Description Usage Arguments Value Examples
Compute weighting between two models based on accuracy in predicting a set of observations. Computation is via the Expectation-Maximization algorithm.
1 2 | fit_weights(mod1, mod2, obs, prop_area, w_ini = 0.5, z_ini = 0.5,
eps = 0.01)
|
mod1 |
array with estimated sea ice probability from model 1. Dimensions are nuumber of training years x lon x lat. |
mod2 |
array with estimated sea ice probability from model 2. Dimensions are nuumber of training years x lon x lat. |
obs |
array with observations of sea ice presence (1) and absence (0). Dimensions are nuumber of training years x lon x lat. |
prop_area |
matrix that gives the proportion of area in each grid box. Should sum to 1. Dimensions are lon x lat. |
w_ini |
initial value of all w, defaults to 0.5. |
z_ini |
initial value of all z, defaults to 0.5. |
eps |
tolerance for EM algorithm to reach convergence, defaults to 0.01. |
value between 0 and 1 giving the weight on the first model
1 2 3 4 5 | ## Not run:
weight <- fit_weights(mod1 = clim_9_2005_2007, mod2 = ppe_9_2005_2007,
obs = obs_9_2005_2007, prop_area = prop_area)
## End(Not run)
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