geoSIMEX: Geographic SIMEX
In itpir/geoSIMEX: geoSIMEX
Description
Usage
Arguments
Details
Note
Author(s)
References
Examples
Description
geoSIMEX Implementation of the geoSIMEX algorithm for models
with spatial uncertainty. Package built to work with data from
AidData's data extraction tool.
Usage
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geoSIMEX(model, geoSIMEXvariable, roiData, aidData, aid.project.amount,
iterations=500, bins=3, fitting.method = "quadratic", roi.area="area",
roi.pc1.name="pc1.id", roi.pc2.name="pc2.id", roi.pc3.name="pc3.id",
roi.pc4.name="pc4.id", roi.pc5.name="pc5.id", roi.pc5.name="pc6.id",
aid.pc1.centroid.name="centroid.pc1.id", aid.precision.code="precision.code",
parallel=TRUE, mc.cores=2)
Arguments
model
the naive model
geoSIMEXvariable
character containing the name of the variable with spatial uncertainty
aidData
name of dataframe of aid project data
aid.project.amount
character containing the name of the variable in the aidData dataset which contains aid amounts (e.g., commitment, disbursement). Set value to 1 if interested in number of aid projects rather than dollars.
aid.pc1.centroid.name
character containing the name of the variable in the aidData dataset which contains names or IDs of a precision code 1 spatial area that the aid project falls within. "centroid.pc1.id" is the default name in datasets produced by AidData's data extraction tool
aid.precision.code
character containing the name of the variable in the aidData dataset which contains precision codes for each project. "pc1.id" is the default name in datasets produced by AidData's data extraction tool
roiData
name of dataframe of ROI data
roi.area
character containing the name of the variable in the ROI dataset which contains areas of ROIs. "area" is the default name in datasets produced by AidData's data extraction tool
roi.prob.aid
character containing the name of the variable in the ROI dataset which contains the probability of an ROI receiving aid.
roi.pc1.name
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 1 spatial area that each ROI falls within. "pc1.id" is the default name in datasets produced by AidData's data extraction tool
roi.pc2.name
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 2 spatial area that each ROI falls within. "pc2.id" is the default name in datasets produced by AidData's data extraction tool
roi.pc3.name
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 3 spatial area that each ROI falls within. "pc3.id" is the default name in datasets produced by AidData's data extraction tool
roi.pc4.name
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 4 spatial area that each ROI falls within. "pc4.id" is the default name in datasets produced by AidData's data extraction tool
roi.pc6.name
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 6 and 8 spatial area that each ROI falls within. "pc6.id" is the default name in datasets produced by AidData's data extraction tool
iterations
number of simulated error iterations
bins
number of bins to group coefficients
fitting.method
fitting method for the extrapolation. linear and quadratic are implemented.
mc.cores
number of cores to use for parallelization
Details
The values contained within roi.pc1.name and aid.pc1.centroid.name variables should be the same.
Note
The function is built to work with data from AidData's data extration tool. The extraction tool can be accessed here: [provide website].
Just like the lm() and glm() packages, geoSIMEX() is equipped to work with stargazer.
Author(s)
AidData
References
Cook, J.R. and Stefanski, L.A. (1994) Simulation-extrapolation estimation in parametric measurement error models. Journal of American Statistical Association, 89, 1314 – 1328
Examples
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library(devtools)
install_github("itpir/geoSIMEX")
set.seed(500)
##### Generating Country-Level Dataset #####
numSubcounties <- 120
numSubcountyInCounty <- 2
numCountyInDistrict <- 3
numDistrictInRegion <- 2
N <- numSubcounties
subcounty <- 1:N
county <- rep(1:(N/numSubcountyInCounty), each=numSubcountyInCounty)
district <- rep(1:(N/(numSubcountyInCounty*numCountyInDistrict)), each=(numSubcountyInCounty*numCountyInDistrict))
region <- rep(1:(N/(numSubcountyInCounty*numCountyInDistrict*numDistrictInRegion)), each=(numSubcountyInCounty*numCountyInDistrict*numDistrictInRegion))
country <- 1
subcountyArea <- runif(N)
probAid_assumed <- runif(N)
subcountyData <- as.data.frame(cbind(subcounty,county,district,region,country,probAid_assumed,subcountyArea))
##### Creating Aid Dataset #####
numberProjects = 50
aidData <- as.data.frame(matrix(NA,nrow=numberProjects,ncol=3))
names(aidData) <- c("aid","trueSubcounty","PC")
aidData$aid <- runif(nrow(aidData)) * 100
probAid_true <- runif(N)
aidData$trueSubcounty <- sample(size=numberProjects,x=c(1:N), prob=probAid_true, replace=TRUE)
aidData$PC <- sample(size=numberProjects, x=c(1,2,3,4,6), prob=runif(5), replace=TRUE)
# True Aid
aidData$PC.1s <- 1
subcountyData$trueAid <- expected_aid_ROI(aidData=aidData,
roiData=subcountyData,
roi.prob.aid="probAid_assumed",
aid.project.amount="aid",
aid.precision.code="PC.1s",
roi.pc1.name="subcounty",
roi.pc2.name="county",
roi.pc3.name="district",
roi.pc4.name="region",
roi.pc5.name="region",
roi.pc6.name="country",
aid.pc1.centroid.name="trueSubcounty")
# Wealth - 1 to 1 relation with aid
subcountyData$wealth <- subcountyData$trueAid + runif(nrow(subcountyData))
# Expected Value Aid
subcountyData$expectedAid <- expected_aid_ROI(aidData=aidData,
aid.project.amount="aid",
aid.precision.code="PC",
aid.pc1.centroid.name="trueSubcounty",
roiData=subcountyData,
roi.prob.aid="probAid_assumed",
roi.pc1.name="subcounty",
roi.pc2.name="county",
roi.pc3.name="district",
roi.pc4.name="region",
roi.pc5.name="region",
roi.pc6.name="country")
naive_model <- lm(wealth ~ expectedAid, data=subcountyData)
geoSIMEX_model <- geoSIMEX(model = naive_model,
geoSIMEXvariable = "expectedAid",
aidData = aidData,
aid.project.amount = "aid",
aid.pc1.centroid.name="trueSubcounty",
aid.precision.code="PC",
roiData = subcountyData,
roi.area = "subcountyArea",
roi.prob.aid = "probAid_assumed",
roi.pc1.name="subcounty",
roi.pc2.name="county",
roi.pc3.name="district",
roi.pc4.name="region",
roi.pc5.name="region",
roi.pc6.name="country")
##### Analyzing Results #####
summary(naive_model)
summary(geoSIMEX_model)
plot(geoSIMEX_model, variable="expectedAid")
itpir/geoSIMEX documentation built on May 18, 2019, 7:10 a.m.
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Description Usage Arguments Details Note Author(s) References Examples
Description
geoSIMEX Implementation of the geoSIMEX algorithm for models
with spatial uncertainty. Package built to work with data from
AidData's data extraction tool.
Usage
1 2 3 4 5 6 | geoSIMEX(model, geoSIMEXvariable, roiData, aidData, aid.project.amount,
iterations=500, bins=3, fitting.method = "quadratic", roi.area="area",
roi.pc1.name="pc1.id", roi.pc2.name="pc2.id", roi.pc3.name="pc3.id",
roi.pc4.name="pc4.id", roi.pc5.name="pc5.id", roi.pc5.name="pc6.id",
aid.pc1.centroid.name="centroid.pc1.id", aid.precision.code="precision.code",
parallel=TRUE, mc.cores=2)
|
Arguments
model |
the naive model |
geoSIMEXvariable |
character containing the name of the variable with spatial uncertainty |
aidData |
name of dataframe of aid project data |
aid.project.amount |
character containing the name of the variable in the aidData dataset which contains aid amounts (e.g., commitment, disbursement). Set value to 1 if interested in number of aid projects rather than dollars. |
aid.pc1.centroid.name |
character containing the name of the variable in the aidData dataset which contains names or IDs of a precision code 1 spatial area that the aid project falls within. "centroid.pc1.id" is the default name in datasets produced by AidData's data extraction tool |
aid.precision.code |
character containing the name of the variable in the aidData dataset which contains precision codes for each project. "pc1.id" is the default name in datasets produced by AidData's data extraction tool |
roiData |
name of dataframe of ROI data |
roi.area |
character containing the name of the variable in the ROI dataset which contains areas of ROIs. "area" is the default name in datasets produced by AidData's data extraction tool |
roi.prob.aid |
character containing the name of the variable in the ROI dataset which contains the probability of an ROI receiving aid. |
roi.pc1.name |
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 1 spatial area that each ROI falls within. "pc1.id" is the default name in datasets produced by AidData's data extraction tool |
roi.pc2.name |
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 2 spatial area that each ROI falls within. "pc2.id" is the default name in datasets produced by AidData's data extraction tool |
roi.pc3.name |
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 3 spatial area that each ROI falls within. "pc3.id" is the default name in datasets produced by AidData's data extraction tool |
roi.pc4.name |
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 4 spatial area that each ROI falls within. "pc4.id" is the default name in datasets produced by AidData's data extraction tool |
roi.pc6.name |
character containing the name of the variable in the ROI dataset which contains names or IDs of the precision code 6 and 8 spatial area that each ROI falls within. "pc6.id" is the default name in datasets produced by AidData's data extraction tool |
iterations |
number of simulated error iterations |
bins |
number of bins to group coefficients |
fitting.method |
fitting method for the extrapolation. linear and quadratic are implemented. |
mc.cores |
number of cores to use for parallelization |
Details
The values contained within roi.pc1.name and aid.pc1.centroid.name variables should be the same.
Note
The function is built to work with data from AidData's data extration tool. The extraction tool can be accessed here: [provide website].
Just like the lm() and glm() packages, geoSIMEX() is equipped to work with stargazer.
Author(s)
AidData
References
Cook, J.R. and Stefanski, L.A. (1994) Simulation-extrapolation estimation in parametric measurement error models. Journal of American Statistical Association, 89, 1314 – 1328
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | library(devtools)
install_github("itpir/geoSIMEX")
set.seed(500)
##### Generating Country-Level Dataset #####
numSubcounties <- 120
numSubcountyInCounty <- 2
numCountyInDistrict <- 3
numDistrictInRegion <- 2
N <- numSubcounties
subcounty <- 1:N
county <- rep(1:(N/numSubcountyInCounty), each=numSubcountyInCounty)
district <- rep(1:(N/(numSubcountyInCounty*numCountyInDistrict)), each=(numSubcountyInCounty*numCountyInDistrict))
region <- rep(1:(N/(numSubcountyInCounty*numCountyInDistrict*numDistrictInRegion)), each=(numSubcountyInCounty*numCountyInDistrict*numDistrictInRegion))
country <- 1
subcountyArea <- runif(N)
probAid_assumed <- runif(N)
subcountyData <- as.data.frame(cbind(subcounty,county,district,region,country,probAid_assumed,subcountyArea))
##### Creating Aid Dataset #####
numberProjects = 50
aidData <- as.data.frame(matrix(NA,nrow=numberProjects,ncol=3))
names(aidData) <- c("aid","trueSubcounty","PC")
aidData$aid <- runif(nrow(aidData)) * 100
probAid_true <- runif(N)
aidData$trueSubcounty <- sample(size=numberProjects,x=c(1:N), prob=probAid_true, replace=TRUE)
aidData$PC <- sample(size=numberProjects, x=c(1,2,3,4,6), prob=runif(5), replace=TRUE)
# True Aid
aidData$PC.1s <- 1
subcountyData$trueAid <- expected_aid_ROI(aidData=aidData,
roiData=subcountyData,
roi.prob.aid="probAid_assumed",
aid.project.amount="aid",
aid.precision.code="PC.1s",
roi.pc1.name="subcounty",
roi.pc2.name="county",
roi.pc3.name="district",
roi.pc4.name="region",
roi.pc5.name="region",
roi.pc6.name="country",
aid.pc1.centroid.name="trueSubcounty")
# Wealth - 1 to 1 relation with aid
subcountyData$wealth <- subcountyData$trueAid + runif(nrow(subcountyData))
# Expected Value Aid
subcountyData$expectedAid <- expected_aid_ROI(aidData=aidData,
aid.project.amount="aid",
aid.precision.code="PC",
aid.pc1.centroid.name="trueSubcounty",
roiData=subcountyData,
roi.prob.aid="probAid_assumed",
roi.pc1.name="subcounty",
roi.pc2.name="county",
roi.pc3.name="district",
roi.pc4.name="region",
roi.pc5.name="region",
roi.pc6.name="country")
naive_model <- lm(wealth ~ expectedAid, data=subcountyData)
geoSIMEX_model <- geoSIMEX(model = naive_model,
geoSIMEXvariable = "expectedAid",
aidData = aidData,
aid.project.amount = "aid",
aid.pc1.centroid.name="trueSubcounty",
aid.precision.code="PC",
roiData = subcountyData,
roi.area = "subcountyArea",
roi.prob.aid = "probAid_assumed",
roi.pc1.name="subcounty",
roi.pc2.name="county",
roi.pc3.name="district",
roi.pc4.name="region",
roi.pc5.name="region",
roi.pc6.name="country")
##### Analyzing Results #####
summary(naive_model)
summary(geoSIMEX_model)
plot(geoSIMEX_model, variable="expectedAid")
|
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