View source: R/request_results.R
request_results | R Documentation |
Uses StreamPULSE API to retrieve .rds
objects containing the outputs
of streamMetabolizer
runs for specific siteyears. To view available model results, see
query_available_results
. Visit the data portal at
http://data.streampulse.org:3838/streampulse_diagnostic_plots/
to visualize model results via a browser.
request_results(sitecode, year, token = NULL)
sitecode |
underscore-separated region and site code, e.g. 'NC_Eno'.
Full list of regions and site codes available at
https://data.streampulse.org/sitelist. Or, you can use the
|
year |
string or numeric representing year, e.g. '2015' or 2015. |
token |
a unique alphanumeric string for each registered user of StreamPULSE. Only necessary for accessing embargoed results. Email StreamPULSE developer Mike Vlah (vlahm13@gmail.com) to receive your token. |
Request results for a single region, site, and year. Some sites are "embargoed," meaning results from those sites are kept private and can only be accessed by authorized users. If you are authorized, you can access your embargoed results using a unique token, which currently you can only receive by emailing StreamPULSE developer Mike Vlah (vlahm13@gmail.com).
returns a list containing an unfortunately limited collection of
information about the "best" model result we have on record for the
site and year requested. The two main items in this list are
model_results
and predictions
. The former contains a subset
of the details returned by streamMetabolizer::metab
.
The latter contains the output of streamMetabolizer::predict_metab
.
Following is a bit more information about the former.
If the output of streamMetabolizer::metab
is a variable called x
, model_results
includes x@fit,
x@data, and x@data_daily. Likewise, if the output of
StreamPULSE::fit_metabolism
(which calls metab
)
is a variable called y
, model_results
includes
y$fit@fit, y$fit@data, and y$fit@data_daily.
In the future, this function may return a lot more relevant information, but at the moment only these elements are stored on our server for each "best" model run.
Model "bestness" is determined by an automatic comparison of five criteria each time a new model is fit: 1) proportion of daily GPP estimates that go negative; 2) proportion of daily ER estimates that go positive; 3) correlation between daily ER and K600; 4) maximum daily K600; and 5) temporal coverage. A score is assigned for each of these criteria, and an aggregate score is determined.
Mike Vlah, vlahm13@gmail.com
query_available_results
for determining which models
are available for download.
res = request_results(sitecode='FL_NR1000', year=2016)
res$predictions
res$model_results$fit$daily
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