| Type: | Package |
| Title: | Total Survey Error Under Multiple, Different Weighting Schemes |
| Version: | 0.1.0 |
| Maintainer: | Joshua Miller <joshlmiller@msn.com> |
| Description: | Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc. |
| Note: | 'TSEwgt' is a companion package to 'TSE'. Each package calculates TSE, but the former for multiple, different surveys, and the latter for a single survey under multiple, different weighting schemes. |
| Imports: | stats |
| Depends: | R (≥ 3.5) |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| Suggests: | knitr, rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2019-07-02 12:47:11 UTC; JOSHUA |
| Author: | Joshua Miller [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2019-07-02 16:30:10 UTC |
Average mean absolute error (aMAE)
Description
Calculates average mean absolute error (aMAE) under multiple, different weighting schemes
Usage
AVEMAEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aMAE for weighting scheme # => mean value of the MAEs for specified variables under weighting scheme # => mean value of MAEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average mean absolute error (aMAE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVEMAEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average mean absolute percentage error (aMAPE)
Description
Calculates average mean absolute percentage error (aMAPE) under multiple, different weighting schemes
Usage
AVEMAPEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aMAPE for weighting scheme # => mean value of the aMAPEs for specified variables under weighting scheme # => mean value of aMAPEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average mean absolute percentage error (aMAPE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVEMAPEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average mean squared error (aMSE) with bias-variance decomposition
Description
Calculates average mean squared error (aMSE) with bias-variance decomposition under multiple, different weighting schemes
Usage
AVEMSEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aMSE for weighting scheme # => mean value of the MSEs for specified variables under weighting scheme # => mean value of MSEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average mean squared error (aMSE) with bias-variance decomposition under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVEMSEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average mean squared logarithmic error (aMSLE)
Description
Calculates average mean squared logarithmic error (aMSLE) under multiple, different weighting schemes
Usage
AVEMSLEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aMSLE for weighting scheme # => mean value of the aMSLEs for specified variables under weighting scheme # => mean value of aMSLEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average mean squared logarithmic error (aMSLE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVEMSLEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average relative absolute error (aRAE)
Description
Calculates average relative absolute error (aRAE) under multiple, different weighting schemes
Usage
AVERAEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aRAE for weighting scheme # => mean value of the aRAEs for specified variables under weighting scheme # => mean value of aRAEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average relative absolute error (aRAE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVERAEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average root mean squared error (aRMSE)
Description
Calculates average root mean squared error (aRMSE) under multiple, different weighting schemes
Usage
AVERMSEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aRMSE for weighting scheme # => mean value of the RMSEs for specified variables under weighting scheme # => mean value of RMSEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average root mean squared error (aRMSE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVERMSEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average root mean squared logarithmic error (aRMSLE)
Description
Calculates average root mean squared logarithmic error (aRMSLE) under multiple, different weighting schemes
Usage
AVERMSLEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aRMSLE for weighting scheme # => mean value of the aRMSLEs for specified variables under weighting scheme # => mean value of aRMSLEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average root mean squared logarithmic error (aRMSLE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVERMSLEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average root relative squared error (aRRSE)
Description
Calculates average root relative squared error (aRRSE) under multiple, different weighting schemes
Usage
AVERRSEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aRRSE for weighting scheme # => mean value of the aRRSEs for specified variables under weighting scheme # => mean value of aRRSEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average root relative squared error (aRRSE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVERRSEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average relative squared error (aRSE)
Description
Calculates average relative squared error (aRSE) under multiple, different weighting schemes
Usage
AVERSEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aRSE for weighting scheme # => mean value of the aRSEs for specified variables under weighting scheme # => mean value of aRSEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average relative squared error (aRSE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVERSEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Average symmetric mean absolute percentage error (aSMAPE)
Description
Calculates average symmetric mean absolute percentage error (aSMAPE) under multiple, different weighting schemes
Usage
AVESMAPEw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Details
aSMAPE for weighting scheme # => mean value of the aSMAPEs for specified variables under weighting scheme # => mean value of aSMAPEs for objects in Survey=data.frame() * objects in Weights=data.frame()
Value
Average symmetric mean absolute percentage error (aSMAPE) under multiple, different weighting schemes
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
AVESMAPEw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Full scale-dependent statistics
Description
Calculates full scale-dependent statistics
Usage
FULLSDw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Value
Full scale-dependent statistics
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
FULLSDw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
Full scale-independent statistics
Description
Calculates full scale-independent statistics
Usage
FULLSIw(Actual = data.frame(), Survey = data.frame(),
Weights = data.frame())
Arguments
Actual |
data from a "gold standard" survey; objects are variable columns from "gold standard" survey that corruspond to variable columns Survey |
Survey |
data from a survey; objects are variable columns from a survey that corruspond to variable columns from Actual |
Weights |
weights to be applied to Survey data; objects are weights columns |
Value
Full scale-independent statistics
Note
Make sure to properly order inputs, per the example: Actual=data.frame() objects and corrusponding Survey=data.frame() objects must be given in the same order as each other; and Weights=data.frame() objects must be given in sequence of weighting scheme #.
Examples
FULLSIw(Actual=data.frame(TESTWGT$A1, TESTWGT$A2),
Survey=data.frame(TESTWGT$Q1, TESTWGT$Q2),
Weights=data.frame(TESTWGT$W1, TESTWGT$W2))
A data set created by merging 1) "actual" data from a "gold standard" survey (A1, A2), and 2) data from another survey (Q1, Q2), including weights columns for that data (W1, W2). A1/Q1 and A2/Q2 are responses to the same two questions, asked to the same 10 respondents (ID), along the same 1-99 response scale.
Description
A data set created by merging 1) "actual" data from a "gold standard" survey (A1, A2), and 2) data from another survey (Q1, Q2), including weights columns for that data (W1, W2). A1/Q1 and A2/Q2 are responses to the same two questions, asked to the same 10 respondents (ID), along the same 1-99 response scale.
Usage
TESTWGT
Format
A data frame with 10 rows and 7 variables
- ID, A1, A2, Q1, Q2, W1, W2
Paired "actual"/survey data with weights columns for survey data
Source
Example data generated by author