View source: R/mastifFunctions.R
| mastVolatility | R Documentation |
Synthesis of volatility and period at the population scale.
mastVolatility( treeID, year, fec, minLength = 6, minFrequency = 1/20 )
treeID |
|
year |
|
fec |
|
minLength |
determines the minimum number of years to a tree to be included in population estimates |
minFrequency |
lowest frequency to include in volatility, period evaluation |
The three vectors treeID, year, fec are aligned by tree and year and, thus, of the same length. Tree fecundity values in the numeric vector fec can differ in number of years due to maturation times, deaths, and observation years. Trees having fewer than minLength observations are omitted from the analysis. minFrequency is high enough to omit low frequencies that are missing in the shortest series to be compared.
More detailed examples can be obtained with: browseVignettes('mastif')
Returns a list that includes stats, which holds the period- and fecundity-weighted estimates of volatility and period at the population scale. The matrix statsDensity holds the means and standard deviations by period (1/frequency). The matrix mastMatrix holds for each tree the number of years, mean log fecundity, variance, volatility, and period mean and standard deviation. Returned as tree by frequency are density and frequency.
James S Clark, jimclark@duke.edu
Qiu, T, ..., and J.S. Clark. 2023. Mutualist dispersers and the global distribution of masting: mediation by climate and fertility. in review.
mastif for analysis
A more detailed vignette is can be obtained with:
browseVignettes('mastif')
website 'http://sites.nicholas.duke.edu/clarklab/code/'.
d <- "https://github.com/jimclarkatduke/mast/blob/master/outputAbies.rdata?raw=True"
repmis::source_data( d )
# all trees in a plot:
wi <- which( fecPred$plotSpec == 'BERK28 abiesGrandis' ) # tree-years in group
tmp <- mastVolatility( treeID = fecPred$treeID[wi], year = fecPred$year[wi],
fec = fecPred$fecEstMu[wi], minLength = 10 )
period <- 1/tmp$frequency
density <- tmp$density
plot( NA, xlim = range( period, na.rm = TRUE ), ylim = range( density, na.rm = TRUE ),
xlab = 'Period (yr)', ylab = 'Density', log = 'xy' )
for( i in 1:nrow(density) )lines( period[i,], density[i, ], col = 'grey' )
lines( tmp$statsDensity['Period', ], tmp$statsDensity['Mean', ], lwd = 2 )
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