**This Package is now obsolete: Everything has moved on to WSPMetrics **
This package is under development. Functions will change rapidly and substantially. Do not use these if you are not part of the development team!
- Dr. Carrie Holt (Project Lead, Carrie.Holt"AT"dfo-mpo.gc.ca)
- Gottfried Pestal (Developer, gpestal"AT"solv.ca)
To install this package directly from github, use
install.packages("devtools") # Install the devtools package
library(devtools) # Load the devtools package.
install_github("SOLV-Code/MetricsTest", dependencies = TRUE, build_vignettes = FALSE)
library(MetricsTest)
This package includes functions to calculate the metrics originally defined by Holt et al. (2009), and further refined in subsequent test cases and additional explorations.
The WSP metrics are designed to capture different considerations related to status, and have been used in combination to develop integrated status designations. Below is a brief overview with general definitions. For more detail about these metrics, alternative calculation approaches, and their use in formal status asessments, go to the WSP Metrics wiki.
| Metric | General Definition | Variations | Function |
|---|---|---|---|
| Long-Term Trend | Recent Avg / Long-term Avg | geometric mean vs. arithmetic mean | calcLongTermTrendSimple() for a single vector. calcLongTermTrend() for a data frame with Years x Stocks (includes retrospective calculations). |
| Percent Change | change over n generations | alternative smoothing (log, gen avg), time window (n gen, n gen +1) | calcPercChangeSimple() for a single vector. calcPercChange() for a data frame with Years x Stocks (includes retrospective calculations). |
| Probability of Decline | Prob(PercChange> X%) | alternative smoothing (log, gen avg), time window (n gen, n gen +1) | calcPercChangeSimple() for regression based calculation in a single vector. calcPercChange() for a data frame with Years x Stocks (includes retrospective calculations). calcPercChangeMCMC() for a Bayesian probability (based on posterior distribution of regression slopes) calculate on a single vector. |
| Relative Abundance - SR-Based BM | Recent Avg compared to biological benchmarks (Sgen, 80%Smsy) | geometric mean vs. arithmetic mean, alternative benchmark estimate approaches | first step will be to integrate a subroutine calcRickerSgen that converts Ricker parameters to benchmark estimates. For long-term, consider building a larger module calcRelAbdBM() that does the model fitting as well. |
| Relative Abd - Percentile BM | Recent Avg compared to user-specified %iles | - | To be included |
First install the package as per above.
# TEST LONG-TERM TREND AND PERC CHANGE - Simple versions for single vector
lt.trend.single <- calcLongTermTrendSimple(vec.in = as.vector(Nile) ,
gen.in = 4,min.lt.yrs = 20, avg.type = "geomean",
tracing=FALSE,
recent.excl = FALSE)
lt.trend.single
# need to address handling of large numbers
calcPercChangeSimple(vec.in = as.vector(Nile))
calcPercChangeSimple(vec.in = as.vector(Nile)/1000)
calcPercChangeSimple(vec.in = c(12,10,14,7,13,5,8,3,4,7,6,5))
# TEST LONG-TERM TREND AND PERC CHANGE - data frame version
sample.df <- data.frame(Stock1 = sample(100,40),Stock2 = sample(2000,40),Stock3 = sample(500,40))
sample.df[29,3] <- NA
lt.trend <- calcLongTermTrend(X = sample.df,gen.in = 4, recent.num.gen = 1, extra.yrs = 0,
min.lt.yrs = 20, avg.type = "geomean", tracing=FALSE,
recent.excl = FALSE)
lt.trend
perc.change <- calcPercChange(X = sample.df,gen.in = 4,
slope.num.gen = 3, extra.yrs = 0,
genmean.smoothing = TRUE,
log.transform = TRUE,
out.exp = FALSE, tracing=FALSE)
perc.change
# TEST BAYESIAN PERC CHANGE
pdf("ProbDecl_Fits.pdf",onefile=TRUE,height=8.5, width=11)
vec.use = c(12,10,14,7,13,5,8,3,4,7,6,5)
slope.mcmc.fit <- calcPercChangeMCMC(vec.in= vec.use,model.in = NULL ,
perc.change.bm = -25 , na.skip=FALSE,
out.type = "long", mcmc.plots = TRUE)
dev.off()
names(slope.mcmc.fit)
slope.mcmc.fit$pchange
slope.mcmc.fit$probdecl
slope.mcmc.fit$summary