make_ccm_data: Makes fake data for other functions

In multispatialCCM: Multispatial Convergent Cross Mapping

Makes fake data for other functions

Description

Builds a fake data set of two interacting processes, based on the model in the Sugihara et al. publication below, and based on a two-species discrete-time competition model. In the model, process A is causally affected by process B, but process B is not influenced by process A.

Usage

make_ccm_data(sp_sd=0.125, obs_sd=0.025,
Sstr=0.375, times=10, burnin=100,
number_of_chains=20, seednum=2718)

Arguments

sp_sd	Standard deviation used to add process noise. If you are simulating multiple plots, this adds normally distributed noise with mean=0 to the growth rates of each species in different plots.
obs_sd	Standard deviation used to add observation error. Observation error is added to process X as a lognormal variable (X*rlnorm), with mean=0 on the lognormal scale.
Sstr	Forcing strength defining the effect of process B on process A.
times	Number of sequential observations desired for the time series in each plot (i.e., length of each independent time series)?
burnin	Burnin time before starting the experiment. This can be used to remove correlation among plots that occurs because of starting conditions.
number_of_chains	Total number of time series (i.e., how many replicates will be assembled into a single long time series?)
seednum	Random seed used for simulation.

Value

Accm	Time series for process A. Gaps between time series are indicated by a "NA" entry.
Bccm	Time series for process B. Gaps between time series are indicated by a "NA" entry.
time_ccm	Time indices corresponding to process A and B.

Author(s)

Adam Clark

References

Sugihara, G., R. May, H. Ye, C. Hsieh, E. Deyle, M. Fogarty, and S. Munch. 2012. Detecting Causality in Complex Ecosystems. Science 338.

Adam T. Clark, H. Ye, Forest Isbell, Ethan R. Deyle, Jane Cowles, David Tilman, and George Sugihara. 2015. Spatial ’convergent cross mapping’ to detect causal relationships from short time-series. Ecology, 96(6):1174–1181.

See Also

CCM_boot, SSR_pred_boot, SSR_check_signal, ccmtest

Examples

#Simulate data to use for multispatial CCM test
#See function for details - A is causally forced by B,
#but the reverse is not true.
ccm_data_out<-make_ccm_data()
Accm<-ccm_data_out$Accm
Bccm<-ccm_data_out$Bccm

multispatialCCM documentation built on Oct. 23, 2023, 1:06 a.m.