thetalog2pop: Simulated time series from 2 theta-logistic populations
In tanyalrogers/GPEDM: Gaussian Process regression for Empirical Dynamic Modeling
thetalog2pop R Documentation
Simulated time series from 2 theta-logistic populations
Description
Contains time series simulated from the discrete time theta-logisic model with noise:
x_{t+1}=x_{t}e^{r(1-x_t^\theta)}e^{s\epsilon}
\epsilon \sim N(0,1)
PopA has \theta=3.5 and PopB has \theta=2.5.
Both have r=1.2 and s=0.05 and time series length of 50,
with random starting values and the first 200 iterates discarded.
Usage
thetalog2pop
Format
A data frame with 100 rows and 3 variables:
- Time
Time index
- Population
Population ID
- Abundance
Abundance value
Source
The simulation code is in data-raw on the GPEDM Github page.
tanyalrogers/GPEDM documentation built on June 1, 2025, 8:10 p.m.
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| thetalog2pop | R Documentation |
Simulated time series from 2 theta-logistic populations
Description
Contains time series simulated from the discrete time theta-logisic model with noise:
x_{t+1}=x_{t}e^{r(1-x_t^\theta)}e^{s\epsilon}
\epsilon \sim N(0,1)
PopA has \theta=3.5 and PopB has \theta=2.5.
Both have r=1.2 and s=0.05 and time series length of 50,
with random starting values and the first 200 iterates discarded.
Usage
thetalog2pop
Format
A data frame with 100 rows and 3 variables:
- Time
Time index
- Population
Population ID
- Abundance
Abundance value
Source
The simulation code is in data-raw on the GPEDM Github page.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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