Uses maximum likelihood to fit a model to estimate GPP and ER from input data on DO, temperature, light, etc. Discharge is only used, if at all, to identify and exclude days with any negative discharge.
Usage
metab_mle(
specs = specs(mm_name("mle")),
data = mm_data(solar.time, DO.obs, DO.sat, depth, temp.water, light, discharge,
optional = "discharge"),
data_daily = mm_data(date, K600.daily, init.GPP.daily, init.Pmax, init.alpha,
init.ER.daily, init.ER20, init.K600.daily, optional = "all"),
info = NULL
)Arguments
- specs
a list of model specifications and parameters for a model. Although this may be specified manually (it's just a list), it is easier and safer to use
specsto generate the list, because the set of required parameters and their defaults depends on the model given in themodel_nameargument tospecs. The help file forspecslists the necessary parameters, describes them in detail, and gives default values.- data
data.frame (not a tbl_df) of input data at the temporal resolution of raw observations (unit-value). Columns must have the same names, units, and format as the default. The solar.time column must also have a timezone code ('tzone' attribute) of 'UTC'. See the 'Formatting
data' section below for a full description.- data_daily
data.frame containing inputs with a daily timestep. See the 'Formatting
data_daily' section below for a full description.- info
any information, in any format, that you would like to store within the metab_model object
Value
A metab_mle object containing the fitted model. This object can be
inspected with the functions in the metab_model_interface.
The code column in get_fit(mm) is defined in the Value
subsection of ?nlm.
See also
Other metab_model:
metab_Kmodel,
metab_bayes,
metab_night,
metab_sim
Examples
dat <- data_metab('3','30')
# PRK
mm <- metab_mle(data=dat)
predict_metab(mm)
#> date GPP GPP.lower GPP.upper ER ER.lower ER.upper
#> 1 2012-09-18 2.816207 1.894260 3.738153 -2.116196 -2.867236 -1.365155
#> 2 2012-09-19 3.287146 2.265982 4.308309 -2.479248 -3.322547 -1.635950
#> 3 2012-09-20 2.599133 1.935957 3.262309 -1.718192 -2.224222 -1.212162
#> msgs.fit warnings errors
#> 1
#> 2
#> 3
# PR with fixed K on two days
dat_daily <- data.frame(date=as.Date(c("2012-09-18","2012-09-20")), K600.daily=35)
metab_mle(data=dat, data_daily=dat_daily)
#> metab_model of type metab_mle
#> streamMetabolizer version 0.12.1
#> Specifications:
#> model_name m_np_oi_tr_plrckm.nlm
#> day_start 4
#> day_end 28
#> day_tests full_day, even_timesteps, complete_data, pos_discharge, p...
#> required_timestep NA
#> init.GPP.daily 8
#> init.ER.daily -10
#> init.K600.daily 10
#> Fitting time: 1.21 secs elapsed
#> Parameters (3 dates):
#> date GPP.daily GPP.daily.lower GPP.daily.upper ER.daily
#> 1 2012-09-18 3.202406 3.018387 3.386425 -2.419569
#> 2 2012-09-19 3.287146 2.265982 4.308309 -2.479248
#> 3 2012-09-20 3.214161 3.042628 3.385693 -2.158125
#> ER.daily.lower ER.daily.upper K600.daily K600.daily.lower K600.daily.upper
#> 1 -2.691317 -2.147821 35.00000NA NA NA
#> 2 -3.322547 -1.635950 33.31607 23.82513 42.80701
#> 3 -2.413318 -1.902933 35.00000NA NA NA
#> msgs.fit
#> 1
#> 2 W
#> 3
#> Fitting warnings:
#> 1 date: data_daily$K600.daily==NA so fitting by MLE
#> Predictions (3 dates):
#> date GPP GPP.lower GPP.upper ER ER.lower ER.upper
#> 1 2012-09-18 3.202406 3.018387 3.386425 -2.419569 -2.691317 -2.147821
#> 2 2012-09-19 3.287146 2.265982 4.308309 -2.479248 -3.322547 -1.635950
#> 3 2012-09-20 3.214161 3.042628 3.385693 -2.158125 -2.413318 -1.902933
#> msgs.fit msgs.pred
#> 1
#> 2 W
#> 3
# PRK with day-specific inits on some days
dat_daily <- data.frame(date=as.Date("2012-09-19"),
init.GPP.daily=4, init.K600.daily=60)
metab_mle(data=dat, data_daily=dat_daily)
#> metab_model of type metab_mle
#> streamMetabolizer version 0.12.1
#> Specifications:
#> model_name m_np_oi_tr_plrckm.nlm
#> day_start 4
#> day_end 28
#> day_tests full_day, even_timesteps, complete_data, pos_discharge, p...
#> required_timestep NA
#> init.GPP.daily 8
#> init.ER.daily -10
#> init.K600.daily 10
#> Fitting time: 1.171 secs elapsed
#> Parameters (3 dates):
#> date GPP.daily GPP.daily.lower GPP.daily.upper ER.daily
#> 1 2012-09-18 2.816207 1.894260 3.738153 -2.116196
#> 2 2012-09-19 3.287148 2.269303 4.304993 -2.479250
#> 3 2012-09-20 2.599133 1.935957 3.262309 -1.718192
#> ER.daily.lower ER.daily.upper K600.daily K600.daily.lower K600.daily.upper
#> 1 -2.867236 -1.365155 31.06500 21.81770 40.31231
#> 2 -3.319998 -1.638502 33.31609 23.85389 42.77829
#> 3 -2.224222 -1.212162 28.73156 22.12035 35.34276
#> msgs.fit
#> 1 W
#> 2
#> 3 W
#> Fitting warnings:
#> 2 dates: data_daily$init.GPP.daily==NA so using specs
#> 2 dates: data_daily$init.K600.daily==NA so using specs
#> Predictions (3 dates):
#> date GPP GPP.lower GPP.upper ER ER.lower ER.upper
#> 1 2012-09-18 2.816207 1.894260 3.738153 -2.116196 -2.867236 -1.365155
#> 2 2012-09-19 3.287148 2.269303 4.304993 -2.479250 -3.319998 -1.638502
#> 3 2012-09-20 2.599133 1.935957 3.262309 -1.718192 -2.224222 -1.212162
#> msgs.fit msgs.pred
#> 1 W
#> 2
#> 3 W
# Nonlinear GPP or ER equations
metab_mle(specs(mm_name('mle', GPP_fun='satlight')), data=dat)
#> metab_model of type metab_mle
#> streamMetabolizer version 0.12.1
#> Specifications:
#> model_name m_np_oi_tr_psrckm.nlm
#> day_start 4
#> day_end 28
#> day_tests full_day, even_timesteps, complete_data, pos_discharge, p...
#> required_timestep NA
#> init.Pmax 10
#> init.alpha 1e-04
#> init.ER.daily -10
#> init.K600.daily 10
#> Fitting time: 1.799 secs elapsed
#> Parameters (3 dates):
#> date Pmax Pmax.lower Pmax.upper alpha alpha.lower
#> 1 2012-09-18 6.109019 5.645571 6.572467 0.008351642 0.007714609
#> 2 2012-09-19 7.286736 6.537937 8.035535 0.009177263 0.008270869
#> 3 2012-09-20 6.269003 5.586875 6.951132 0.007380747 0.006604585
#> alpha.upper ER.daily ER.daily.lower ER.daily.upper K600.daily
#> 1 0.008988675 -1.958642 -2.091696 -1.825588 24.80398
#> 2 0.010083657 -2.260356 -2.468652 -2.052060 26.65519
#> 3 0.008156909 -1.685959 -1.851738 -1.520180 24.46159
#> K600.daily.lower K600.daily.upper msgs.fit
#> 1 23.32547 26.28248
#> 2 24.52053 28.78984
#> 3 22.48988 26.43329
#> Predictions (3 dates):
#> date GPP GPP.lower GPP.upper ER ER.lower ER.upper
#> 1 2012-09-18 2.495253 NA NA -1.958642 -2.091696 -1.825588
#> 2 2012-09-19 2.892568 NA NA -2.260356 -2.468652 -2.052060
#> 3 2012-09-20 2.422478 NA NA -1.685959 -1.851738 -1.520180
#> msgs.fit msgs.pred
#> 1
#> 2
#> 3
metab_mle(specs(mm_name('mle', ER_fun='q10temp')), data=dat)
#> metab_model of type metab_mle
#> streamMetabolizer version 0.12.1
#> Specifications:
#> model_name m_np_oi_tr_plrqkm.nlm
#> day_start 4
#> day_end 28
#> day_tests full_day, even_timesteps, complete_data, pos_discharge, p...
#> required_timestep NA
#> init.GPP.daily 8
#> init.ER20 -10
#> init.K600.daily 10
#> Fitting time: 1.396 secs elapsed
#> Parameters (3 dates):
#> date GPP.daily GPP.daily.lower GPP.daily.upper ER20 ER20.lower
#> 1 2012-09-18 2.056615 1.532462 2.580769 -2.704649 -3.453849
#> 2 2012-09-19 2.438948 1.833218 3.044678 -3.149906 -4.011733
#> 3 2012-09-20 2.097309 1.653788 2.540831 -2.377270 -2.962660
#> ER20.upper K600.daily K600.daily.lower K600.daily.upper msgs.fit
#> 1 -1.955450 22.41849 17.33170 27.50528
#> 2 -2.288080 24.24939 18.82827 29.67052
#> 3 -1.791879 22.85723 18.55131 27.16315
#> Predictions (3 dates):
#> date GPP GPP.lower GPP.upper ER ER.lower ER.upper msgs.fit
#> 1 2012-09-18 2.056615 1.532462 2.580769 -1.546919 NA NA
#> 2 2012-09-19 2.438948 1.833218 3.044678 -1.836107 NA NA
#> 3 2012-09-20 2.097309 1.653788 2.540831 -1.386761 NA NA
#> msgs.pred
#> 1
#> 2
#> 3
if (FALSE) {
plot_DO_preds(predict_DO(mm))
}