Fit a metabolism model to data

Runs the metabolism model specified by the specs argument. Returns a fitted model.

Usage

metab(
  specs = specs(mm_name()),
  data = v(mm_data(NULL)),
  data_daily = v(mm_data(NULL)),
  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 specs to generate the list, because the set of required parameters and their defaults depends on the model given in the model_name argument to specs. The help file for specs lists 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

An object inheriting from metab_model and containing the fitted model. This object can be inspected with the functions in the

metab_model_interface.

Formatting data

Unit-value model inputs passed via the data argument should be formatted as a data.frame with column names and values that depend on the model type, as follows. (If all columns are optional, data may equal NULL.)

mle or night

colname	class	units	need
solar.time	POSIXct,POSIXt		required
DO.obs	numeric	mgO2 L^-1	required
DO.sat	numeric	mgO2 L^-1	required
depth	numeric	m	required
temp.water	numeric	degC	required
light	numeric	umol m^-2 s^-1	required
discharge	numeric	m^3 s^-1	optional

Example:

solar.time	DO.obs	DO.sat	depth	temp.water	light	discharge
2050-03-14 15:10:00	10.1	14.2	0.5	21.8	300.9	9

bayes

colname	class	units	need
solar.time	POSIXct,POSIXt		required
DO.obs	numeric	mgO2 L^-1	required
DO.sat	numeric	mgO2 L^-1	required
depth	numeric	m	required
temp.water	numeric	degC	required
light	numeric	umol m^-2 s^-1	required
discharge	numeric	m^3 s^-1	optional

Example:

solar.time	DO.obs	DO.sat	depth	temp.water	light	discharge
2050-03-14 15:10:00	10.1	14.2	0.5	21.8	300.9	9

Kmodel

colname	class	units	need
solar.time	POSIXct,POSIXt		optional
discharge	numeric	m^3 s^-1	optional
velocity	numeric	m s^-1	optional

Example:

solar.time	discharge	velocity
2050-03-14 15:10:00	9	2

sim

colname	class	units	need
solar.time	POSIXct,POSIXt		required
DO.obs	numeric	mgO2 L^-1	optional
DO.sat	numeric	mgO2 L^-1	required
depth	numeric	m	required
temp.water	numeric	degC	required
light	numeric	umol m^-2 s^-1	required

Example:

solar.time	DO.obs	DO.sat	depth	temp.water	light
2050-03-14 15:10:00	10.1	14.2	0.5	21.8	300.9

Formatting data_daily

Daily-value model inputs passed via the data_daily argument should be formatted as a data.frame with column names and values that depend on the model type, as follows. (If all columns are optional, data_daily may equal NULL.)

night

NULL

mle

colname	class	units	need
date	Date		optional
K600.daily	numeric	d^-1	optional
init.GPP.daily	numeric	gO2 d^-1 m^-2	optional
init.Pmax	numeric	gO2 d^-1 m^-2	optional
init.alpha	numeric	gO2 s d^-1 umol^-1	optional
init.ER.daily	numeric	gO2 d^-1 m^-2	optional
init.ER20	numeric	gO2 d^-1 m^-2	optional
init.K600.daily	numeric	d^-1	optional

Example:

date	K600.daily	init.GPP.daily	init.Pmax	init.alpha	init.ER.daily	init.ER20	init.K600.daily
2050-03-14	10	5	10	1e-04	-10	-10	10

bayes

colname	class	units	need
date	Date		optional
discharge.daily	numeric	m^3 s^-1	optional

Example:

date	discharge.daily
2050-03-14	9

Kmodel

colname	class	units	need
date	Date		required
K600.daily	numeric	d^-1	required
K600.daily.lower	numeric	d^-1	optional
K600.daily.upper	numeric	d^-1	optional
discharge.daily	numeric	m^3 s^-1	optional
velocity.daily	numeric	m s^-1	optional

Example:

date	K600.daily	K600.daily.lower	K600.daily.upper	discharge.daily	velocity.daily
2050-03-14	10	4.5	15.6	9	2

sim

colname	class	units	need
date	Date		optional
discharge.daily	numeric	m^3 s^-1	optional
DO.mod.1	numeric	mgO2 L^-1	optional
K600.daily	numeric	d^-1	optional
GPP.daily	numeric	gO2 d^-1 m^-2	optional
Pmax	numeric	gO2 d^-1 m^-2	optional
alpha	numeric	gO2 s d^-1 umol^-1	optional
ER.daily	numeric	gO2 d^-1 m^-2	optional
ER20	numeric	gO2 d^-1 m^-2	optional
err.obs.sigma	numeric	mgO2 L^-1	optional
err.obs.phi	numeric		optional
err.proc.sigma	numeric	gO2 d^-1 m^-2	optional
err.proc.phi	numeric		optional

Example:

date	discharge.daily	DO.mod.1	K600.daily	GPP.daily	Pmax	alpha	ER.daily	ER20	err.obs.sigma	err.obs.phi	err.proc.sigma	err.proc.phi
2050-03-14	9	7.5	10	5	10	1e-04	-10	-10	0.01	0	5	0

Author

Alison Appling

Examples

dat <- data_metab(num_days='3')

# fit a basic MLE model
mm <- metab(specs(mm_name('mle')), data=dat, info='my info')
predict_metab(mm)
#>         date      GPP GPP.lower GPP.upper        ER  ER.lower  ER.upper
#> 1 2012-09-18 2.812955  2.435230  3.190680 -2.103521 -2.410113 -1.796930
#> 2 2012-09-19 3.277526  2.866958  3.688094 -2.470115 -2.808856 -2.131375
#> 3 2012-09-20 2.582700  2.313734  2.851666 -1.709653 -1.914700 -1.504606
#>   msgs.fit warnings errors
#> 1                         
#> 2      W                  
#> 3                         
get_info(mm)
#> [1] "my info"
get_fitting_time(mm)
#>    user  system elapsed 
#>   2.175   0.042   2.225 

# with chaining & customization
library(dplyr)
#> ------------------------------------------------------------------------------
#> You have loaded dplyr after unitted - this is likely to cause problems.
#> If you need functions from both dplyr and unitted, please load dplyr first, then unitted:
#> library(dplyr); library(unitted)
#> ------------------------------------------------------------------------------
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
mm <- mm_name('mle', ode_method='euler') %>%
  specs(init.GPP.daily=40) %>%
  metab(data=dat)
predict_metab(mm)
#>         date      GPP GPP.lower GPP.upper        ER  ER.lower  ER.upper
#> 1 2012-09-18 2.809444  2.402234  3.216654 -2.098404 -2.427638 -1.769169
#> 2 2012-09-19 3.271351  2.838930  3.703772 -2.463138 -2.818887 -2.107388
#> 3 2012-09-20 2.568592  2.286376  2.850807 -1.697389 -1.911571 -1.483207
#>   msgs.fit warnings errors
#> 1      W                  
#> 2      W                  
#> 3      W                  
if (FALSE) {
plot_DO_preds(predict_DO(mm))
plot_DO_preds(predict_DO(mm), y_var='pctsat', style='dygraphs')
}