Datasets for "Meteorological factors associated with the timing and abundance of Hymenoscyphus fraxineus spore release" by Burns, Timmermann and Yearsley.

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# Data Files:

File: burns_etal_preprocessed_data.Rdata

        This file contains the pre-processed spore count data and the cleaned meteorological data
        The file contains:

stations            The longitude and latitude of the two weather stations used for the metro data
varStr_mean    Names of the meteorological variables
windowStr        Names of the three time windows

emission                  The main data frame containing the spore and meteorological data
     date                     Date of a spore count recording. (POSIXlt)
     year                     Year of spore count recording
     month                   Month of spore count recording 
     day                       Day of year of spore count recording
     total                      The total daily spore count
     peak                     The maximum spore count each day    
     peak_time            The time (hours after midnight) of the maximum spore count each day
     peak_time_raw    Raw value for time of maximum spore count each day
     peak_time_date   Date and time (POSIXct) for maximum spore count each day
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File: results_burns_etal_daily_emission_analysis_2010_2011_prop0.8.Rdata

        This file gives the results for the total daily emission of spores

File: results_burns_etal_daily_peaktime_analysis_2010_2011_prop0.8.Rdata

        This file gives the results for the time of the daily per in spore counts

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Both files have the same variables, which are listed below.

# Setup parameters
use.prop                  Proportion of the data to use for fitting model
colinear_threshold  The correlation threshold to identify collinear covariates
frost_var                   The name of the variable to use as a frost covariate (three possible windows)
k.use                        The dimension of the basis for the smoothing thin-plate splines in the GAM
nIter                          Number of Monte-Carlo random subsamples of the data
seed                         The random number seed at the start of the analysis
years                        The years of data to use for fitting the GAM models.
                                 Leaving a year out allows it to be used as independent validation data

# Outputs from the analysis
var.use              The names of covariate used in the final analysis after removing collinear covariates
models              A list (of length nIter) giving all the fitted models
d                        A data frame with a summary of the nIter model results.
                          There are nIter rows. Each row summarises the results from one GAM
    The data frame contains:
      r2                r-squared between the model and the validation data.
                         Validation data are the (1-use.prop) proportion not used for fitting
      r2_fitted.     r-squared for the data used to fit the model
      dev.exp.      The explained deviance from the fitted GAM
      nTerm.        The number of smooth terms in the fitted GAM
      term1          The smooth term with the smallest p-value (number is an index for var.use)
      term2          The smooth term with the second smallest p-value (number is an index for var.use)
      term3          The smooth term with the third smallest p-value (number is an index for var.use)
      termF          The smooth term with the largest F-value (number is an index for var.use)
pValues             p values for each of the smooth terms (columns) for each of the nIter models (rows)
FValues             F values for each of the smooth terms (columns) for each of the nIter models (rows)
edf                     Estimated degrees of freedom for each of the smooth terms (columns) for each of the nIter models (rows)
pValue_param   p values for each of the parametric terms (columns) in each of nIter models (rows)
tVal_param.       t statistics for each of the parametric terms (columns) in each of nIter models (rows)