treeclim (version 1.0.7)

g_test: Gershunov test for spurious low-frequency modulations

Description

This function provides a test to decide whether low-frequency modulations in the relationship between climate and tree-growth are significantly stronger or weaker than could be expected by chance.

Usage

g_test(x, boot = FALSE, ci = 0.05, sb = TRUE)

Arguments

x

an object of class '"tc_dcc"' as returned from a call to dcc with moving correlations enabled

boot

logical shall the individual correlation be bootstrapped? (see details)

ci

Significance level for testing, one of c(0.01, 0.05, 0.1)

sb

logical shall a status bar be drawn?

Value

• a data.frame with columns for variable identification and logical flags for whether the low-frequency modulation of the correlation of this variable with tree-growth is higher than expected by chance, lower than expected by chance, or can be considered as noise.

Details

This function is a multivariate extension of the test for spurious low-frequency modulations for moving correlations of time series as proposed by Gershunov et al. (2001). In short, 1000 simulations of random data sets are generated, where the climate data is simulated as Gaussian noise, and the tree-data as linear combinations of the climate parameters using the original coefficients of the correlation function, and an error component with a variance equal to the variance unexplained by the individual parameters.

For each iteration, a moving correlation function is calculated with exactly the same settings as the original model. The standard deviation over the individual windows for each parameter is then compared to the bootstrapped distribution of the standard deviation of the simulated data to test for significantly higher or lower low-frequency modulations.

References

Gershunov, A., N. Schneider, and T. Barnett. 2001. Low-frequency modulation of the ENSO-Indian Monsoon rainfall relationship: Signal or noise? Journal of Climate 14:2486-2492.

Examples

data(muc_clim) # climatic data
data(muc_pruce) # spruce data
dc_cor <- dcc(muc_spruce, muc_clim, 3:9, method = "cor", moving = TRUE)
g_test(dc_cor)