sync.trend: Calculate temporal trends of synchrony
In DendroSync: A Set of Tools for Calculating Spatial Synchrony Between Tree-Ring Chronologies

sync.trend

R Documentation

Calculate temporal trends of synchrony

Description

The function calculates temporal trends of spatial synchrony from a data.frame with tree-ring width chronologies using a moving window as described in Shestakova et al. (2016). This method splits the time variable (varTime) in 30 years windows plus a 5 years lag, and in each window the within- or between-group level (varGroup) synchronies are calculated. The function can also be used to find synchrony with similar time series data.frame from other fields.

Usage

sync.trend (formula, varTime="", varGroup="", data,  window = 30, lag = 5, 
                    null.mod = TRUE, selection.method = c("AIC", "AICc", "BIC"), 
                    all.mod = FALSE, homoscedastic = TRUE, between.group = FALSE)

Arguments

`formula`	a `formula` a typical model formula such as `Y ~ A`, where `Y` is usually tree-ring width and `A` may be a grouping factor such as the specific names of tree-ring width chronologies (`conifersIP`).
`varTime`	a `character` specifying the time variable to consider in calculating synchrony estimates. Models with less than 10 different time-points may produce unreliable results.
`varGroup`	a `character` grouping variable. In dendrochronological studies different grouping strategies can be used. We used here two strategies following taxonomic (i.e. species) or geographic (i.e. region) criteria.
`data`	a `data.frame` with tree-ring chronologies, years and grouping variables as columns.
`window`	an `integer` specifying the window size (i.e. number of years) to be used to calculate synchrony. Must be greater than 20 (>=20). Defaults to 20.
`lag`	an `integer` specifying the lag that the window is moving (i.e. number of vrTirs moving window) to be used to calculate synchrony. Must be greater than 1 (>=1). Defaults to 5.
`null.mod`	a `logical` specifying if only the null model for general synchrony is fitted (broad evaluation, mBE). Default `TRUE`.
`selection.method`	a `character` string of `"AIC"`, `"AICc"` or `"BIC"`, specifying the information criterion used for model selection.
`all.mod`	a `logical` specifying if all homoscedastic and heteroscedastic models should be fitted. Default `FALSE`.
`homoscedastic`	a `logical` specifying if models should be an optional `varFunc` object or one-sided formula describing the within-group heteroscedasticity structure. Default `TRUE`
`between.group`	a `logical` specifying if between-group synchrony is displayed instead of whitin-group synchrony. Default `FALSE`.

Details

The function fits by default ("null.mod=T") the null model for general synchrony (broad evaluation, mBE) for a specified time window size and lag. If "null.mod=F" the function calculates homoscedastic or heteroscedastic versions of variance-covariance (VCOV) mixed models available (mBE, mNE, mCS, mUN, mHeNE, mHeCS, mHeUN; dendro.varcov) for each time window size and lag selected. In each window the best model is chosen based on the minimum information criterion selected between "AIC", "AICc" or "BIC". When no selection.method is defined by default AIC is used. If "all.mod=T" the functions fits the homoscedastic and heteroscedastic versions of the 7 models (this is a higly time consuming process).

Value

The function returns a data.frame containing the following components:

for null.mod TRUE:

`a_Group`	a column representing the within-group synchrony (mBE).
`SE`	standard error of each observation.
`Windlag`	a column representing the lag of the window used to split the time variable. A 0 value means that lag is 0, and then the defined time window starts from minimun varTime value.
`varTime`	a column representing the `varTime` variable.

for null.mod FALSE:

`Modname`	a column indicating the best model fit and the information criterion used.
`GroupName`	a column indicating levels of the `varGroup` for each time-window selected.
`a_Group`	a column indicating within-group synchrony for each `varGroup` level at time-window selected.
`a_betw_Grp`	a column indicating between-group synchrony for each `varGroup` level at time-window selected. Only if `between.group` is set to `TRUE`.
`SE`	standard error of each observation.
`Windlag`	a column representing the lag of the window used to split the time variable. A 0 value means that lag is 0, and then the defined time window starts from minimun varTime value.
`varTime`	a column representing the `varTime` variable window mean point.

Author(s)

Josu G. Alday, Tatiana A. Shestakova, Victor Resco de Dios, Jordi Voltas

References

Shestakova, T.A., Aguilera, M., Ferrio, J.P., Gutierrez, E. & Voltas, J. (2014). Unravelling spatiotemporal tree-ring signals in Mediterranean oaks: a variance-covariance modelling approach of carbon and oxygen isotope ratios. Tree Physiology 34: 819-838.

Shestakova, T.A., Gutierrez, E., Kirdyanov, A.V., Camarero, J.J., Genova, M., Knorre, A.A., Linares, J.C., Resco de Dios, V., Sanchez-Salguero, R. & Voltas, J. (2016). Forests synchronize their growth in contrasting Eurasian regions in response to climate warming. Proceedings of the National Academy of Sciences of the United States of America 113: 662-667.

Examples

## Calculate  temporal trends of spatial synchrony for conifersIP data:
 data(conifersIP)
 
 ##Fit the null.model temporal trend (mBE) 
 #using taxonomic grouping criteria (i.e. Species)
 mBE.trend <- sync.trend(TRW ~ Code, varTime = "Year", varGroup = "Species", 
                          data = conifersIP, null.mod = TRUE, window = 30, lag = 5)
 
 mBE.trend# it returns a data.frame

## Not run:  
 ##Fit homoscedastic within-group trends (mBE, mNE, mCS, mUN) 
 # using geographic grouping criteria (i.e. Region)
 geo.trend <- sync.trend(TRW ~ Code, varTime = "Year", varGroup = "Region", 
                         data = conifersIP, window = 30, lag = 5, 
                         null.mod = FALSE, homoscedastic = TRUE)
                         
 geo.trend#a data.frame with varGroup syncrony for each time window.
 
 ##Fit heteroscedastic between-group trends (mBE, mHeNE, mHeCS, mHeUN) 
 #using geographic grouping criteria (i.e. Region) and BIC
 geo.het.trend <- sync.trend(TRW ~ Code, varTime = "Year", varGroup = "Region", 
                             data = conifersIP, window = 30, lag = 5, null.mod = FALSE, 
                             selection.method = c("BIC"), homoscedastic = FALSE, 
                             between.group = TRUE)
 geo.het.trend
 
 ##Fit homoscedastic and heterocedastic within-group trends
 # using taxonomic grouping criteria (i.e. Species) and BIC
 geo.tot.trend <- sync.trend(TRW ~ Code, varTime = "Year", varGroup = "Species", 
                             data = conifersIP, window = 30, lag = 5, 
                             selection.method = c("BIC"), all.mod = TRUE)
 geo.tot.trend
 
## End(Not run)

DendroSync documentation built on May 28, 2022, 1:22 a.m.