Description Usage Arguments Value Note See Also Examples
View source: R/Algorithms_assessment.R
This function mainly use function Assessment_via_cluster to get
assessments both from fuzzy and hard mode. Specifically, it will return the accuracy and precision of
MAE
,CMAPE
,BIAS
, and CMRPE
which would be seemed as the input value of
function Scoring_system.
1 2 3 4 5 6 7 8 9 10 11 12 
sample.size 
Sample size. This supports a bootstrap way to run the function Assessment_via_cluster. The number should not be larger than the row number of pred or so. 
replace 
Logical, replace, default as 
pred 
Prediction matrix or data.frame 
meas 
Measured (actual) matrix or data.frame 
memb 
Membership matrix 
metrics_used 
The metric combination used in the function. Default is If If 
cluster 
Cluster vector. Could be calculated by the parameter 
seed 
Seed number for fixing the random process. See 
log10 
pass to Sampling_by_sort. 
valid.definition 
The definition of valid prediction, default as

A list containing fuzzy and hard results from Assessment_via_cluster
The row number of pred
, meas
, memb
, and cluster
should be the same.
This function is designed for bootstrapping process to get Chla algorithms assessment. Therefore,
parameters of Assessment_via_cluster is set as fixed such as log10 = TRUE
,
na.process = TRUE
. Given that, I will not export this function in latter to avoid confuses.
Other Algorithm assessment:
Assessment_via_cluster()
,
Sampling_via_cluster()
,
Score_algorithms_interval()
,
Score_algorithms_sort()
,
Scoring_system()
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  library(FCMm)
library(ggplot2)
library(magrittr)
library(stringr)
data("Nechad2015")
x < Nechad2015[,3:11]
wv < gsub("X","",names(x)) %>% as.numeric
set.seed(1234)
w < sample.int(nrow(x), 300)
x < x[w, ]
names(x) < wv
nb = 4 # Obtained from the vignette "Cluster a new dataset by FCMm"
set.seed(1234)
FD < FuzzifierDetermination(x, wv, do.stand=TRUE)
result < FCM.new(FD, nb, fast.mode = TRUE)
p.spec < plot_spec(result, show.stand=TRUE)
print(p.spec$p.cluster.spec)
Chla < Nechad2015$X.Chl_a..ug.L.[w]
Chla[Chla >= 999] < NA
dt_Chla < run_all_Chla_algorithms(x) %>% as.data.frame
dt_Chla < data.frame(Chla_true = Chla,
BR_Gil10 = dt_Chla$BR_Gil10,
OC4_OLCI = dt_Chla$OC4_OLCI,
OCI_Hu12 = dt_Chla$OCI_Hu12,
NDCI_Mi12= dt_Chla$NDCI_Mi12) %>% round(3)
w = which(!is.na(dt_Chla$Chla_true))
dt_Chla = dt_Chla[w,]
memb = result$res.FCM$u[w,] %>% round(4)
cluster = result$res.FCM$cluster[w]
Asses_results < Getting_Asses_results(sample.size=length(cluster),
pred = dt_Chla[,1], meas = data.frame(dt_Chla[,1]), memb = memb,
cluster = cluster)

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