Site elevation from PRISM elevation data, mįinal region assignment for the site in the regional frequency analysis Normal average wintertime temperature from PRISM, ☌ Normal total wintertime precipitation from PRISM, mm Mean annual maximum 3-day wintertime precipitation ( L-mean), inĬoefficient of L-variation for annual maximum 3-day wintertime precipitationĬoefficient of L-skewness for annual maximum 3-day wintertime precipitationĬoefficient of L-kurtosis for annual maximum 3-day wintertime precipitation ![]() ![]() For this workshop we will be working with two variables, which are in bold. The fifteen variables contained in this dataframe are shown below. Using PRISM as the basis for the regression means that the predicted variable can be estimated almost anywhere. ![]() PRISM climate normals provide a spatially-continuous map of long-term averages of meteorological variables such as temperature, precipitation, etc. This allows for estimation of the mean annual maximum precipitation anywhere the seasonal total precipitation can be estimated. In regional precipitation-frequency analysis, it is common to try to predict the mean annual maximum precipitation at the study sites using annual or seasonal total precipitation as a predictor. 295 stations were analyzed and properties of their annual maximum precipitation as well as characteristics of the location of the station were placed in a data file. The data you have been provided in "stations.csv" are from a precipitation-frequency analysis for 3-day maximum wintertime precipitation in the Pacific Northwest. The data required to start this workshop can be downloaded here: stations.csv Background If at any time you need a refresher on working with R commands or on how to use RStudio, feel free to look back at the R introduction or the RStudio introduction sections. In this part of the workshop, you will load a data file into a data frame, perform a regression analysis, and look at some of the diagnostics that you can produce in R for linear regression. Do not copy, forward, or release the information without United States Army Corps of Engineers approval. Hope it helps!Ĭongratulations, you can now add the regression line equation and several measures to your ggplot2 visualizations.Disclaimer: The United States Army Corps of Engineers has granted access to these data for instructional purposes only. If you simply need an introduction into R, and less into the Data Science part, I can absolutely recommend this book by Richard Cotton. rr.label.)) +īy the way, if you’re having trouble understanding some of the code and concepts, I can highly recommend “An Introduction to Statistical Learning: with Applications in R”, which is the must-have data science bible. Stat_regline_equation(label.y = 350, aes(label =. Stat_regline_equation(label.y = 400, aes(label =. For every subset of your data, there is a different regression line equation and accompanying measures. BIC.label.: BIC for the fitted model.īy the way, you can easily use the measures from ggpubr in facets using facet_wrap() or facet_grid(). adj.rr.label.: Adjusted R2 of the fitted model as a character string to be parsed ![]() rr.label.: R2 of the fitted model as a character string to be parsed eq.label.: equation for the fitted polynomial as a character string to be parsed Here are the other measures you can access:
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