A collection of functions for use in the PASIT project group. Install
using devtools::install_github("CWWhitney/pasitR")
| Quick Links |
|---|
| Estimated yield given the expected chill |
| Chill portion intervals |
| Probability of yield given chill |
| Working with pasitR |
| pasitR Wiki |
Data comprising related observations of chill and yield are used in all functions.
Using chillscatter() create a chill portions (x) and yield (y) scatter
plot with associated and estimated densities with loess smooth linear
fits density curves. Plot made with the scatter.hist() function in the
plyr() package (Wickham 2011) in R (R Core Team 2017).
Use chillkernel() or another type of graphical representation of the
same relationship. chillkernel() performs a two-dimensional kernel
density estimation for yield and chill using the kde2d() function in
the MASS() package (Venables and Ripley 2002). The function produces a
matrix of the estimated density (z) of yield (y) and chill (x). As the
density function restricts the shape of the kernel to a bivariate normal
kernel, it looks slightly different compared to the scatter plot
estimates above.
chillkernel() shows a density surface plot of chill portions (x) and
yield (y). The legend shows the value for the estimated density (z). The
plot is made with the filled.contour() function of the graphics()
package.
In chillkernel() the density (z) over the entire plot integrates to
one, and therefore represents the relative probability of an observation
(yield along y-axis) given a specific chill portion (along x-axis).
chillkernelslice() calculates the estimated yield given the expected
chill, based on a slice of ‘z’ from the Kernel density calcualted with
chillkernel(). The expectedchill parameter is set to 30.
chillkernelslice() plots the probabilities (shown along the y-axis)
for the expected yield (shown along the x-axis). Since this is a cut
through the density kernel chillkernel(), which integrates to 1, the
probability values are relative, not absolute measures.
chillviolin() determines different possible chill portion intervals by
calculating the optimal interval width for chill portions using the
IQR() function in the stats() package, after the Freedman-Diaconis
rule (IQR = interquartile range).
Optimal interval width for our sample = 2 * interquartile range for our sample / (total number of observations for the interquartile range for our sample)^(1/3)
chillviolin() shows violin plots of chill portions (x) and yield (y)
with six different intervals of chill portions. Plot made with
ggplot2() (Wickham 2016).
chillkernelslicerange() The chill portion intervals, optimized
interquartile ranges shown in chillviolin() can be used to select a
range to slice from the density kernel chillkernel() as was doen for a
single chill value in chillkernelslice(). Here we set the maximum
chill to 20 and the minimum to 10.
chillkernelslicerange() plots the probabilities (shown along the
y-axis) for the expected yield (shown along the x-axis). As with
chillkernelslice() the probability values shown are relative, not
absolute measures. They are the result of cuts through the density
kernel chillkernel(), which integrates to 1.
Create an Rmarkdown vignette using usethis::use_vignette()
We created an online version of the vignette html by adding the prepend (http://htmlpreview.github.io/?) to the original URL http://htmlpreview.github.io/?https://github.com/hortibonn/pasitR/blob/master/vignettes/chill-yield-vignette.html
To change the default data assign the data to an r object and use
usethis::use_data(data, overwrite=TRUE)
R Core Team (2017) R: A Language and Environment for Statistical Computing [R Version 3.4.1 (2017-06-30) “Single Candle”] 3.4.1.
Venables, W. N., and B. D. Ripley (2002) Modern Applied Statistics with S. Fourth Edition. New York: Springer.
Wickham, Hadley (2011) Plyr: Tools for Splitting, Applying and Combining Data, Version 1.4. Available Online at Cran. r-Project. Elegant Graphics for Data Analysis. Springer.