metab | R Documentation |
Returns daily time series of gross primary production (GPP), respiration (R), and net ecosystem production (NEP). Depending on the method used, other information may be returned as well. Calculations are made using one of 5 statistical methods.
metab(data, method, wtr.name="wtr", irr.name="irr", do.obs.name="do.obs", ...)
data |
a data.frame whose columns are
Columns that are not used by a particular statistical method do not need to be supplied. |
method |
a character string specifying one of the 5 statistical methods (bayesian, bookkeep, kalman, ols, mle) |
wtr.name |
the name of the column containing temperature at the depth of do.obs (predictor variable for R) |
irr.name |
the name of the column containing irradiance (predictor variable for GPP) |
do.obs.name |
the name of the column in data containing the DO observations (in mg/L) to be used as the response variable |
... |
arguments to be passed on to the metabolism model specified by |
A data.frame containing columns for year, doy (day of year, julian day plus fraction of day), GPP, R, and NEP
year |
integer year |
doy |
numeric, day of year + fraction of day, where the day is the julian day, and a fraction of 0.5 corresponds to noon |
GPP |
numeric, gross primary production, in units of mg O2 per liter per day. By convention, this value is positive. |
R |
numeric, respiration, in units of mg O2 per liter per day. By convention, this value is negative |
NEP |
numeric, net ecosystem production, in units of mg O2 per liter per day. For most methods this equal GPP+R, but this is not necessarily the case for |
Note that different models will have different attributes attached to them. See examples.
Ryan D. Batt
Metabolism models: metab.bookkeep, metab.ols, metab.mle, metab.kalman, metab.bayesian
For smoothing noisy temperature: temp.kalman
To calculate do.sat: o2.at.sat
To calculate k.gas: k600.2.kGAS
To calculate k600 values for k.gas: k.cole, k.crusius, k.macIntyre, k.read
# fake data datetime <- seq(as.POSIXct("2014-06-16 00:00:00", tz="GMT"), as.POSIXct("2014-06-17 23:55:00", tz="GMT"), length.out=288*2) do.obs <- 2*sin(2*pi*(1/288)*(1:(288*2))+1.1*pi) + 8 + rnorm(288*2, 0, 0.5) wtr <- 3*sin(2*pi*(1/288)*(1:(288*2))+pi) + 17 + rnorm(288*2, 0, 0.15) do.sat <- LakeMetabolizer::o2.at.sat.base(wtr, 960) irr <- (1500*sin(2*pi*(1/288)*(1:(288*2))+1.5*pi) +650 + rnorm(288*2, 0, 0.25)) * ifelse(is.day(datetime, 42.3), 1, 0) k.gas <- 0.4 z.mix <- 1 # plot time series plot(wtr, type="l", xaxt="n", yaxt="n", xlab="", ylab="") par(new=TRUE); plot(do.obs, type="l", col="blue", xaxt="n", yaxt="n", xlab="", ylab="") par(new=TRUE); plot(irr, type="l", col="orange", xaxt="n", yaxt="n", xlab="", ylab="") abline(v=144, lty="dotted") abline(v=288) legend("topleft", legend=c("wtr", "do.obs", "irr"), lty=1, col=c("black", "blue", "orange"), inset=c(0.08, 0.01)) # put data in a data.frame data <- data.frame(datetime=datetime, do.obs=do.obs, do.sat=do.sat, k.gas=k.gas, z.mix=z.mix, irr=irr, wtr=wtr) # run each metabolism model m.bk <- metab(data, "bookkeep", lake.lat=42.6) m.bk <- metab(data, lake.lat=42.6) # no method defaults to "bookeep" m.ols <- metab(data, "ols", lake.lat=42.6) m.mle <- metab(data, "mle", lake.lat=42.6) m.kal <- metab(data, "kalman", lake.lat=42.6) ## Not run: m.bay <- metab(data, "bayesian", lake.lat=42.6) # example attributes names(attributes(m.ols)) attr(m.ols, "mod") # To get full JAGS model # including posterior draws: ## Not run: names(attributes(m.bay)) ## Not run: attr(m.bay, "model")
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