vignettes/covariate_matching.md
In nitesh-1507/WindPlus: Covariates matching and performance quantification
title: "Covariate Matching"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{covariate_matching}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
Introduction
The package aims at unveiling a technique called covariate matching, which takes in different data sets and returns data sets whose feature values exhibit similar characteristics or similar distribution. In other words, if a wind speed of 3.2 m/s exists in one data set, the algorithm tries to find wind speed in the vicinity of 3.2 m/s in the other data set.
Theory
Covariate matching methods are rooted in the statistical literature. In
stabilizing the non-experimental discrepancy between non-treated and treated subjects of observational data, covariate distributions are adjusted by selecting non-treated subjects that have a similar covariate condition as that of treated ones. Through the process of matching, non-treated and treated groups become only randomly different on all background covariates, as if these covariates were designed by experimenters. As a result, the outcomes of the matched non-treated and treated groups, which keep the originally observed values, are comparable under the matched covariate conditions.
1. Definition
Covariate matching is a technique to adjust covariates distribution to make treated and non treated group to have the same covariates condition.
2. Algorithm
Covariate matching is broadly divided into steps : Hierarchial subgrouping and one to one matching.
Hierarchial subrouping - Filters data record in non-treated group which satifies the threshold condition for each data record in treated group.
-
Locate a data record in the treated group, Qaft, and label it by the index j.
-
Select one of the covariates, for instance, wind speed, V , and designate it as the variable of which the similarity between two data records is computed.
-
Go through the data records in the non-treated group, Qbef, by selecting the subset of data records such that the difference, in terms of the designated covariate, between the data record j in Qaft and any one of the records in Qbef is smaller than a pre-specified threshold. When V is in fact the one designated in Step 2, the resulting subset is then labeled by placing V as the subscript to Q, namely QV.
-
Next, designate another covariate and use it to prune QV in the same way as one prunes Qbef into QV in Step 3. Doing so produces a smaller subset nested within QV . Then continue with another covariate until all covariates are used.
One to One matching - returns the most similar data record after hierarchial sub-grouping
-
Select the most similar record from Qbef for each Qaft using mahalanobis distance as the similarity measure.
-
Remove the record from Qaft, if a record in Qbef satisfies the above mentioned criteria.
-
Repeat the process for each record in Qaft
Getting Started with WindPlus
Load the library
library(WindPlus)
Functions available
- Matching two data sets
Function : covmatch.binary(dname, cov, weight, cov_circ)
- dname - List of data sets to match
- cov - vector defining position or column number of non circular covariates such as wind speed, temperature etc.
- weight - vector defining the threshold value for each covariate to skim out most similar observation
-
cov_circ - vector defining position or column number of circular covariates such as wind direction, nacelle position etc.
-
Matching multiple data sets
Function : covmatch.mult(dname, cov, weight, cov_circ)
- dname - List of data sets to match
- cov - vector defining position or column number of non circular covariates such as wind speed, temperature etc.
- weight - vector defining the threshold value for each covariate to skim out most similar observation
- cov_circ - vector defining position or column number of circular covariates such as wind direction, nacelle position etc.
Functions distinction
Even though, the arguments seem pretty similar for both the functions, there is a subtle difference in execution. For matching multiple data sets, data set is matched once, whereas for macthing two data sets, the matching is done twice keeping each of the data set as a reference. Since the method is entirely subjective, an exact explanation of such is not possible.
Data set available
- Matching two data sets
The two data sets attached to make use of function covmatch.binary for users are data1 and data2. The data can be made available after installing the package and loading the library. The data sets correspond to wind energy data set, and both of these are two different turbines data aggregated by 10 minutes over a year.
head(data1)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 7.945652 -139.6754 4.107717 15.78978 812.1883
#> 2 7.812174 -139.4615 1.811087 15.37674 753.0530
#> 3 7.270667 -139.2280 6.995000 14.01167 575.1650
#> 4 6.535495 -145.5636 4.193736 12.75363 431.0602
#> 5 7.057674 -149.5409 -4.656279 13.87977 548.4242
#> 6 6.357174 -150.0903 -5.499022 12.81163 434.1179
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state stdeviation
#> 1 7.423696 -1.110000 -1.160000 -1.170000 0 1 2.781440
#> 2 7.229783 -1.110000 -1.160000 -1.170000 0 1 4.896038
#> 3 7.301000 -1.177333 -1.117333 -1.113667 0 1 6.202045
#> 4 7.345495 -1.290000 -1.230000 -1.280000 0 1 6.409968
#> 5 7.142326 -1.172326 -1.128837 -1.130698 0 1 4.276982
#> 6 6.992717 -1.000000 -1.020000 -1.050000 0 1 4.254190
#> turbulence_intensity angle_avg
#> 1 0.13551092 -1.146667
#> 2 0.14273562 -1.146667
#> 3 0.09662243 -1.136111
#> 4 0.12099316 -1.266667
#> 5 0.12291786 -1.143953
#> 6 0.09653338 -1.023333
head(data2)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 6.739239 443.0380 3.942935 13.34217 512.9733
#> 2 6.307717 443.1316 3.603696 12.77554 447.0826
#> 3 6.017283 442.9578 2.200652 12.14239 384.0130
#> 4 6.331522 442.9311 4.859348 12.59370 419.8793
#> 5 6.685978 443.0648 6.777717 13.14359 477.1336
#> 6 5.390833 443.2475 4.926250 10.87000 248.5533
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state stdeviation
#> 1 4.484565 -1.21 -1.21 -1.23 0 1 3.098794
#> 2 4.519239 -1.21 -1.21 -1.23 0 1 3.065769
#> 3 4.442717 -1.21 -1.21 -1.23 0 1 3.355477
#> 4 4.539239 -1.21 -1.21 -1.23 0 1 2.965232
#> 5 4.464783 -1.21 -1.21 -1.23 0 1 1.751849
#> 6 4.305417 -1.21 -1.21 -1.23 0 1 3.133455
#> turbulence_intensity angle_avg
#> 1 0.1414439 -1.216667
#> 2 0.1363695 -1.216667
#> 3 0.1274254 -1.216667
#> 4 0.1080415 -1.216667
#> 5 0.1498261 -1.216667
#> 6 0.1105451 -1.216667
- Matching multiple data sets
The three data sets attached to make use of function covmatch.mult for users are Season1, Season2 and Season3. The data sets correspond to wind energy data set of a single turbine, splitted in 3 seasons over a year.
head(Season1)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 9.519457 570.3934 -6.6969565 17.29500 1417.1964
#> 2 9.145057 570.4648 -5.1733333 17.29851 1328.8118
#> 3 8.190000 576.5300 -0.3680000 16.93800 1041.9080
#> 4 6.538939 574.5732 -2.7621212 13.74227 529.6517
#> 5 6.781196 574.7518 0.3546739 14.12543 571.1225
#> 6 6.772717 574.7117 5.2710870 14.23826 585.9740
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state month
#> 1 5.848587 -0.5384783 -0.5640217 -0.5389130 0 1 1
#> 2 5.843448 -0.9454023 -0.9573563 -0.9162069 0 1 1
#> 3 5.980000 -1.2000000 -1.1900000 -1.2300000 0 1 1
#> 4 5.875909 -1.0400000 -1.0300000 -1.0300000 0 1 1
#> 5 5.973587 -1.0400000 -1.0300000 -1.0300000 0 1 1
#> 6 6.021957 -1.0400000 -1.0300000 -1.0300000 0 1 1
#> stdeviation turbulence_intensity
#> 1 0.6142741 0.08949789
#> 2 0.5847955 0.08520295
#> 3 0.7913912 0.11530332
#> 4 0.5145069 0.07496211
#> 5 0.4473754 0.06518124
#> 6 0.4189708 0.06104278
head(Season2)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 13.36543 194.2200 4.620543 17.30011 1528.446
#> 2 13.52815 194.2200 6.779891 17.31304 1531.465
#> 3 14.18163 194.2334 7.360000 17.29272 1532.282
#> 4 14.02707 191.2653 5.816848 17.29196 1532.273
#> 5 14.12576 189.2733 5.997609 17.30565 1527.963
#> 6 13.94370 189.3000 5.677717 17.29163 1529.607
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state month
#> 1 19.02500 11.48696 11.44989 11.51217 0 1 4
#> 2 19.01500 11.56185 11.51609 11.59804 0 1 4
#> 3 18.99489 12.63000 12.58522 12.64446 0 1 4
#> 4 18.92728 12.25467 12.22141 12.26870 0 1 4
#> 5 18.95804 12.46446 12.42130 12.48467 0 1 4
#> 6 18.88870 11.99717 11.94130 12.00946 0 1 4
#> stdeviation turbulence_intensity
#> 1 1.0590929 0.1485948
#> 2 0.9685266 0.1358880
#> 3 0.8514284 0.1194587
#> 4 0.9916969 0.1391389
#> 5 1.2501088 0.1753950
#> 6 1.2154335 0.1705300
head(Season3)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 3.720000 -164.7345 1.3381176 9.990118 97.098706
#> 2 2.830698 -176.9842 6.6573256 9.999186 59.256628
#> 3 3.276941 -188.2048 5.7588235 9.993765 74.341647
#> 4 2.420370 -198.6938 -0.2095062 9.828519 15.090370
#> 5 2.437273 -191.7600 14.3063636 9.279091 5.102727
#> 6 2.885094 -198.8130 -3.4054717 9.997736 23.681509
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state month stdeviation
#> 1 26.26835 -0.01 -0.10 -0.05 0 1 7 0.5207275
#> 2 26.39500 -0.01 -0.10 -0.05 0 1 7 0.4862335
#> 3 25.95929 -0.01 -0.10 -0.05 0 1 7 0.3052790
#> 4 26.10630 -0.01 -0.10 -0.05 0 1 7 0.4919945
#> 5 26.10909 -0.01 -0.10 -0.05 0 1 7 0.2037200
#> 6 25.79679 -0.10 -0.02 -0.14 0 1 7 0.2265937
#> turbulence_intensity
#> 1 0.06892192
#> 2 0.06435640
#> 3 0.04040581
#> 4 0.06511891
#> 5 0.02696376
#> 6 0.02999126
Usage
- Matching two data sets
# Prepare data set for matching
dname = rep(list(), 2)
dname[[1]] = data1
dname[[2]] = data2
# Non circular covariates column
cov = c(6, 1, 13)
# Weight
weight = c(0.1, 0.1, 0.05)
# Matching
matched = covmatch.binary(dname = dname, weight = weight, cov = cov, cov_circ = NULL)
# Commpare result of one covariate with original data
par(mfrow = c(1, 2))
par(bg = 'grey')
plot(density(dname[[1]][, 13]), col = 'red', main = 'Before Matching', xlab = 'Turbulence Intensity', lwd = 2, xlim = c(0, 0.8), ylim = c(0, 8))
lines(density(dname[[2]][, 13]), col = 'blue', lwd = 2, ylim = c(0, 8), xlim = c(0, 0.8), ylim = c(0, 8))
legend('topright', legend = c('data1', 'data2'), col=c("red", "blue"), lty=1, lwd = 2)
plot(density(matched[[1]][, 13]), col = 'red', main = 'After Matching', xlab = 'Turbulence Intensity', lwd = 2, xlim = c(0, 0.8), ylim = c(0, 8))
lines(density(matched[[2]][,13]), col = 'blue', lwd = 2, xlim = c(0, 0.8), ylim = c(0, 8))
legend('topright', legend = c('data1', 'data2'), col=c("red", "blue"), lty=1, lwd = 2)
- Matching multiple data sets
# Prepare data set for matching
dname = rep(list(), 3)
dname[[1]] = Season1
dname[[2]] = Season2
dname[[3]] = Season3
# Non circular covariates column
cov = c(1, 6, 14)
# Weight
weight = c(0.2, 0.2, 0.2)
# Matching
matched = covmatch.mult(dname = dname, weight = weight, cov = cov, cov_circ = NULL)
# Commpare result of one covariate with original data
par(mfrow = c(1, 2))
par(bg = 'grey')
plot(density(dname[[1]][, 1]), col = 'red', main = 'Before Matching', xlab = 'Wind Speed (m/s)', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(dname[[2]][, 1]), col = 'blue', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(dname[[3]][, 1]), col = 'green', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
legend('topright',legend = c('Season1', 'Season2', 'Season3'), col=c("red", "blue", "green"), lty=1, lwd = 2)
plot(density(matched[[1]][, 1]), col = 'red', main = 'After Matching', xlab = 'Wind Speed (m/s)', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(matched[[2]][, 1]), col = 'blue', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(matched[[3]][, 1]), col = 'green', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
legend('topright',legend = c('Season1', 'Season2', 'Season3'), col=c("red", "blue", "green"), lty=1, lwd = 2)
Result
The task at hand is to find the observations from two or more different set, which exhibit similar characteristics. The functions return a list containing the data sets only with matched observations. It should be kept in mind that, only the supplied covariates are matched. Even though data set containing all the columns are returned back, the columns of interest should be looked into.
nitesh-1507/WindPlus documentation built on Dec. 8, 2019, 1:57 p.m.
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title: "Covariate Matching" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{covariate_matching} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
Introduction
The package aims at unveiling a technique called covariate matching, which takes in different data sets and returns data sets whose feature values exhibit similar characteristics or similar distribution. In other words, if a wind speed of 3.2 m/s exists in one data set, the algorithm tries to find wind speed in the vicinity of 3.2 m/s in the other data set.
Theory
Covariate matching methods are rooted in the statistical literature. In stabilizing the non-experimental discrepancy between non-treated and treated subjects of observational data, covariate distributions are adjusted by selecting non-treated subjects that have a similar covariate condition as that of treated ones. Through the process of matching, non-treated and treated groups become only randomly different on all background covariates, as if these covariates were designed by experimenters. As a result, the outcomes of the matched non-treated and treated groups, which keep the originally observed values, are comparable under the matched covariate conditions.
1. Definition
Covariate matching is a technique to adjust covariates distribution to make treated and non treated group to have the same covariates condition.
2. Algorithm
Covariate matching is broadly divided into steps : Hierarchial subgrouping and one to one matching.
Hierarchial subrouping - Filters data record in non-treated group which satifies the threshold condition for each data record in treated group.
-
Locate a data record in the treated group, Qaft, and label it by the index j.
-
Select one of the covariates, for instance, wind speed, V , and designate it as the variable of which the similarity between two data records is computed.
-
Go through the data records in the non-treated group, Qbef, by selecting the subset of data records such that the difference, in terms of the designated covariate, between the data record j in Qaft and any one of the records in Qbef is smaller than a pre-specified threshold. When V is in fact the one designated in Step 2, the resulting subset is then labeled by placing V as the subscript to Q, namely QV.
-
Next, designate another covariate and use it to prune QV in the same way as one prunes Qbef into QV in Step 3. Doing so produces a smaller subset nested within QV . Then continue with another covariate until all covariates are used.
One to One matching - returns the most similar data record after hierarchial sub-grouping
-
Select the most similar record from Qbef for each Qaft using mahalanobis distance as the similarity measure.
-
Remove the record from Qaft, if a record in Qbef satisfies the above mentioned criteria.
-
Repeat the process for each record in Qaft
Getting Started with WindPlus
Load the library
library(WindPlus)
Functions available
- Matching two data sets
Function : covmatch.binary(dname, cov, weight, cov_circ)
- dname - List of data sets to match
- cov - vector defining position or column number of non circular covariates such as wind speed, temperature etc.
- weight - vector defining the threshold value for each covariate to skim out most similar observation
-
cov_circ - vector defining position or column number of circular covariates such as wind direction, nacelle position etc.
-
Matching multiple data sets
Function : covmatch.mult(dname, cov, weight, cov_circ)
- dname - List of data sets to match
- cov - vector defining position or column number of non circular covariates such as wind speed, temperature etc.
- weight - vector defining the threshold value for each covariate to skim out most similar observation
- cov_circ - vector defining position or column number of circular covariates such as wind direction, nacelle position etc.
Functions distinction
Even though, the arguments seem pretty similar for both the functions, there is a subtle difference in execution. For matching multiple data sets, data set is matched once, whereas for macthing two data sets, the matching is done twice keeping each of the data set as a reference. Since the method is entirely subjective, an exact explanation of such is not possible.
Data set available
- Matching two data sets
The two data sets attached to make use of function covmatch.binary for users are data1 and data2. The data can be made available after installing the package and loading the library. The data sets correspond to wind energy data set, and both of these are two different turbines data aggregated by 10 minutes over a year.
head(data1)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 7.945652 -139.6754 4.107717 15.78978 812.1883
#> 2 7.812174 -139.4615 1.811087 15.37674 753.0530
#> 3 7.270667 -139.2280 6.995000 14.01167 575.1650
#> 4 6.535495 -145.5636 4.193736 12.75363 431.0602
#> 5 7.057674 -149.5409 -4.656279 13.87977 548.4242
#> 6 6.357174 -150.0903 -5.499022 12.81163 434.1179
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state stdeviation
#> 1 7.423696 -1.110000 -1.160000 -1.170000 0 1 2.781440
#> 2 7.229783 -1.110000 -1.160000 -1.170000 0 1 4.896038
#> 3 7.301000 -1.177333 -1.117333 -1.113667 0 1 6.202045
#> 4 7.345495 -1.290000 -1.230000 -1.280000 0 1 6.409968
#> 5 7.142326 -1.172326 -1.128837 -1.130698 0 1 4.276982
#> 6 6.992717 -1.000000 -1.020000 -1.050000 0 1 4.254190
#> turbulence_intensity angle_avg
#> 1 0.13551092 -1.146667
#> 2 0.14273562 -1.146667
#> 3 0.09662243 -1.136111
#> 4 0.12099316 -1.266667
#> 5 0.12291786 -1.143953
#> 6 0.09653338 -1.023333
head(data2)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 6.739239 443.0380 3.942935 13.34217 512.9733
#> 2 6.307717 443.1316 3.603696 12.77554 447.0826
#> 3 6.017283 442.9578 2.200652 12.14239 384.0130
#> 4 6.331522 442.9311 4.859348 12.59370 419.8793
#> 5 6.685978 443.0648 6.777717 13.14359 477.1336
#> 6 5.390833 443.2475 4.926250 10.87000 248.5533
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state stdeviation
#> 1 4.484565 -1.21 -1.21 -1.23 0 1 3.098794
#> 2 4.519239 -1.21 -1.21 -1.23 0 1 3.065769
#> 3 4.442717 -1.21 -1.21 -1.23 0 1 3.355477
#> 4 4.539239 -1.21 -1.21 -1.23 0 1 2.965232
#> 5 4.464783 -1.21 -1.21 -1.23 0 1 1.751849
#> 6 4.305417 -1.21 -1.21 -1.23 0 1 3.133455
#> turbulence_intensity angle_avg
#> 1 0.1414439 -1.216667
#> 2 0.1363695 -1.216667
#> 3 0.1274254 -1.216667
#> 4 0.1080415 -1.216667
#> 5 0.1498261 -1.216667
#> 6 0.1105451 -1.216667
- Matching multiple data sets
The three data sets attached to make use of function covmatch.mult for users are Season1, Season2 and Season3. The data sets correspond to wind energy data set of a single turbine, splitted in 3 seasons over a year.
head(Season1)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 9.519457 570.3934 -6.6969565 17.29500 1417.1964
#> 2 9.145057 570.4648 -5.1733333 17.29851 1328.8118
#> 3 8.190000 576.5300 -0.3680000 16.93800 1041.9080
#> 4 6.538939 574.5732 -2.7621212 13.74227 529.6517
#> 5 6.781196 574.7518 0.3546739 14.12543 571.1225
#> 6 6.772717 574.7117 5.2710870 14.23826 585.9740
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state month
#> 1 5.848587 -0.5384783 -0.5640217 -0.5389130 0 1 1
#> 2 5.843448 -0.9454023 -0.9573563 -0.9162069 0 1 1
#> 3 5.980000 -1.2000000 -1.1900000 -1.2300000 0 1 1
#> 4 5.875909 -1.0400000 -1.0300000 -1.0300000 0 1 1
#> 5 5.973587 -1.0400000 -1.0300000 -1.0300000 0 1 1
#> 6 6.021957 -1.0400000 -1.0300000 -1.0300000 0 1 1
#> stdeviation turbulence_intensity
#> 1 0.6142741 0.08949789
#> 2 0.5847955 0.08520295
#> 3 0.7913912 0.11530332
#> 4 0.5145069 0.07496211
#> 5 0.4473754 0.06518124
#> 6 0.4189708 0.06104278
head(Season2)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 13.36543 194.2200 4.620543 17.30011 1528.446
#> 2 13.52815 194.2200 6.779891 17.31304 1531.465
#> 3 14.18163 194.2334 7.360000 17.29272 1532.282
#> 4 14.02707 191.2653 5.816848 17.29196 1532.273
#> 5 14.12576 189.2733 5.997609 17.30565 1527.963
#> 6 13.94370 189.3000 5.677717 17.29163 1529.607
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state month
#> 1 19.02500 11.48696 11.44989 11.51217 0 1 4
#> 2 19.01500 11.56185 11.51609 11.59804 0 1 4
#> 3 18.99489 12.63000 12.58522 12.64446 0 1 4
#> 4 18.92728 12.25467 12.22141 12.26870 0 1 4
#> 5 18.95804 12.46446 12.42130 12.48467 0 1 4
#> 6 18.88870 11.99717 11.94130 12.00946 0 1 4
#> stdeviation turbulence_intensity
#> 1 1.0590929 0.1485948
#> 2 0.9685266 0.1358880
#> 3 0.8514284 0.1194587
#> 4 0.9916969 0.1391389
#> 5 1.2501088 0.1753950
#> 6 1.2154335 0.1705300
head(Season3)
#> wind_speed nac_position wind_direction generator_speed active_power
#> 1 3.720000 -164.7345 1.3381176 9.990118 97.098706
#> 2 2.830698 -176.9842 6.6573256 9.999186 59.256628
#> 3 3.276941 -188.2048 5.7588235 9.993765 74.341647
#> 4 2.420370 -198.6938 -0.2095062 9.828519 15.090370
#> 5 2.437273 -191.7600 14.3063636 9.279091 5.102727
#> 6 2.885094 -198.8130 -3.4054717 9.997736 23.681509
#> ambient_temp angle_1 angle_2 angle_3 power_limitation state month stdeviation
#> 1 26.26835 -0.01 -0.10 -0.05 0 1 7 0.5207275
#> 2 26.39500 -0.01 -0.10 -0.05 0 1 7 0.4862335
#> 3 25.95929 -0.01 -0.10 -0.05 0 1 7 0.3052790
#> 4 26.10630 -0.01 -0.10 -0.05 0 1 7 0.4919945
#> 5 26.10909 -0.01 -0.10 -0.05 0 1 7 0.2037200
#> 6 25.79679 -0.10 -0.02 -0.14 0 1 7 0.2265937
#> turbulence_intensity
#> 1 0.06892192
#> 2 0.06435640
#> 3 0.04040581
#> 4 0.06511891
#> 5 0.02696376
#> 6 0.02999126
Usage
- Matching two data sets
# Prepare data set for matching
dname = rep(list(), 2)
dname[[1]] = data1
dname[[2]] = data2
# Non circular covariates column
cov = c(6, 1, 13)
# Weight
weight = c(0.1, 0.1, 0.05)
# Matching
matched = covmatch.binary(dname = dname, weight = weight, cov = cov, cov_circ = NULL)
# Commpare result of one covariate with original data
par(mfrow = c(1, 2))
par(bg = 'grey')
plot(density(dname[[1]][, 13]), col = 'red', main = 'Before Matching', xlab = 'Turbulence Intensity', lwd = 2, xlim = c(0, 0.8), ylim = c(0, 8))
lines(density(dname[[2]][, 13]), col = 'blue', lwd = 2, ylim = c(0, 8), xlim = c(0, 0.8), ylim = c(0, 8))
legend('topright', legend = c('data1', 'data2'), col=c("red", "blue"), lty=1, lwd = 2)
plot(density(matched[[1]][, 13]), col = 'red', main = 'After Matching', xlab = 'Turbulence Intensity', lwd = 2, xlim = c(0, 0.8), ylim = c(0, 8))
lines(density(matched[[2]][,13]), col = 'blue', lwd = 2, xlim = c(0, 0.8), ylim = c(0, 8))
legend('topright', legend = c('data1', 'data2'), col=c("red", "blue"), lty=1, lwd = 2)
- Matching multiple data sets
# Prepare data set for matching
dname = rep(list(), 3)
dname[[1]] = Season1
dname[[2]] = Season2
dname[[3]] = Season3
# Non circular covariates column
cov = c(1, 6, 14)
# Weight
weight = c(0.2, 0.2, 0.2)
# Matching
matched = covmatch.mult(dname = dname, weight = weight, cov = cov, cov_circ = NULL)
# Commpare result of one covariate with original data
par(mfrow = c(1, 2))
par(bg = 'grey')
plot(density(dname[[1]][, 1]), col = 'red', main = 'Before Matching', xlab = 'Wind Speed (m/s)', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(dname[[2]][, 1]), col = 'blue', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(dname[[3]][, 1]), col = 'green', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
legend('topright',legend = c('Season1', 'Season2', 'Season3'), col=c("red", "blue", "green"), lty=1, lwd = 2)
plot(density(matched[[1]][, 1]), col = 'red', main = 'After Matching', xlab = 'Wind Speed (m/s)', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(matched[[2]][, 1]), col = 'blue', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
lines(density(matched[[3]][, 1]), col = 'green', lwd = 2, xlim = c(0, 20), ylim = c(0, 0.18))
legend('topright',legend = c('Season1', 'Season2', 'Season3'), col=c("red", "blue", "green"), lty=1, lwd = 2)
Result
The task at hand is to find the observations from two or more different set, which exhibit similar characteristics. The functions return a list containing the data sets only with matched observations. It should be kept in mind that, only the supplied covariates are matched. Even though data set containing all the columns are returned back, the columns of interest should be looked into.
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