Interaction Terms
The bregr package provides comprehensive support for
the processing and visualization of interaction terms in regression
models, provided that the interaction term is inherently supported by
the model.
Below is an illustrative example using a linear model:
rv <- br_pipeline(
data = mtcars,
y = "mpg", x = c("cyl", "disp*hp"), x2 = "am",
method = "lm"
)
Examine the results:
br_get_results(rv, tidy = TRUE)
#> # A tibble: 6 × 8
#> Focal_variable term estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl cyl -2.50e+0 0.361 -6.93 1.28e-7 -3.24e+0 -1.76
#> 2 cyl am 2.57e+0 1.29 1.99 5.64e-2 -7.42e-2 5.21
#> 3 disp*hp disp -5.42e-2 0.0186 -2.92 7.06e-3 -9.23e-2 -0.0160
#> 4 disp*hp hp -9.20e-2 0.0244 -3.77 8.16e-4 -1.42e-1 -0.0419
#> 5 disp*hp am 2.29e+0 1.45 1.57 1.27e-1 -6.96e-1 5.27
#> 6 disp*hp disp:hp 2.27e-4 0.0000938 2.42 2.26e-2 3.45e-5 0.000419
Visualize the results:
Additionally, specified models can be visualized using the functions
provided by bregr:
br_show_forest_ggstats(rv, idx = 2)
Or compare them:
br_show_forest_ggstats(rv)
Customized GLM Family
For GLM models, the family object can be further customized with
various arguments. By default, bregr directly supports
GLM family functions with their default arguments, which can be viewed
using:
br_avail_methods()
#> [1] "lm" "coxph" "survreg" "clogit"
#> [5] "cch" "binomial" "gaussian" "Gamma"
#> [9] "inverse.gaussian" "poisson" "quasi" "quasibinomial"
#> [13] "quasipoisson" "nls" "aov"
However, for alternative GLM configurations, modifications can be
made directly. For instance, consider the following example using a
quasi-family with specified variance and link functions:
data <- data.frame(
x = rnorm(100)
)
data$y <- rpois(100, exp(1 + data$x))
head(data)
#> x y
#> 1 0.5594572 6
#> 2 -0.7838015 0
#> 3 -1.9278205 1
#> 4 1.1311586 8
#> 5 -1.3567963 0
#> 6 1.1693306 8
rv <- br_pipeline(
data = data,
y = "y", x = "x",
method = 'quasi(variance = "mu", link = "log")'
)
#> Warning: nonstandard method `quasi(variance = "mu", link = "log")` passed to
#> `stats::glm()`, double-check if it's correct
br_get_results(rv, tidy = TRUE)
#> # A tibble: 1 × 8
#> Focal_variable term estimate std.error statistic p.value conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 x x 1.02 0.0517 19.7 7.70e-36 0.916 1.12
This is equivalent to:
glm(y ~ x, data = data, family = quasi(variance = "mu", link = "log")) |>
summary()
#>
#> Call:
#> glm(formula = y ~ x, family = quasi(variance = "mu", link = "log"),
#> data = data)
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.99441 0.06114 16.26 <2e-16 ***
#> x 1.01762 0.05167 19.69 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for quasi family taken to be 0.8259798)
#>
#> Null deviance: 424.092 on 99 degrees of freedom
#> Residual deviance: 99.266 on 98 degrees of freedom
#> AIC: NA
#>
#> Number of Fisher Scoring iterations: 5