gamTest {baytrends} | R Documentation |
Perform GAM analysis. Relies on mgcv::gam to perform general additive model.
gam
The baseDay function has been added to this package from the smwrBase package.
gamTest( df, dep, stat, layer = NA, analySpec, gamTable = TRUE, gamPlot = 10, gamDiffModel = NA, flow.detrended = NA, salinity.detrended = NA )
df |
data frame |
dep |
dependent variable |
stat |
station |
layer |
layer |
analySpec |
analytical specifications |
gamTable |
gam table setting (set to FALSE to turn off table output) |
gamPlot |
gam plot setting (set to FALSE to turn off plotting) |
gamDiffModel |
GAM model(s) used for computing differences on sub-annual/multi-period basis |
flow.detrended |
data generated by detrended.flow. Default = NA. |
salinity.detrended |
data generated by detrended.flow. Default = NA. |
Set gamPlot=FALSE
to turn off plotting. Computing the information
("predictions") to create plots is one of the more time consumings aspects of
the gamTest
function. Setting gamPlot=FALSE
turns off these
computations and speeds up gamTest
. The disadvantage is that no
predictions are returned; however, the tabularized results stored in
stat.gam.result
and, if requested, chng.gam.result
are still
returned.
Setting gamPlot
to a value between 1-30 changes the resolution of the
resulting figure by setting the interval on which the prediction data set is
made. By default gamPlot
is set to 10. That is, a prediction is made
every 10th day, or about 36 predictions per year. Values closer to 1 result
in larger returned prediction data sets and take more computation time.
Values closer to 30 result in smaller returned data sets and take less
computation time. Although there is no change in the fitted model, values
closer to 30 may have slight degraded figure quality if there is subtantial
seasonality in the fitted model since the seasonal minimum and maximum might
not be included in the prediction data set and therefore not plotted. Values
greater than 30 are treated as 30. Setting gamPlot=30
might be
advantangeous when the analysis only requires cursory figure examination.
Setting gamTable=FALSE
will turn off table output to the console. This
may be advantageous to reduce the amount of output. Since these computations
do not significantly affect gamTest
run time, the standard Analysis of
Variance, GAM Parameter Coefficients, Diagnostics, and Estimates of Change
tables are returned from gamTest
regardless of the gamTable
setting. Many of the values from these tables are also returned as part of
tabularized stat.gam.result
.
The default settings for gamDiffModel
(i.e., gamDiffModel=NA
)
will not result in sub-annual (i.e., seasonal) differences being computed. In
this default setting, the returned chng.gam.result
that is returned
from gamTest
will be empty. If gamDiffModel
is a value (i.e.,
not NA
), then chng.gam.result
will include one row for each
combination of years specified in analySpec$gamDiffPeriods
, seasons
specified in analySpec$gamDiffSeason
, and the number of models listed
in gamDiffModel
. For example gamDiffModel=c(0,1,2)
would
result in sub-annual being computed for gam0, gam1, and gam2.
Flow and Salinity Adjustments (gam4). It is necessary to create and
pass properly formatted data via the flow.detrended
and
salinity.detrended
arguments to evaluate gam4 models. See
detrended.flow
and detrended.salinity
for more
information on how to create properly formatted data.
Returns a list with results
stat.gam.result – Data frame with summary results in a tabular format suitable for output to a csv file
chng.gam.result – Data frame with estimate of change for sub-annual differences based on the
settings specified in gamDiffModel
. Includes all data from stat.gam.result and sub-annual estimates of change
in a tabular format suitable for output to a csv file.
data – Data frame of data used in analysis (for example years with large proportion of censored data might be removed from the analysis and not included in this data frame)
data.all – Data frame of all potential data available for analysis
iSpec – List of baytrends analysis settings used for a specific analysis of station and parameter
gamOutput0 – Results including model predictions for gam formula 0 (i.e., gam0)
gamOutput1 – Results including model predictions for gam formula 1 (i.e., gam1)
gamOutput2 – Results including model predictions for gam formula 2 (i.e., gam2)
gamOutput3 – Results including model predictions for gam formula 3 (i.e., gam3)
gamOutput4 – Results including model predictions for gam formula 4 (i.e., gam4)
gamOutput5 – Results including model predictions for gam formula 5 (i.e., gam5)
gamOutput6 – Results including model predictions for gam formula 6 (i.e., gam6)
gamOutput* – For each evaluated model, gamOutput*
(see above
element list) is a list with the following elements:
gamOption – gam formula ID, i.e., 0, 1, 2, 3, 4 , 5, 6 corresponding to gam0, gam1, gam2, etc.
gamRslt – mgcv::gam output
gamRsltSum – "summary" of mgcv::gam, i.e., summary(mgcv::gam)
gamANOVAtbl – GAM Analysis of Variance table
gamCoefftbl – GAM Parameter Coefficients table
gamDiagnostics – GAM Diagnostics table(AIC, RMSE, and Adj. R-squared)
perChange – Estimates of Change table
porDiff.regular – Estimate of change in list format
porDiff.adjusted – Estimate of change adjusted for interventions in list format
predictions – Data frame of gam predictions (all information used to create baytrends graphics excluding measurements)
## Not run: # Specify parameter and station to analyze dep <- 'do' stat <- 'CB5.4' layer <- 'B' # Prepare data and set up specifications for analysis dfr <- analysisOrganizeData (dataCensored) df <- dfr[[1]] analySpec <- dfr[[2]] # Apply gamTest gamResult <- gamTest(df, dep, stat, layer, analySpec=analySpec) gamPlotDisp(gamResult = gamResult, analySpec = analySpec, fullModel = 2, seasAvgModel = 2, seasonalModel = 2, diffType = "regular", obserPlot = TRUE, interventionPlot = TRUE, seasAvgPlot = TRUE, seasAvgConfIntPlot = FALSE, seasAvgSigPlot = FALSE, fullModelPlot = TRUE, seasModelPlot = TRUE, BaseCurrentMeanPlot = FALSE, adjustedPlot = FALSE) # Apply gamTestSeason gamResult2 <- gamTestSeason(df, dep, stat, layer, analySpec=analySpec, gamSeasonPlot = c("7/15-8/15", "purple", "range")) gamPlotDispSeason(gamResult = gamResult2, analySpec = analySpec, fullModel = 2, seasAvgModel = 2, seasonalModel = 2, diffType = "regular", obserPlot = TRUE, interventionPlot = TRUE, seasAvgPlot = TRUE, seasAvgConfIntPlot = FALSE, seasAvgSigPlot = FALSE, fullModelPlot = FALSE, seasModelPlot = FALSE, BaseCurrentMeanPlot = TRUE, adjustedPlot = FALSE, gamSeasonFocus = TRUE) ## End(Not run)