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apply.bias.corrections()
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Apply bias corrections to model outputs |
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assign.sigma.Q()
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Assign observational errors to observed demand quantities |
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calc.elas.actual()
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Calculate actual elasticities using numerical derivatives. |
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calc.hicks.actual()
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Calculate the actual Hicks elasticities using the Slutsky equation. |
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calc.pop.weight()
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Calculate a weight factor based on the population. |
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calc1eps()
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Calculate the exponents in the demand equation. |
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calc1q()
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Calculate demand quantities for a single set of inputs |
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calculate.ambrosia.params()
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Calculates the 11 parameters for ambrosia using data calculated by create.dataset.for.parameter.fit by maximizing log likelihood |
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compute.bias.corrections()
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Compute regional bias corrections. |
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create.dataset.for.parameter.fit()
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Create dataset with observational error for log likelihood calculation using clustering. |
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eta.constant() eta.s() eta.n()
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Generate an income elasticity function with constant income elasticity. |
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food.dmnd.byincome()
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Tabulate food demand by per-capita-income |
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food.dmnd.byyear()
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Tabulate food demand by year for a model. |
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food.dmnd()
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Calculate food demand using the Edmonds, et al. model. |
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lamks2nu1y0()
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Convert the lambda and ks parameters to nu1 and y0 |
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make.byincome.plot()
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Plot model results by per-capita income |
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make.byyear.plot()
|
Plot model results by year |
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make.demand.plot()
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Plot staple, nonstaple, and total demand output from the model |
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mc.food.dmnd.byyear()
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Compute food demand by year |
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mc.make.byyear.plot()
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Make the by-year plot for a set of monte carlo results by sampling the distribution |
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mc.setup()
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Create a posterior log-pdf function for monte carlo sampling the model parameters. |
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mcparam.clip.tails()
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Filter a Monte Carlo distribution by quantiles |
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mcparam.density()
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Create a density plot for all of the MC variables |
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mcparam.itercount()
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Get the iteration count for a monte carlo dataset. |
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mcparam.ML()
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Get the maximum a-posteriori (MAP) parameters |
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mcparam.sample()
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Sample the MC results using bootstrap sampling. |
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merge(<trn.tst>)
|
Create a merged dataset with training and test data, each labeled accordingly |
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namemc()
|
Return the list of names for the parameters in the model. |
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plohi
|
Recommended parameter limits for the model. |
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prepare.obs()
|
Prepare observations for use in the model. |
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read.mc.data()
|
Read the Monte Carlo results file |
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recursive.partition()
|
Partition input data into clusters with a minimum number of members |
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runapp()
|
Launch the interactive GCAM food demand model |
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y.vals Ps.vals Pn.vals Pm.vals samp.params x1 x0
|
Sample values for the demand model. |
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vec2param()
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Convert a vector of parameters into a params structure. |