knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(testpackage) library(stats) library(ggplot2)
To construct a linear regression model using demo_data
m = lm(fev~A+H+M+S, data = demo_data)
To test whether maximum leverage is the same from hat_matrix
and hatvalues
manualy
h = hatvalues(m) all.equal(as.numeric(hat_matrix(m)[1]), unname(h[which.max(h)])) bench::mark(as.numeric(hat_matrix(m)[1]), unname(h[which.max(h)])) plot(bench::mark(mine = as.numeric(hat_matrix(m)[1]), original = unname(h[which.max(h)])))
Benchmark res_3
and rstandard
for computing internally studentized residuals
all.equal(rstandard(m),res_3(demo_data, m, r = "int")) int_bench = bench::mark(original = rstandard(m),mine = res_3(demo_data, m, r = "int")) print(int_bench) plot(int_bench)
Benchmark res_3
and rstandard
for computing externally studentized residuals
all.equal(rstudent(m),res_3(demo_data, m, r = "ex")) ex_bench = bench::mark(original = rstudent(m),mine = res_3(demo_data, m, r = "ex")) print(ex_bench) plot(ex_bench)
Benchmark outlier_influence
and diffits
for computing DFFITS
all.equal(dffits(m),outlier_influence(demo_data, m, option = c("dffits"))) result_ff = bench::mark(original = dffits(m),mine = outlier_influence(demo_data, m, option = c("dffits"))) print(result_ff) plot(result_ff)
Becnhmark outlier_influence
and cooks.distance
for computing Cook's distance
all.equal(outlier_influence(demo_data, m, option = "cd"), cooks.distance(m)) result_cd = bench::mark(mine = outlier_influence(demo_data, m, option = "cd"), original = cooks.distance(m)) print(result_cd) plot(result_cd)
Benchmark outlier_influence
and covratio
for computing COVRATIO
all.equal(outlier_influence(demo_data, m, option = c("cvr")), covratio(m)) result_cvr = bench::mark(mine = outlier_influence(demo_data, m, option = c("cvr")), original = covratio(m)) print(result_cvr) plot(result_cvr)
Demostration on plot_dffits
, plot_cd
, and plot_CVR
plot_dffits(m) plot_cd(m) plot_CVR(m)
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