt_right">0.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">McFadden's Pseudo R^2</td>
#> <td headers="Model" class="gt_row gt_right">0.2083</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> </tbody>
#>
#> </table>
#> </div>
#>
#> $latexTable
#> \begin{table}
#>
#> \caption{Model Comparison Statistics}
#> \centering
#> \begin{tabular}[t]{lrr}
#> \toprule
#> Statistic & Model & BaseModel\\
#> \midrule
#> AIC & 2140.5327 & 2687.607\\
#> BIC & 2183.0438 & 2698.235\\
#> LR Test Statistic & 559.0746 & NA\\
#> LR degrees of freedom & 6.0000 & NA\\
#> LR p-value & 0.0000 & NA\\
#> \addlinespace
#> McFadden's Pseudo R\textasciicircum{}2 & 0.2083 & NA\\
#> \bottomrule
#> \end{tabular}
#> \end{table}
#>
#> $htmlTable
#> <table class='table table-striped'>
#> <caption>Model Comparison Statistics</caption>
#> <thead>
#> <tr>
#> <th style="text-align:left;"> Statistic </th>
#> <th style="text-align:right;"> Model </th>
#> <th style="text-align:right;"> BaseModel </th>
#> </tr>
#> </thead>
#> <tbody>
#> <tr>
#> <td style="text-align:left;"> AIC </td>
#> <td style="text-align:right;"> 2140.5327 </td>
#> <td style="text-align:right;"> 2687.607 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> BIC </td>
#> <td style="text-align:right;"> 2183.0438 </td>
#> <td style="text-align:right;"> 2698.235 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> LR Test Statistic </td>
#> <td style="text-align:right;"> 559.0746 </td>
#> <td style="text-align:right;"> NA </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> LR degrees of freedom </td>
#> <td style="text-align:right;"> 6.0000 </td>
#> <td style="text-align:right;"> NA </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> LR p-value </td>
#> <td style="text-align:right;"> 0.0000 </td>
#> <td style="text-align:right;"> NA </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> McFadden's Pseudo R^2 </td>
#> <td style="text-align:right;"> 0.2083 </td>
#> <td style="text-align:right;"> NA </td>
#> </tr>
#> </tbody>
#> </table>
#>