These functions provide pipe-friendly wrappers around model fitters provided by several external packages. The functions require the corresponding packages to be installed, if the required package is missing the functions warns with directions for how to install it.
zlm_robust() wraps estimatr::lm_robust(), which fits a linear model with
a variety of options for estimating robust standard errors.
zpolr() wraps MASS::polr(), which fits an ordered logistic response for
multi-value ordinal variables, using a proportional odds logistic regression.
zplsr() wraps pls::plsr(), which performs a partial least squares
regression.
Examples
if (requireNamespace("estimatr") && getRversion() >= "4.1.0")
zlm_robust(cars, dist ~ speed) |> summary() |> try()
#>
#> Call:
#> lm_robust(formula = dist ~ speed, data = cars)
#>
#> Standard error type: HC2
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|) CI Lower CI Upper DF
#> (Intercept) -17.579 5.7323 -3.067 3.551e-03 -29.105 -6.053 48
#> speed 3.932 0.4128 9.526 1.211e-12 3.102 4.762 48
#>
#> Multiple R-squared: 0.6511 , Adjusted R-squared: 0.6438
#> F-statistic: 90.75 on 1 and 48 DF, p-value: 1.211e-12
if (requireNamespace("MASS") && getRversion() >= "4.1.0")
zpolr(mtcars, ordered(gear) ~ mpg + hp) |> summary() |> try()
#>
#> Re-fitting to get Hessian
#> Call:
#> polr(formula = ordered(gear) ~ mpg + hp, data = mtcars)
#>
#> Coefficients:
#> Value Std. Error t value
#> mpg 0.37279 0.123027 3.030
#> hp 0.02002 0.009634 2.078
#>
#> Intercepts:
#> Value Std. Error t value
#> 3|4 10.1578 3.6605 2.7750
#> 4|5 12.7982 4.0427 3.1657
#>
#> Residual Deviance: 51.16071
#> AIC: 59.16071
if (requireNamespace("pls") && getRversion() >= "4.1.0")
zplsr(cars, dist ~ speed) |> summary() |> try()
#> Data: X dimension: 50 1
#> Y dimension: 50 1
#> Fit method: kernelpls
#> Number of components considered: 1
#> TRAINING: % variance explained
#> 1 comps
#> X 100.00
#> dist 65.11