The model_parameters() function also allows the computation of standard errors, confidence intervals, and p-values based on robust covariance matrix estimation from model parameters. Robust estimation is relies on sandwich and clubSandwich package. This means that all models supported by either of these packages will with model_parameters() when robust = TRUE.

## Linear Regression Models

### Robust Covariance Matrix Estimation from Model Parameters

By default, when model_parameters(robust = TRUE), it internally calls sandwich::vcovHC(type = "HC3"). However, there are three arguments (see ?standard_error_robust for further details) that allow for choosing different methods and options of robust estimation: - vcov_estimation - vcov_type - vcov_args

Let us start with a simple example, which uses a heteroskedasticity-consistent covariance matrix estimation with estimation-type “HC3” (i.e. sandwich::vcovHC(type = "HC3")).

First let’s create a simple linear regression model, which we know violates homoscedasticity assumption, and thus robust estimation methods are to be considered.

data(cars)
model <- lm(dist ~ speed, data = cars)

library(performance)
check_heteroscedasticity(model)
#> Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.031).

We would extract model parameters both with and without robust estimation to highlight difference it makes to standard errors, confidence intervals, and p-values. Also, note that the coefficient estimate and the t-statistic associated with it remain unchanged.

# model parameters, where SE, CI, and p-values are *not* based on robust estimation
model_parameters(model)
#> Parameter   | Coefficient |   SE |          95% CI | t(48) |      p
#> -------------------------------------------------------------------
#> (Intercept) |      -17.58 | 6.76 | [-31.17, -3.99] | -2.60 | 0.012
#> speed       |        3.93 | 0.42 | [  3.10,  4.77] |  9.46 | < .001

# model parameters, where SE, CI, and p-values are based on robust estimation
mp <- model_parameters(model, robust = TRUE)
mp
#> Parameter   | Coefficient |   SE |          95% CI | t(48) |      p
#> -------------------------------------------------------------------
#> (Intercept) |      -17.58 | 5.93 | [-29.51, -5.65] | -2.96 | 0.005
#> speed       |        3.93 | 0.43 | [  3.07,  4.79] |  9.20 | < .001

# compare standard errors to result from sandwich-package
mp$SE #> [1] 5.93 0.43 unname(sqrt(diag(sandwich::vcovHC(model)))) #> [1] 5.93 0.43 ### Cluster-Robust Covariance Matrix Estimation (sandwich) If a different type of covariance matrix estimation is required, use the vcov_estimation-argument. This argument needs the suffix for the related vcov*()-functions as value, i.e. vcov_estimation = "CL" would call sandwich::vcovCL(), or vcov_estimation = "HAC" would call sandwich::vcovHAC(). The specific estimation type can also be changed with vcov_type. For example, sandwich::vcovCL() accepts estimation types HC0 to HC3. In the next example, we use a clustered covariance matrix estimation with HC1-estimation type. # let's create a more complicated model data(iris) model <- lm(Petal.Length ~ Sepal.Length * Species + Sepal.Width, data = iris) # change estimation-type mp <- model_parameters( model, robust = TRUE, vcov_estimation = "CL", # type of covariance matrix vcov_type = "HC1" # type of robust estimation ) mp #> Parameter | Coefficient | SE | 95% CI | t(143) | p #> ------------------------------------------------------------------------------------------ #> (Intercept) | 0.87 | 0.42 | [ 0.03, 1.70] | 2.05 | 0.042 #> Sepal.Length | 0.04 | 0.11 | [-0.18, 0.26] | 0.40 | 0.692 #> Species [versicolor] | -0.78 | 0.65 | [-2.07, 0.51] | -1.19 | 0.237 #> Species [virginica] | -0.41 | 0.59 | [-1.57, 0.75] | -0.70 | 0.483 #> Sepal.Width | 0.11 | 0.08 | [-0.05, 0.27] | 1.38 | 0.170 #> Sepal.Length * Species [versicolor] | 0.61 | 0.12 | [ 0.37, 0.85] | 4.96 | < .001 #> Sepal.Length * Species [virginica] | 0.68 | 0.11 | [ 0.46, 0.90] | 6.15 | < .001 # compare standard errors to result from sandwich-package mp$SE
#> [1] 0.422 0.111 0.653 0.587 0.079 0.123 0.111
unname(sqrt(diag(sandwich::vcovCL(model))))
#> [1] 0.422 0.111 0.653 0.587 0.079 0.123 0.111

Usually, clustered covariance matrix estimation is used when there is a cluster-structure in the data. The variable indicating the cluster-structure can be defined in sandwich::vcovCL() with the cluster-argument. In model_parameters(), additional arguments that should be passed down to functions from the sandwich package can be specified in vcov_args:

iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris))) # change estimation-type, defining additional arguments mp <- model_parameters( model, robust = TRUE, vcov_estimation = "CL", vcov_type = "HC1", vcov_args = list(cluster = iris$cluster)
)

mp
#> Parameter                           | Coefficient |   SE |        95% CI | t(143) |      p
#> ------------------------------------------------------------------------------------------
#> (Intercept)                         |        0.87 | 0.34 | [ 0.20, 1.53] |   2.57 | 0.011
#> Sepal.Length                        |        0.04 | 0.07 | [-0.10, 0.19] |   0.61 | 0.540
#> Species [versicolor]                |       -0.78 | 0.52 | [-1.80, 0.25] |  -1.49 | 0.137
#> Species [virginica]                 |       -0.41 | 0.26 | [-0.94, 0.11] |  -1.56 | 0.120
#> Sepal.Width                         |        0.11 | 0.07 | [-0.03, 0.25] |   1.52 | 0.131
#> Sepal.Length * Species [versicolor] |        0.61 | 0.10 | [ 0.42, 0.80] |   6.29 | < .001
#> Sepal.Length * Species [virginica]  |        0.68 | 0.05 | [ 0.58, 0.78] |  13.28 | < .001

# compare standard errors to result from sandwich-package
mp$SE #> [1] 0.337 0.072 0.519 0.264 0.072 0.097 0.051 unname(sqrt(diag(sandwich::vcovCL(model, cluster = iris$cluster))))
#> [1] 0.337 0.072 0.519 0.264 0.072 0.097 0.051

### Cluster-Robust Covariance Matrix Estimation (clubSandwich)

Cluster-robust estimation of the variance-covariance matrix can also be achieved using clubSandwich::vcovCR(). Thus, when vcov_estimation = "CR", the related function from the clubSandwich package is called. Note that this function requires the specification of the cluster-argument.

# create fake-cluster-variable, to demonstrate cluster robust standard errors
iris$cluster <- factor(rep(LETTERS[1:8], length.out = nrow(iris))) # cluster-robust estimation mp <- model_parameters( model, robust = TRUE, vcov_estimation = "CR", vcov_type = "CR1", vcov_args = list(cluster = iris$cluster)
)
mp
#> Parameter                           | Coefficient |   SE |        95% CI | t(143) |      p
#> ------------------------------------------------------------------------------------------
#> (Intercept)                         |        0.87 | 0.33 | [ 0.21, 1.52] |   2.62 | 0.010
#> Sepal.Length                        |        0.04 | 0.07 | [-0.10, 0.18] |   0.63 | 0.531
#> Species [versicolor]                |       -0.78 | 0.51 | [-1.78, 0.23] |  -1.53 | 0.129
#> Species [virginica]                 |       -0.41 | 0.26 | [-0.92, 0.10] |  -1.60 | 0.112
#> Sepal.Width                         |        0.11 | 0.07 | [-0.03, 0.25] |   1.55 | 0.123
#> Sepal.Length * Species [versicolor] |        0.61 | 0.09 | [ 0.42, 0.79] |   6.42 | < .001
#> Sepal.Length * Species [virginica]  |        0.68 | 0.05 | [ 0.58, 0.78] |  13.56 | < .001

# compare standard errors to result from clubSsandwich-package
mp$SE #> [1] 0.330 0.070 0.508 0.259 0.071 0.095 0.050 unname(sqrt(diag(clubSandwich::vcovCR(model, type = "CR1", cluster = iris$cluster))))
#> [1] 0.330 0.070 0.508 0.259 0.071 0.095 0.050

### Robust Covariance Matrix Estimation on Standardized Model Parameters

Finally, robust estimation can be combined with standardization. However, robust covariance matrix estimation only works for standardize = "refit".

# model parameters, robust estimation on standardized model
model_parameters(model, standardize = "refit", robust = TRUE)
#> Parameter                           | Coefficient |   SE |         95% CI | t(143) |      p
#> -------------------------------------------------------------------------------------------
#> (Intercept)                         |       -1.30 | 0.07 | [-1.44, -1.16] | -18.70 | < .001
#> Sepal.Length                        |        0.02 | 0.06 | [-0.09,  0.13] |   0.37 | 0.711
#> Species [versicolor]                |        1.57 | 0.09 | [ 1.40,  1.74] |  17.84 | < .001
#> Species [virginica]                 |        2.02 | 0.09 | [ 1.84,  2.20] |  22.49 | < .001
#> Sepal.Width                         |        0.03 | 0.02 | [-0.01,  0.07] |   1.32 | 0.190
#> Sepal.Length * Species [versicolor] |        0.28 | 0.06 | [ 0.16,  0.41] |   4.65 | < .001
#> Sepal.Length * Species [virginica]  |        0.32 | 0.06 | [ 0.21,  0.43] |   5.75 | < .001

## Linear Mixed-Effects Regression Models

### Robust Covariance Matrix Estimation for Mixed Models

For linear mixed-effects models, that by definition have a clustered (hierarchical or multilevel) structure in the data, it is also possible to estimate a cluster-robust covariance matrix. This is possible due to the clubSandwich package, thus we need to define the same arguments as in the above example.

library(lme4)
data(iris)
set.seed(1234)
iris$grp <- as.factor(sample(1:3, nrow(iris), replace = TRUE)) # fit example model model <- lme4::lmer( Sepal.Length ~ Species * Sepal.Width + Petal.Length + (1 | grp), data = iris ) # model parameters without robust estimation model_parameters(model) #> # Fixed Effects #> #> Parameter | Coefficient | SE | 95% CI | t(141) | p #> ------------------------------------------------------------------------------------------ #> (Intercept) | 1.55 | 0.40 | [ 0.77, 2.34] | 3.87 | < .001 #> Species [versicolor] | 0.41 | 0.55 | [-0.66, 1.49] | 0.75 | 0.453 #> Species [virginica] | -0.41 | 0.58 | [-1.55, 0.73] | -0.70 | 0.482 #> Sepal.Width | 0.66 | 0.11 | [ 0.44, 0.88] | 5.83 | < .001 #> Petal.Length | 0.82 | 0.07 | [ 0.69, 0.95] | 12.52 | < .001 #> Species [versicolor] * Sepal.Width | -0.48 | 0.19 | [-0.85, -0.12] | -2.60 | 0.009 #> Species [virginica] * Sepal.Width | -0.36 | 0.18 | [-0.71, -0.01] | -1.99 | 0.046 #> #> # Random Effects #> #> Parameter | Coefficient #> --------------------------------- #> SD (Intercept: grp) | 0.08 #> SD (Residual) | 0.55 # model parameters with cluster robust estimation model_parameters( model, robust = TRUE, vcov_estimation = "CR", vcov_type = "CR1", vcov_args = list(cluster = iris$grp)
)
#> # Fixed Effects
#>
#> Parameter                          | Coefficient |   SE |         95% CI | t(141) |      p
#> ------------------------------------------------------------------------------------------
#> (Intercept)                        |        1.55 | 0.40 | [ 0.76,  2.35] |   3.87 | < .001
#> Species [versicolor]               |        0.41 | 0.80 | [-1.17,  1.99] |   0.75 | 0.608
#> Species [virginica]                |       -0.41 | 0.19 | [-0.78, -0.03] |  -0.70 | 0.033
#> Sepal.Width                        |        0.66 | 0.10 | [ 0.46,  0.86] |   5.83 | < .001
#> Petal.Length                       |        0.82 | 0.05 | [ 0.72,  0.91] |  12.52 | < .001
#> Species [versicolor] * Sepal.Width |       -0.48 | 0.35 | [-1.18,  0.21] |  -2.60 | 0.172
#> Species [virginica] * Sepal.Width  |       -0.36 | 0.11 | [-0.57, -0.15] |  -1.99 | < .001
#>
#> # Random Effects
#>
#> Parameter           | Coefficient
#> ---------------------------------
#> SD (Intercept: grp) |        0.08
#> SD (Residual)       |        0.55

Notice that robust estimation returns different standard errors, confidence intervals, and p-values compared to the standard estimation. Also, note that the coefficient estimate and the statistic associated with it remain unchanged.

### Robust Covariance Matrix Estimation on Standardized Mixed Model Parameters

Once again, robust estimation can be combined with standardization for linear mixed-effects models as well and works only with standardize = "refit".

# model parameters, cluster robust estimation of standardized mixed model
model_parameters(
model,
standardize = "refit",
robust = TRUE,
vcov_estimation = "CR",
vcov_type = "CR1",
vcov_args = list(cluster = iris\$grp)
)
#> # Fixed Effects
#>
#> Parameter                          | Coefficient |   SE |         95% CI | t(141) |      p
#> ------------------------------------------------------------------------------------------
#> (Intercept)                        |        0.97 | 0.08 | [ 0.82,  1.12] |   4.74 | < .001
#> Species [versicolor]               |       -1.29 | 0.33 | [-1.95, -0.63] |  -4.91 | < .001
#> Species [virginica]                |       -1.81 | 0.23 | [-2.26, -1.37] |  -5.33 | < .001
#> Sepal.Width                        |        0.35 | 0.05 | [ 0.24,  0.45] |   5.83 | < .001
#> Petal.Length                       |        1.74 | 0.10 | [ 1.54,  1.94] |  12.52 | < .001
#> Species [versicolor] * Sepal.Width |       -0.25 | 0.19 | [-0.62,  0.11] |  -2.60 | 0.172
#> Species [virginica] * Sepal.Width  |       -0.19 | 0.06 | [-0.30, -0.08] |  -1.99 | < .001

Notice how drastically some of the p-values change between robust-unstandardized model and robust-standardized model.