predictModel3: Generate results used by CCL online calculator
In ummel/exampleR: Back-end to CCL calculator
Description
Usage
Arguments
Details
Value
Example applications
View source: R/predict model 3.R
Description
CCL’s
Household
Impact Study estimates the direct financial effect of a carbon tax and
dividend policy for a large, representative sample of U.S. households.
Techniques, data, and assumptions are described in detail in the
associated
working paper.
The online calculator tool uses the study’s results
to estimate a household’s additional costs under the policy (due to higher
prices for goods and services), depending on a limited set of household
characteristics (income, number of vehicles, etc.). It also calculates the
expected dividend, which is a function of the household’s number of adults,
number of minors, and expected federal marginal tax rate. The difference
between the dividend and additional cost is the "net" or overall financial
impact – positive if a household is likely to “come out ahead” under
CF&D.
For ease of use, a small and generally easy-to-recall set of
user inputs are solicited. The calculator reports the expected average
outcome for a household. The actual outcome for any specific household
could vary from the average. For example, if a user household is a
below-average consumer of carbon-intensive goods like air travel and meat,
the calculator will understate the net impact (and vice-versa). Developing
a precise estimate for every household would require many more questions
and accurate recall. We have opted for simplicity over precision.
The
predictModel2() function described here translates user-provided
household characteristics into results displayed on the online calculator
application. The function uses a limited set of user characteristics (see
'Arguments' below). The input data format is based on the OpenCPU
'tvscore' example.
See 'Details' section below for technical details.
The complete
'exampleR' package and source code is available at:
https://github.com/ummel/exampleR
Usage
1
predictModel3(input)
Arguments
input
A text string or .csv file passed via
OpenCPU API (i.e. cURL POST) or a
local R data frame (i.e. for debugging). In either case, input
should contain the user-provided variables below. Calling
inputSummary() or viewing the input_summary data object
provided with package will provide the data types and allowable values for
each of the variables.
zip: 5-digit zip code
na:
number of adults in household
nc: number of minors in household
hinc: household income
hfuel: household primary heating fuel
veh: number of vehicles owned by household
htype: dwelling
type
dirfrac: fraction (0 - 1) of direct emissions tax burden that
is passed through to consumer prices. If this argument is not supplied, it
defaults to 0.95.
indfrac: fraction (0 - 1) of indirect emissions tax
burden that is passed through to consumer prices. If this argument is not
supplied, it defaults to 0.495.
Details
Household-level results from the Household Impact Study were
selected for the year 2012, resulting in a total sample of just over 1
million households. Results for each household were processed to determine
the expected additional financial cost (under the CF&D policy) associated
with "indirect" emissions and those stemming from consumption of gasoline,
electricity, and the household's primary heating fuel. A series of
statistical models were fit to the household sample to determine the
relationship between a limited set of household characteristics and the
cost components. A wide variety of household characteristics were
considered for inclusion in the models; the subset ultimatey selected are
both easy for users to accurately recall and demonstrate a good ability to
(collectively) predict a household's expected additional cost.
The
fitted models are capable of translating household characteristics into
expected (average) additional cost (generalized additive models with
smoothing terms) as well as conditional quantiles (quantile regression) for
the purposes of uncertainty estimation. When estimating emissions/cost
associated with a households indirect emissions component,
predictModels() uses a GAM model to predict the average cost and
quantile models to predict the conditional 25th and 75th percentiles. The
latter are used to estimate the uncertainty around the expected value,
assuming a Normal distribution.
In the case of emissons associated
with gasoline and utilities, predictModel() returns the expected
(average) monthly expenditure value and a "cost formula" that can translate
monthly expenditures into total annual additional cost (including cost
associated with indirect emissions). This feature allows users of the
calculator to adjust the "default" average expenditure values to reflect
their specific situation, resulting in a more accurate overall estimate of
the additonal cost for that household.
In order to account for the
fact that the data used to fit the statistical models is from 2012,
predictModel() includes state-level, fuel-specific price adjustment
factors to inflate or deflate (as appropriate) user-provided expenditure
and income values to current price levels. This ensures that inflation and
changes in fuel prices over time do not unduly affect the results. No
analogous adjustment is made for changes to electricity grid
carbon-intensity over time.
A fixed carbon price of $15 per ton CO2
is assumed. Per the assumptions and caveats in the Household Impact Study
working paper, the household results reflect the "overnight" (i.e.
short-term) direct financial impact of the CF&D proposal, ignoring dynamic
economic effects and changes in employment, preferences, or
technologies.
The expected post-tax dividend for a given household is
determined by the "div_pre" and "mrate" values returned by
predictModel(). The former is simply a function of the number of
adults and children in the household and the "full-share" dividend value.
The latter is estimated via margRate.
Validity and
performance of predictModel() was tested by passing a random sample
of the original Household Impact Study household-level results to the
function and comparing the model-generated results to those in the original
data. This quality-control test indicates that the predictive R^2
value
(coefficient
of determination) is 0.55 in the event that a user relies on the
model-generated gasoline and utility expenditure values. Model skill
improves to a R^2 value of 0.73 when users provide their own
(accurate) expenditure values. Those interested in greater detail are asked
to review the annotated public source code for the predictModel2()
function on GitHub.
Value
Function returns a data frame with one row of outputs for each row in
input. The output variables are:
div_pre: household
pre-tax annual dividend
mrate: estimated marginal federal tax rate
(see margRate)
cost: character string giving formula
that (when evaluated) returns annual policy cost given annual total
expenditure inputs for gasoline (gas), electricity (elec), and heating fuel
(heat)
gas: predicted annual gasoline expenditure for
household (used as slider preset in online calculator)
elec:
predicted annual electricity expenditure for household (used as
slider preset in online calculator)
heat: predicted annual
primary heating fuel expenditure for household (used as slider preset in
online calculator; zero if not applicable)
gas_upr: predicted
maximum feasible annual gasoline expenditure for household (used as slider
max value in online calculator)
elec_upr: predicted maximum feasible
annual electricity expenditure for household (used as slider max value in
online calculator)
heat_upr: predicted maximum feasible annual
primary heating fuel expenditure for household (used as slider max value in
online calculator; zero if not applicable)
Example applications
The CCL calculator tool invokes
predictModel2() remotely via cURL POST calls to OpenCPU. For
example:
curl
https://ummel.ocpu.io/exampleR/R/predictModel2/json -H "Content-Type:
application/json" -d '{"input" : [ {"zip":"80524", "na":2, "nc":2,
"hinc":50000, "hfuel":"Natural gas", "veh":2, "htype":"Stand-alone house"}
]}'
Or predictModel2() can be called locally within R:
nd <- data.frame(zip = "94062", na = 2, nc = 2, hinc = 50e3, hfuel =
"Electricity", veh = 2, htype = "Other")
predictModel2(nd)
A front-end developer can view input parameters details by calling:
curl https://ummel.ocpu.io/exampleR/R/inputSummary/json -H
"Content-Type: application/json" -d '{}'
ummel/exampleR documentation built on Aug. 5, 2020, 2:06 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Example applications
View source: R/predict model 3.R
Description
CCL’s
Household
Impact Study estimates the direct financial effect of a carbon tax and
dividend policy for a large, representative sample of U.S. households.
Techniques, data, and assumptions are described in detail in the
associated
working paper.
The online calculator tool uses the study’s results
to estimate a household’s additional costs under the policy (due to higher
prices for goods and services), depending on a limited set of household
characteristics (income, number of vehicles, etc.). It also calculates the
expected dividend, which is a function of the household’s number of adults,
number of minors, and expected federal marginal tax rate. The difference
between the dividend and additional cost is the "net" or overall financial
impact – positive if a household is likely to “come out ahead” under
CF&D.
For ease of use, a small and generally easy-to-recall set of
user inputs are solicited. The calculator reports the expected average
outcome for a household. The actual outcome for any specific household
could vary from the average. For example, if a user household is a
below-average consumer of carbon-intensive goods like air travel and meat,
the calculator will understate the net impact (and vice-versa). Developing
a precise estimate for every household would require many more questions
and accurate recall. We have opted for simplicity over precision.
The
predictModel2() function described here translates user-provided
household characteristics into results displayed on the online calculator
application. The function uses a limited set of user characteristics (see
'Arguments' below). The input data format is based on the OpenCPU
'tvscore' example.
See 'Details' section below for technical details.
The complete
'exampleR' package and source code is available at:
https://github.com/ummel/exampleR
Usage
1 | predictModel3(input)
|
Arguments
input |
A text string or .csv file passed via
OpenCPU API (i.e. cURL POST) or a
local R data frame (i.e. for debugging). In either case,
|
Details
Household-level results from the Household Impact Study were
selected for the year 2012, resulting in a total sample of just over 1
million households. Results for each household were processed to determine
the expected additional financial cost (under the CF&D policy) associated
with "indirect" emissions and those stemming from consumption of gasoline,
electricity, and the household's primary heating fuel. A series of
statistical models were fit to the household sample to determine the
relationship between a limited set of household characteristics and the
cost components. A wide variety of household characteristics were
considered for inclusion in the models; the subset ultimatey selected are
both easy for users to accurately recall and demonstrate a good ability to
(collectively) predict a household's expected additional cost.
The
fitted models are capable of translating household characteristics into
expected (average) additional cost (generalized additive models with
smoothing terms) as well as conditional quantiles (quantile regression) for
the purposes of uncertainty estimation. When estimating emissions/cost
associated with a households indirect emissions component,
predictModels() uses a GAM model to predict the average cost and
quantile models to predict the conditional 25th and 75th percentiles. The
latter are used to estimate the uncertainty around the expected value,
assuming a Normal distribution.
In the case of emissons associated
with gasoline and utilities, predictModel() returns the expected
(average) monthly expenditure value and a "cost formula" that can translate
monthly expenditures into total annual additional cost (including cost
associated with indirect emissions). This feature allows users of the
calculator to adjust the "default" average expenditure values to reflect
their specific situation, resulting in a more accurate overall estimate of
the additonal cost for that household.
In order to account for the
fact that the data used to fit the statistical models is from 2012,
predictModel() includes state-level, fuel-specific price adjustment
factors to inflate or deflate (as appropriate) user-provided expenditure
and income values to current price levels. This ensures that inflation and
changes in fuel prices over time do not unduly affect the results. No
analogous adjustment is made for changes to electricity grid
carbon-intensity over time.
A fixed carbon price of $15 per ton CO2
is assumed. Per the assumptions and caveats in the Household Impact Study
working paper, the household results reflect the "overnight" (i.e.
short-term) direct financial impact of the CF&D proposal, ignoring dynamic
economic effects and changes in employment, preferences, or
technologies.
The expected post-tax dividend for a given household is
determined by the "div_pre" and "mrate" values returned by
predictModel(). The former is simply a function of the number of
adults and children in the household and the "full-share" dividend value.
The latter is estimated via margRate.
Validity and
performance of predictModel() was tested by passing a random sample
of the original Household Impact Study household-level results to the
function and comparing the model-generated results to those in the original
data. This quality-control test indicates that the predictive R^2
value
(coefficient
of determination) is 0.55 in the event that a user relies on the
model-generated gasoline and utility expenditure values. Model skill
improves to a R^2 value of 0.73 when users provide their own
(accurate) expenditure values. Those interested in greater detail are asked
to review the annotated public source code for the predictModel2()
function on GitHub.
Value
Function returns a data frame with one row of outputs for each row in
input. The output variables are:
div_pre: household pre-tax annual dividend
mrate: estimated marginal federal tax rate (see
margRate)cost: character string giving formula that (when evaluated) returns annual policy cost given annual total expenditure inputs for gasoline (gas), electricity (elec), and heating fuel (heat)
gas: predicted annual gasoline expenditure for household (used as slider preset in online calculator)
elec: predicted annual electricity expenditure for household (used as slider preset in online calculator)
heat: predicted annual primary heating fuel expenditure for household (used as slider preset in online calculator; zero if not applicable)
gas_upr: predicted maximum feasible annual gasoline expenditure for household (used as slider max value in online calculator)
elec_upr: predicted maximum feasible annual electricity expenditure for household (used as slider max value in online calculator)
heat_upr: predicted maximum feasible annual primary heating fuel expenditure for household (used as slider max value in online calculator; zero if not applicable)
Example applications
The CCL calculator tool invokes
predictModel2() remotely via cURL POST calls to OpenCPU. For
example:
curl
https://ummel.ocpu.io/exampleR/R/predictModel2/json -H "Content-Type:
application/json" -d '{"input" : [ {"zip":"80524", "na":2, "nc":2,
"hinc":50000, "hfuel":"Natural gas", "veh":2, "htype":"Stand-alone house"}
]}'
Or predictModel2() can be called locally within R:
nd <- data.frame(zip = "94062", na = 2, nc = 2, hinc = 50e3, hfuel =
"Electricity", veh = 2, htype = "Other")
predictModel2(nd)
A front-end developer can view input parameters details by calling:
curl https://ummel.ocpu.io/exampleR/R/inputSummary/json -H
"Content-Type: application/json" -d '{}'
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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