This package contains functions I often use when working with R. There is chance that some of them might be useful for others, hence wrapping them in a package with documentation and examples.
install.packages("devtools")
devtools::install_github("ptompalski/UsefulRFunctions")
library(UsefulRFunctions)
Function calc.error()
is useful for quickly calculating bias and RMSE (and other) between observed and predicted values. This function also check the statistical significance of the differences. For example:
ref <- iris$Sepal.Length
est <- predict(lm(data=iris,iris$Sepal.Length~iris$Petal.Width))
calc.error(reference = ref, estimate = est)
Optionally a grouping variable can be added:
grouping_var <- iris$Species
calc.error(reference = ref, estimate = est,by = grouping_var)
I recommend setting the parameter noinfo = F
during the first run and see how the absolute and relative bias are calculated.
There are two plot templates: one for scatterplot (scatter()
) and one for histogram (h()
).
Using:
ref <- iris$Sepal.Length
est <- predict(lm(data=iris,iris$Sepal.Length ~ iris$Petal.Width))
scatter(x = ref, y = est)
will produce a scatter plot with additional information (text box) on the bias and RMSE. Optionally you can disable this by setting info = F
.
The default behaviour of h()
is to plot a histogram with chosen descriptive statistics added in a corner. You can turn them off by setting info = F
. You can also very easly define the width of the histogram bins (much easier than with standard hist()
) with:
h(ref,0.1)
Both scatter()
and h()
use ggplot2 package to produce plots. The returned ggplot objects can be
further modified with additional options:
h(iris$Sepal.Length) + theme_minimal()
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