Compare Regression Models with Likelihood Ratio Test, AIC, and BIC
regCompTest.RdThis function compares fitted regression model objects 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,
...,
model2 = NULL
)Arguments
- model
A fitted regression model object.
- data
An optional data frame containing the variables in the model. If not supplied, the original data used to estimate the model will be used when available.
- 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. These arguments are used only when a single fitted model is supplied and the function needs to estimate the base model.
- model2
An optional second fitted regression model object. If supplied, the function compares
modelandmodel2directly and does not estimate a separate base model.
Value
A list containing the comparison results.
For the single-model workflow, the returned list includes LL,
LLbase, LR, LRdof, AIC, AICbase,
BIC, BICbase, LR_pvalue, PseudoR2,
statistics, gtTable, latexTable, and htmlTable.
For the two-model workflow, the returned list includes model1,
model2, base_model, comparison_model,
base_index, comparison_index, LL_base,
LL_comparison, LR, LRdof, LR_pvalue,
statistics, gtTable, latexTable, and htmlTable.
Details
When only one fitted model is supplied, the function estimates a base model from the supplied data and compares the fitted model against that base model. When two fitted models are supplied, the function compares the two fitted models directly and uses the model with the larger log-likelihood as the base model for the LR test.
The function performs the following steps for the single-model workflow:
Fits the base model, either a Poisson regression or another specified model.
Computes the log-likelihoods of the provided model and the base model.
Calculates the AIC and BIC for both models.
Conducts a Likelihood Ratio test to compare the fitted model against the estimated base model when the fitted model has more parameters than the base model.
Computes McFadden's Pseudo R^2.
For the two-model workflow, the function compares the two fitted models directly. The model with the larger log-likelihood is treated as the base model for the LR test, and the other model is treated as the comparison model.
The Likelihood-Ratio test is computed as $$LR = -2 (LL_{base} - LL_{comparison})$$. The test is chi-squared with degrees of freedom $$dof = |N_{base \ params} - N_{comparison \ params}|$$. 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
# Example 1: Compare one fitted model against an estimated Poisson base 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, data = washington_roads, basemodel = "Poisson",
variables = TRUE, print = TRUE)
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#> <thead>
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#> <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>
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#> <tr><td headers="Statistic" class="gt_row gt_left">Log-likelihood</td>
#> <td headers="Model" class="gt_row gt_right">−1,062.2664</td>
#> <td headers="BaseModel" class="gt_row gt_right">−1,071.2672</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">Number of parameters</td>
#> <td headers="Model" class="gt_row gt_right">8.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">6.0000</td></tr>
#> <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">2,154.5345</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">2,186.4178</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR Test Statistic</td>
#> <td headers="Model" class="gt_row gt_right">18.0018</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">2.0000</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">0.0001</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">0.3029</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
#> </tbody>
#>
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#> </div>
#> $model
#> Maximum Likelihood estimation
#> Nelder-Mead maximization, 703 iterations
#> Return code 0: successful convergence
#> Log-Likelihood: -1062.266 (8 free parameter(s))
#> Estimate(s): -7.79761 0.9418564 0.8335215 -0.3860324 0.2577976 0.6863257 -1.327278 0.4993697
#>
#> $y
#> [1] 0 2 2 0 0 1 2 0 1 0 0 0 0 1 0 0 1 0 0 0 0 2 0 0
#> [25] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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#> [145] 0 1 1 0 0 0 0 0 0 0 2 2 1 1 3 0 1 2 1 1 0 0 0 0
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#> [193] 8 1 2 2 1 3 4 5 0 1 6 4 0 0 1 4 0 0 0 0 0 0 1 1
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#> [409] 1 1 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 2 0 1 0 0
#> [433] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
#> [457] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1
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#> [601] 0 0 0 2 0 0 1 0 0 0 0 0 0 1 1 2 0 0 0 0 0 0 0 1
#> [625] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 2 0 0 0 0 0 0 0
#> [649] 1 1 0 0 1 3 0 3 4 2 3 1 0 1 1 0 0 0 0 1 0 1 0 2
#> [673] 0 1 2 1 2 4 2 2 3 2 3 2 1 0 0 0 0 0 1 1 1 5 0 2
#> [697] 5 3 1 3 2 5 2 2 2 1 4 0 0 0 0 0 0 0 0 0 1 0 0 0
#> [721] 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
#> [745] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1
#> [769] 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 2 2 1 1
#> [793] 1 2 1 1 0 0 0 0 1 1 1 2 0 2 1 4 0 2 1 3 0 1 2 2
#> [817] 1 0 4 1 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0
#> [841] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [865] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
#> [889] 0 1 0 0 0 1 0 1 0 0 0 0 2 0 1 2 1 0 1 1 1 1 0 0
#> [913] 0 0 3 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [937] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [961] 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 2 0 0 1 0
#> [985] 0 0 0 0 0 0 0 0 0 0 0 0 3 3 2 2 8 1 3 0 1 0 1 0
#> [1009] 0 0 1 1 0 0 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1033] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
#> [1057] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0
#> [1081] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0
#> [1105] 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 1 0 2 1 4 1 0
#> [1129] 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0
#> [1153] 0 1 1 1 7 0 4 3 0 1 1 0 0 0 3 1 1 1 1 0 0 1 3 0
#> [1177] 4 2 1 4 2 2 1 1 0 0 0 0 0 0 4 2 0 4 0 3 7 2 4 2
#> [1201] 4 2 6 0 1 2 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1
#> [1225] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
#> [1249] 0 0 0 0 0 1 0 0 0 0 0 0 0 1 2 0 0 0 0 2 0 0 0 0
#> [1273] 0 0 0 1 0 0 0 1 0 0 1 2 1 2 1 0 2 0 2 1 1 2 1 1
#> [1297] 2 0 0 0 0 0 1 0 0 2 0 4 1 3 0 0 0 0 0 0 4 1 5 0
#> [1321] 0 1 3 2 0 1 2 0 0 0 0 0 2 1 0 0 0 0 0 1 0 0 0 0
#> [1345] 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0
#> [1369] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1393] 0 0 0 0 0 0 0 0 4 0 0 0 0 1 0 1 1 0 2 1 0 0 2 0
#> [1417] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0
#> [1465] 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 2 0 1 0 0
#> [1489] 1 0 0 0 1 0 0 0 0 1 2 1 5
#>
#> $LL
#> [1] -1062.266
#>
#> $LLbase
#> [1] -1071.267
#>
#> $LR
#> [1] 18.00176
#>
#> $LRdof
#> [1] 2
#>
#> $AIC
#> [1] 2140.533
#>
#> $AICbase
#> [1] 2154.534
#>
#> $BIC
#> [1] 2183.044
#>
#> $BICbase
#> [1] 2186.418
#>
#> $LR_pvalue
#> [1] 0.000123301
#>
#> $PseudoR2
#> [1] 0.3028969
#>
#> $statistics
#> # A tibble: 8 × 3
#> Statistic Model BaseModel
#> <chr> <dbl> <dbl>
#> 1 Log-likelihood -1062. -1071.
#> 2 Number of parameters 8 6
#> 3 AIC 2141. 2155.
#> 4 BIC 2183. 2186.
#> 5 LR Test Statistic 18.0 NA
#> 6 LR degrees of freedom 2 NA
#> 7 LR p-value 0.0001 NA
#> 8 McFadden's Pseudo R^2 0.303 NA
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#> <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>
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#> <tr><td headers="Statistic" class="gt_row gt_left">Log-likelihood</td>
#> <td headers="Model" class="gt_row gt_right">−1,062.2664</td>
#> <td headers="BaseModel" class="gt_row gt_right">−1,071.2672</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">Number of parameters</td>
#> <td headers="Model" class="gt_row gt_right">8.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">6.0000</td></tr>
#> <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">2,154.5345</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">2,186.4178</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR Test Statistic</td>
#> <td headers="Model" class="gt_row gt_right">18.0018</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">2.0000</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">0.0001</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">0.3029</td>
#> <td headers="BaseModel" class="gt_row gt_right">NA</td></tr>
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#>
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#>
#> $latexTable
#> \begin{table}
#>
#> \caption{Model Comparison Statistics}
#> \centering
#> \begin{tabular}[t]{lrr}
#> \toprule
#> Statistic & Model & BaseModel\\
#> \midrule
#> Log-likelihood & -1062.2664 & -1071.267\\
#> Number of parameters & 8.0000 & 6.000\\
#> AIC & 2140.5327 & 2154.535\\
#> BIC & 2183.0438 & 2186.418\\
#> LR Test Statistic & 18.0018 & NA\\
#> \addlinespace
#> LR degrees of freedom & 2.0000 & NA\\
#> LR p-value & 0.0001 & NA\\
#> McFadden's Pseudo R\textasciicircum{}2 & 0.3029 & NA\\
#> \bottomrule
#> \end{tabular}
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#>
#> $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>
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#> </thead>
#> <tbody>
#> <tr>
#> <td style="text-align:left;"> Log-likelihood </td>
#> <td style="text-align:right;"> -1062.2664 </td>
#> <td style="text-align:right;"> -1071.267 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> Number of parameters </td>
#> <td style="text-align:right;"> 8.0000 </td>
#> <td style="text-align:right;"> 6.000 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> AIC </td>
#> <td style="text-align:right;"> 2140.5327 </td>
#> <td style="text-align:right;"> 2154.535 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> BIC </td>
#> <td style="text-align:right;"> 2183.0438 </td>
#> <td style="text-align:right;"> 2186.418 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> LR Test Statistic </td>
#> <td style="text-align:right;"> 18.0018 </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;"> 2.0000 </td>
#> <td style="text-align:right;"> NA </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> LR p-value </td>
#> <td style="text-align:right;"> 0.0001 </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;"> 0.3029 </td>
#> <td style="text-align:right;"> NA </td>
#> </tr>
#> </tbody>
#> </table>
#>
# Example 2: Compare two fitted models directly
nb2.base <- countreg(Total_crashes ~ lnaadt + lnlength + speed50 +
ShouldWidth04 + AADTover10k,
data = washington_roads, family = "NB2", method = "NM",
max.iters = 3000)
regCompTest(nbp.base, data = washington_roads, model2 = nb2.base,
print = TRUE)
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#>
#> <tr class="gt_col_headings">
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#> <tr><td headers="Statistic" class="gt_row gt_left">Log-likelihood</td>
#> <td headers="Model" class="gt_row gt_right">−1,062.2664</td>
#> <td headers="BaseModel" class="gt_row gt_right">−1,063.6726</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">Number of parameters</td>
#> <td headers="Model" class="gt_row gt_right">8.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">7.0000</td></tr>
#> <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">2,141.3452</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">2,178.5424</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR Test Statistic</td>
#> <td headers="Model" class="gt_row gt_right">2.8125</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">1.0000</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">0.0935</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">0.3029</td>
#> <td headers="BaseModel" class="gt_row gt_right">0.3020</td></tr>
#> </tbody>
#>
#> </table>
#> </div>
#> $base_model
#> Maximum Likelihood estimation
#> Nelder-Mead maximization, 703 iterations
#> Return code 0: successful convergence
#> Log-Likelihood: -1062.266 (8 free parameter(s))
#> Estimate(s): -7.79761 0.9418564 0.8335215 -0.3860324 0.2577976 0.6863257 -1.327278 0.4993697
#>
#> $comparison_model
#> Maximum Likelihood estimation
#> Nelder-Mead maximization, 890 iterations
#> Return code 0: successful convergence
#> Log-Likelihood: -1063.673 (7 free parameter(s))
#> Estimate(s): -7.150729 0.8663599 0.8358287 -0.4012173 0.230993 0.8275592 -1.32798
#>
#> $base_index
#> [1] 1
#>
#> $comparison_index
#> [1] 2
#>
#> $LL_base
#> [1] -1062.266
#>
#> $LL_comparison
#> [1] -1063.673
#>
#> $LR
#> [1] 2.812464
#>
#> $LRdof
#> [1] 1
#>
#> $LR_pvalue
#> [1] 0.0935346
#>
#> $PseudoR2_base
#> [1] 0.3028969
#>
#> $PseudoR2_comparison
#> [1] 0.301974
#>
#> $statistics
#> # A tibble: 8 × 3
#> Statistic Model BaseModel
#> <chr> <dbl> <dbl>
#> 1 Log-likelihood -1062. -1064.
#> 2 Number of parameters 8 7
#> 3 AIC 2141. 2141.
#> 4 BIC 2183. 2179.
#> 5 LR Test Statistic 2.81 NA
#> 6 LR degrees of freedom 1 NA
#> 7 LR p-value 0.0935 NA
#> 8 McFadden's Pseudo R^2 0.303 0.302
#>
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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">Log-likelihood</td>
#> <td headers="Model" class="gt_row gt_right">−1,062.2664</td>
#> <td headers="BaseModel" class="gt_row gt_right">−1,063.6726</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">Number of parameters</td>
#> <td headers="Model" class="gt_row gt_right">8.0000</td>
#> <td headers="BaseModel" class="gt_row gt_right">7.0000</td></tr>
#> <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">2,141.3452</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">2,178.5424</td></tr>
#> <tr><td headers="Statistic" class="gt_row gt_left">LR Test Statistic</td>
#> <td headers="Model" class="gt_row gt_right">2.8125</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">1.0000</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">0.0935</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">0.3029</td>
#> <td headers="BaseModel" class="gt_row gt_right">0.3020</td></tr>
#> </tbody>
#>
#> </table>
#> </div>
#>
#> $latexTable
#> \begin{table}
#>
#> \caption{Model Comparison Statistics}
#> \centering
#> \begin{tabular}[t]{lrr}
#> \toprule
#> Statistic & Model & BaseModel\\
#> \midrule
#> Log-likelihood & -1062.2664 & -1063.673\\
#> Number of parameters & 8.0000 & 7.000\\
#> AIC & 2140.5327 & 2141.345\\
#> BIC & 2183.0438 & 2178.542\\
#> LR Test Statistic & 2.8125 & NA\\
#> \addlinespace
#> LR degrees of freedom & 1.0000 & NA\\
#> LR p-value & 0.0935 & NA\\
#> McFadden's Pseudo R\textasciicircum{}2 & 0.3029 & 0.302\\
#> \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;"> Log-likelihood </td>
#> <td style="text-align:right;"> -1062.2664 </td>
#> <td style="text-align:right;"> -1063.673 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> Number of parameters </td>
#> <td style="text-align:right;"> 8.0000 </td>
#> <td style="text-align:right;"> 7.000 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> AIC </td>
#> <td style="text-align:right;"> 2140.5327 </td>
#> <td style="text-align:right;"> 2141.345 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> BIC </td>
#> <td style="text-align:right;"> 2183.0438 </td>
#> <td style="text-align:right;"> 2178.542 </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> LR Test Statistic </td>
#> <td style="text-align:right;"> 2.8125 </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;"> 1.0000 </td>
#> <td style="text-align:right;"> NA </td>
#> </tr>
#> <tr>
#> <td style="text-align:left;"> LR p-value </td>
#> <td style="text-align:right;"> 0.0935 </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;"> 0.3029 </td>
#> <td style="text-align:right;"> 0.302 </td>
#> </tr>
#> </tbody>
#> </table>
#>