DOI: 10.5281/zenodo.4467823
title: Datasets for "Meteorological factors associated with the timing and abundance of Hymenoscyphus fraxineus spore release" by Burns, Timmermann and Yearsley.

description: ======++++++++++++++++++++++++++++++==============<br> <br> # Data Files:<br> <br> File: burns_etal_preprocessed_data.Rdata<br> <br> This file contains the pre-processed spore count data and the cleaned meteorological data<br> The file contains:<br> <br> stations The longitude and latitude of the two weather stations used for the metro data<br> varStr_mean Names of the meteorological variables<br> windowStr Names of the three time windows<br> <br> emission The main data frame containing the spore and meteorological data<br> date Date of a spore count recording. (POSIXlt)<br> year Year of spore count recording<br> month Month of spore count recording <br> day Day of year of spore count recording<br> total The total daily spore count<br> peak The maximum spore count each day <br> peak_time The time (hours after midnight) of the maximum spore count each day<br> peak_time_raw Raw value for time of maximum spore count each day<br> peak_time_date Date and time (POSIXct) for maximum spore count each day<br> ===================================================<br> <br> File: results_burns_etal_daily_emission_analysis_2010_2011_prop0.8.Rdata<br> <br> This file gives the results for the total daily emission of spores<br> <br> File: results_burns_etal_daily_peaktime_analysis_2010_2011_prop0.8.Rdata<br> <br> This file gives the results for the time of the daily per in spore counts<br> <br> ================<br> Both files have the same variables, which are listed below.<br> <br> # Setup parameters<br> use.prop Proportion of the data to use for fitting model<br> colinear_threshold The correlation threshold to identify collinear covariates<br> frost_var The name of the variable to use as a frost covariate (three possible windows)<br> k.use The dimension of the basis for the smoothing thin-plate splines in the GAM<br> nIter Number of Monte-Carlo random subsamples of the data<br> seed The random number seed at the start of the analysis<br> years The years of data to use for fitting the GAM models.<br> Leaving a year out allows it to be used as independent validation data<br> <br> # Outputs from the analysis<br> var.use The names of covariate used in the final analysis after removing collinear covariates<br> models A list (of length nIter) giving all the fitted models<br> d A data frame with a summary of the nIter model results.<br> There are nIter rows. Each row summarises the results from one GAM<br> The data frame contains:<br> r2 r-squared between the model and the validation data.<br> Validation data are the (1-use.prop) proportion not used for fitting<br> r2_fitted. r-squared for the data used to fit the model<br> dev.exp. The explained deviance from the fitted GAM<br> nTerm. The number of smooth terms in the fitted GAM<br> term1 The smooth term with the smallest p-value (number is an index for var.use)<br> term2 The smooth term with the second smallest p-value (number is an index for var.use)<br> term3 The smooth term with the third smallest p-value (number is an index for var.use)<br> termF The smooth term with the largest F-value (number is an index for var.use)<br> pValues p values for each of the smooth terms (columns) for each of the nIter models (rows)<br> FValues F values for each of the smooth terms (columns) for each of the nIter models (rows)<br> edf Estimated degrees of freedom for each of the smooth terms (columns) for each of the nIter models (rows)<br> pValue_param p values for each of the parametric terms (columns) in each of nIter models (rows)<br> tVal_param. t statistics for each of the parametric terms (columns) in each of nIter models (rows)<br>