Author: Eunseop Kim

# Model fitting

#### Eunseop Kim

## Fitting an `EL` object

The melt package provides several functions to construct an `EL` object or an object that inherits from `EL`:

- `el_mean()` for the mean.
- `el_sd()` for the standard deviation.
- `el_lm()` for linear models.
- `el_glm()` for generalized linear models.

We illustrate the usage of `el_mean()` with the `faithful` data set.

    data("faithful")
    str(faithful)
    #> 'data.frame':    272 obs. of  2 variables:
    #>  $ eruptions: num  3.6 1.8 3.33 2.28 4.53 ...
    #>  $ waiting  : num  79 54 74 62 85 55 88 85 51 85 ...

    summary(faithful)
    #>    eruptions        waiting
    #>  Min.   :1.600   Min.   :43.0
    #>  1st Qu.:2.163   1st Qu.:58.0
    #>  Median :4.000   Median :76.0
    #>  Mean   :3.488   Mean   :70.9
    #>  3rd Qu.:4.454   3rd Qu.:82.0
    #>  Max.   :5.100   Max.   :96.0

Suppose we are interested in evaluating empirical likelihood at `c(3.5, 70)`.

    fit <- el_mean(faithful, par = c(3.5, 70))
    class(fit)
    #> [1] "EL"
    #> attr(,"package")
    #> [1] "melt"

    showClass("EL")
    #> Class "EL" [package "melt"]
    #>
    #> Slots:
    #>
    #> Name:         optim         logp         logl        loglr    statistic
    #> Class:         list      numeric      numeric      numeric      numeric
    #>
    #> Name:            df         pval         nobs         npar      weights
    #> Class:      integer      numeric      integer      integer      numeric
    #>
    #> Name:  coefficients       method         data      control
    #> Class:      numeric    character          ANY    ControlEL
    #>
    #> Known Subclasses:
    #> Class "CEL", directly
    #> Class "SD", directly
    #> Class "LM", by class "CEL", distance 2
    #> Class "GLM", by class "CEL", distance 3
    #> Class "QGLM", by class "CEL", distance 4

The `faithful` data frame is coerced to a numeric matrix. Simple print method shows essential information on `fit`.

    fit
    #>
    #>  Empirical Likelihood
    #>
    #> Model: mean
    #>
    #> Maximum EL estimates:
    #> eruptions   waiting
    #>     3.488    70.897
    #>
    #> Chisq: 8.483, df: 2, Pr(>Chisq): 0.01439
    #> EL evaluation: converged

Note that the maximum empirical likelihood estimates are the same as the sample average. The chi-square value shown corresponds to the minus twice the empirical log-likelihood ratio. It has an asymptotic chi-square distribution of 2 degrees of freedom under the null hypothesis. Hence the \(p\)-value here is not exact. The convergence status at the bottom can be used to check the convex hull constraint.

Weighted data can be handled by supplying the `weights` argument. For non-`NULL` `weights`, weighted empirical likelihood is computed. Any valid `weights` is re-scaled for internal computation to add up to the total number of observations. For simplicity, we use `faithful$waiting` as our weight vector.

    w <- faithful$waiting
    (wfit <- el_mean(faithful, par = c(3.5, 70), weights = w))
    #>
    #>  Weighted Empirical Likelihood
    #>
    #> Model: mean
    #>
    #> Maximum EL estimates:
    #> eruptions   waiting
    #>     3.684    73.494
    #>
    #> Chisq: 25.41, df: 2, Pr(>Chisq): 3.039e-06
    #> EL evaluation: converged

We get different results, where the estimates are now the weighted sample average. The chi-square value and the associated \(p\)-value are based on the same limit theorem, but care must be taken when interpreting the results since they are largely affected by the limiting behavior of the weights.