Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(JWileymisc)
## -----------------------------------------------------------------------------
test <- testDistribution(mtcars$mpg, "normal")
head(test$Data)
table(test$Data$isEV)
plot(test)
## -----------------------------------------------------------------------------
test <- testDistribution(mtcars$mpg, "normal",
extremevalues = "theoretical",
ev.perc = .10)
## view the data with extreme values
head(test$Data)
## count how many extreme values there are
table(test$Data$isEV)
## plot the distribution
plot(test)
## show which values are extreme
test$Data[isEV == "Yes"]
## view extreme values on mpg in the original dataset
## by use the original order, the original rows to select
## the correct rows from the original dataset
mtcars[test$Data[isEV == "Yes", OriginalOrder], ]
## -----------------------------------------------------------------------------
test <- testDistribution(mtcars$mpg, "normal",
extremevalues = "empirical",
ev.perc = .10)
head(test$Data)
table(test$Data$isEV)
plot(test)
## -----------------------------------------------------------------------------
testN <- testDistribution(mtcars$mpg, "normal",
extremevalues = "theoretical",
ev.perc = .05)
testG <- testDistribution(mtcars$mpg, "gamma",
extremevalues = "theoretical",
ev.perc = .05)
## compare the log likelihood assuming a normal or gamma distribution
testN$Distribution$LL
testG$Distribution$LL
plot(testN)
plot(testG)
## ----fig.height = 10----------------------------------------------------------
m <- lm(mpg ~ hp * factor(cyl), data = mtcars)
md <- modelDiagnostics(m, ev.perc = .05)
plot(md, ncol = 1)
## -----------------------------------------------------------------------------
## show extreme values
md$extremeValues
## show extreme values in overall dataset
mtcars[md$extremeValues$Index, 1:4]
## ----results = "hide"---------------------------------------------------------
## exclude extreme values
m2 <- lm(mpg ~ hp * factor(cyl), data = mtcars[-md$extremeValues$Index, ])
## show a summary of coefficients from both models
## and the percent change
round(data.frame(
M1 = coef(m),
M2 = coef(m2),
PercentChange = coef(m2) / coef(m) * 100 - 100), 2)
## ----echo = FALSE, results = "asis"-------------------------------------------
if (requireNamespace("pander", quietly = TRUE)) {
pander::pandoc.table(round(data.frame(
M1 = coef(m),
M2 = coef(m2),
PercentChange = coef(m2) / coef(m) * 100 - 100), 2),
justify = "left")
} else {
""
}
## ----fig.height = 10----------------------------------------------------------
## diagnostics after removing outliers from first model
md2 <- modelDiagnostics(m2, ev.perc = .05)
plot(md2, ask = FALSE, ncol = 1)
## show (new) extreme values
md2$extremeValues
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