model_gamm accounts for temporal autocorrelation (TAC) in the time series
by fitting Generalized Additive Mixed Models (GAMMs) that include AR or ARMA
correlation structures using the
gamm function. The GAMMs
are applied to all IND~pressure combinations provided as input or only those
with significant TAC in the GAM residuals (using filter argument).
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The output tibble of the
Choice of knots (for the smoothing function
A description of the error distribution and link to be used in the GAM.
This needs to be defined as a family function (see also
A list of values identified as outliers in specific
IND~pressure GAMMs, which should be excluded in this modeling step
(the output tibble of this function includes the variable
'pres_outlier', which is a column-list containing
all indices of values with cook's distance > 1 (see below). The function
can be re-run again, then excluding all these outliers provided in
logical; a filter used to select specific rows in init_tbl
(row gets selected if value TRUE). That could be the
A specification list of the control values for the lme fit.
The default is NULL so the control settings in
Modeling first-differenced indicator time series can be an alternative solution to avoid temporal dependence between observations. However, this approach does often not help reducing the significant auto-correlation while GAMMs do as found in Otto et al. (2018). Such an extension implies that the single elements of the response variable are not independent anymore and that the correlation between the residuals at time t1 and t2 only depends on their time difference t1 – t2 (Wood, 2006).
model_gamm six GAMMs are computed for each filtered IND~pressure pair, i.e.
no correlation structure (for AIC comparison)
auto-regressive error structure of order p=1 (AR1)
auto-regressive error structure of order p=2 (AR2)
auto-regressive moving average of order p=1 and q=1 (ARMA11)
auto-regressive moving average of order p=1 and q=2 (ARMA12)
auto-regressive moving average of order p=2 and q=1 (ARMA21)
Returns a model output tibble that contains for each filtered IND~pressure pair
6 rows with the individual GAMM outputs. The structure remains the same as in
model_gam except for the explained deviance, which is not computed
by the gamm function.
The selection of the final correlation structure for each IND~pressure model can be
done manually on this tibble or with an automatized routine using
Otto, S.A., Kadin, M., Casini, M., Torres, M.A., Blenckner, T. (2018) A quantitative framework for selecting and validating food web indicators. Ecological Indicators, 84: 619-631, doi: https://doi.org/10.1016/j.ecolind.2017.05.045
Wood, S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press
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# Using the Baltic Sea demo data in this package dat_init <- ind_init( ind_tbl = data.frame(Cod = ind_ex$Sprat), press_tbl = press_ex[, c("Fsprat", "Fher")], time = ind_ex[ ,1]) gam_tbl <- model_gam(dat_init) # Any temporal autocorrelation gam_tbl$tac # Applying model_gamm function and passing the $tac variable as filter gamm_tbl <- model_gamm(dat_init, filter = gam_tbl$tac) # Use different control values for lme fit: gamm_tbl <- model_gamm( dat_init, filter = gam_tbl$tac, lme_control = nlme::lmeControl(niterEM = 100, msMaxIter = 200) ) # run analysis will full demo dataset gam_tbl <- model_gam(ind_init_ex) gamm_tbl <- model_gamm(ind_init_ex, filter = gam_tbl$tac) # now run the same analysis but exclude all outliers in the GAMMs gamm_tbl2 <- model_gamm(ind_init_ex, filter = gam_tbl$tac, excl_outlier = gamm_tbl$pres_outlier)
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