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Getting started

getting-started.Rmd


Install package 

# install devtools packages
if (!require(devtools)) install.packages("devtools")

# load devtools package
library(devtools)

# install mapac package from gitlab
install_git("https://scm.cms.hu-berlin.de/pflugmad/mapac.git", quiet=F, force=T)

# Additional packages
install.packages(c("ggplot2", "tidyr", "gt"))

Load package and example 

library(mapac)
library(gt)

# load example data from Stehman (2014)

exdata <- aa_examples("stehman2014")
exdata
#> $reference
#>  [1] "A" "A" "A" "A" "A" "C" "B" "A" "B" "C" "A" "B" "B" "B" "B" "B" "A" "A" "B"
#> [20] "B" "C" "C" "C" "C" "C" "D" "D" "B" "B" "A" "D" "D" "D" "D" "D" "D" "D" "C"
#> [39] "C" "B"
#> 
#> $map
#>  [1] "A" "A" "A" "A" "A" "A" "A" "B" "B" "B" "A" "B" "B" "B" "B" "B" "B" "B" "B"
#> [20] "B" "B" "B" "C" "C" "C" "C" "C" "C" "B" "B" "D" "D" "D" "D" "D" "D" "D" "D"
#> [39] "D" "D"
#> 
#> $stratum
#>  [1] "1" "1" "1" "1" "1" "1" "1" "1" "1" "1" "2" "2" "2" "2" "2" "2" "2" "2" "2"
#> [20] "2" "3" "3" "3" "3" "3" "3" "3" "3" "3" "3" "4" "4" "4" "4" "4" "4" "4" "4"
#> [39] "4" "4"
#> 
#> $h
#> [1] "1" "2" "3" "4"
#> 
#> $N_h
#> [1] 40000 30000 20000 10000
#> 
#> $citation
#> [1] "Stehman, S. V., 2014. Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. Int. J. Remote Sens. 35, 4923-4939."

In the example from Stehman (2014), the sampling strata are different from the map strata. To illustrate the point, we extract the stratum labels, the map labels, and the reference labels into three vectors below. The stratum, map, and reference labels can be of type numeric, character, or factor. However, reference and map labels should be of the same type. Use factors if you want to control the class order of the resulting confusion matrices and graphs.

strata <- exdata[["stratum"]]
ref <- exdata[["reference"]]
map <- exdata[["map"]]

To estimate map accuracy and area we can use the aa_stratified() function. The function takes the three vectors above and information on the stratum sizes N_h, e.g. in the form of number of pixels, area, or area proportion. In our example, there are 4 strata where h = {"1", "2", "3", "4"}. Thus, h is a vector of the stratum labels that correspond to the stratum sizes N_h and the stratum samples strata.

result <-  aa_stratified(strata, ref, map, 
                         h=exdata[["h"]], 
                         N_h=exdata[["N_h"]])

The result is a list with the following content:

  • $cm: the adjusted confusion matrix in counts

  • $cmp: the adjusted confusion matrix in area proportion (sums to 1).

  • $stats: User’s (ua) and Producer’s (pa) accuracy and the corresponding standard errors (se) for each class.

  • $accuracy: Overall accuracy and its standard error

  • $area: estimated area proportion and standard errors for each class

result
#> $cm
#>     A    B   C   D
#> A 9.2  1.6 1.6 0.0
#> B 4.8 10.8 3.2 0.0
#> C 0.0  0.8 2.4 1.6
#> D 0.0  0.4 0.8 2.8
#> 
#> $cmp
#>      A    B    C    D
#> A 0.23 0.04 0.04 0.00
#> B 0.12 0.27 0.08 0.00
#> C 0.00 0.02 0.06 0.04
#> D 0.00 0.01 0.02 0.07
#> 
#> $stats
#>   class        ua     ua_se        pa     pa_se        f1      f1_se
#> 1     A 0.7419355 0.1645627 0.6571429 0.1477318 0.6969697 0.11034620
#> 2     B 0.5744681 0.1248023 0.7941176 0.1165671 0.6666667 0.09354009
#> 3     C 0.5000000 0.2151657 0.3000000 0.1504438 0.3750000 0.13219833
#> 4     D 0.7000000 0.1527525 0.6363636 0.1623242 0.6666667 0.11284328
#> 
#> $accuracy
#> [1] 0.63000000 0.08465617
#> 
#> $fpc
#> [1] 1 1 1 1
#> 
#> $area
#>   class proportion proportion_se
#> 1     A       0.35    0.08225975
#> 2     B       0.34    0.07586538
#> 3     C       0.20    0.06429101
#> 4     D       0.11    0.03073181

Export results

You can easily export the results as tables in text files.

write.csv(result$cmp, "my_confusion_matrix.csv")

Visualize confusion matrices

There are a few functions to facilitate visualization and reporting. For example, with aa_confusion_matrix_gtable() you can create confusion matrices with conditional formats. You can save the table in HTML format via the out_file argument.

Map class
Reference class (area %)
Σ
Accuracy
A B C D Map Ref. Producer's User's F-score
A 23.00 4.00 4.00 0.00 31.00 35.00 65.71 ± 14.77 74.19 ± 16.46 69.70 ± 11.03
B 12.00 27.00 8.00 0.00 47.00 34.00 79.41 ± 11.66 57.45 ± 12.48 66.67 ± 9.35
C 0.00 2.00 6.00 4.00 12.00 20.00 30.00 ± 15.04 50.00 ± 21.52 37.50 ± 13.22
D 0.00 1.00 2.00 7.00 10.00 11.00 63.64 ± 16.23 70.00 ± 15.28 66.67 ± 11.28
Overall accuracy = 63 ± 8.47 


cm <- aa_confusion_matrix_gtable(result, type="proportion", 
                                 title="Confusion matrix (area proportion)")
cm
Confusion matrix (area proportion)
Map class
Reference class (area proportion)
Σ
Accuracy
A B C D Map Ref. Producer's User's F-score
A 0.23 0.04 0.04 0.00 0.31 0.35 65.71 ± 14.77 74.19 ± 16.46 69.70 ± 11.03
B 0.12 0.27 0.08 0.00 0.47 0.34 79.41 ± 11.66 57.45 ± 12.48 66.67 ± 9.35
C 0.00 0.02 0.06 0.04 0.12 0.20 30.00 ± 15.04 50.00 ± 21.52 37.50 ± 13.22
D 0.00 0.01 0.02 0.07 0.10 0.11 63.64 ± 16.23 70.00 ± 15.28 66.67 ± 11.28
Overall accuracy = 63 ± 8.47


If you have long class names, it may be useful to use numbers in the column header.

cm <- aa_confusion_matrix_gtable(result, colNumbering = T, zero.rm=T)
cm
Map class
Reference class (area %)
Σ
Accuracy
1 2 3 4 Map Ref. Producer's User's F-score
A | 1 23.00 4.00 4.00 0.00 31.00 35.00 65.71 ± 14.77 74.19 ± 16.46 69.70 ± 11.03
B | 2 12.00 27.00 8.00 0.00 47.00 34.00 79.41 ± 11.66 57.45 ± 12.48 66.67 ± 9.35
C | 3 0.00 2.00 6.00 4.00 12.00 20.00 30.00 ± 15.04 50.00 ± 21.52 37.50 ± 13.22
D | 4 0.00 1.00 2.00 7.00 10.00 11.00 63.64 ± 16.23 70.00 ± 15.28 66.67 ± 11.28
Overall accuracy = 63 ± 8.47


Alternatively, you can provide a different set of class names.

cm <- aa_confusion_matrix_gtable(result, colNumbering = T, 
                                 classLabels=c("u", "v", "x", "y"))
cm
Map class
Reference class (area %)
Σ
Accuracy
1 2 3 4 Map Ref. Producer's User's F-score
u | 1 23.00 4.00 4.00 0.00 31.00 35.00 65.71 ± 14.77 74.19 ± 16.46 69.70 ± 11.03
v | 2 12.00 27.00 8.00 0.00 47.00 34.00 79.41 ± 11.66 57.45 ± 12.48 66.67 ± 9.35
x | 3 0.00 2.00 6.00 4.00 12.00 20.00 30.00 ± 15.04 50.00 ± 21.52 37.50 ± 13.22
y | 4 0.00 1.00 2.00 7.00 10.00 11.00 63.64 ± 16.23 70.00 ± 15.28 66.67 ± 11.28
Overall accuracy = 63 ± 8.47

Visualize class accuracies

Class accuracy

You can save the ggplot as follows:

ggsave('class_accuracy_.png', p, width=9, height=6, units="cm", dpi=300, scale=2)

References

Stehman, S.V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. International Journal of Remote Sensing, 35, 4923-4939