Clustering_Time_Series-TSClust-Study-01-HR
Gonzalo Aris 16021
2023-03-05
Libraries
library(TSA) # time series
library(TSclust)
library(tidyr)
library(readr)
library(ggplot2) # ggplot graphs
library(knitr)
library(readxl)
library(xlsx)
library(openxlsx)
library(reactable) # reactable(df)
library(naniar) # miss_case_summary
library(dplyr)
## KNN imputation
library(caret)
library(RANN)
# CLustering
library(factoextra) # Clustering visualization
library(cluster) # Clustering algorithms
library(dendextend) # For comparing two dendrograms
library(corrplot) # Corelation between dendrograms
library(tidyverse) # Data manupulation
library(NbClust) # Determine optimal no. of clusters [not working...]
library(TSclust)
library(mclust) # Adjusted Rand index
#RandomForest
library(randomForest) # RandomForest Discrete Classification
library(imbalance) # To create a more balanced datasetFunctions
source("../../scripts/useful-functions/get_column_position.R")
# In a normal script it will be: source("./scripts/useful-functions/get_column_position.R")Reading Data
Time Series Data: FC Heart Rate
FC_TS_HR_P2 = data.frame(read_xlsx("../../data/clean-data/BoxBasedImputation/FC_valid_patients_input_P2.xlsx", sheet = "FC_valid_patients_input_P2" ))
# First patients with OAF
name_patients_DETERIORO_OAF_0 <- data.frame(read_csv("../../data/clean-data/name_patients_DETERIORO_OAF_0.csv"))
name_patients_DETERIORO_OAF_0 <- name_patients_DETERIORO_OAF_0$x
name_patients_DETERIORO_OAF_0_8 <- data.frame(read_csv("../../data/clean-data/name_patients_DETERIORO_OAF_0_8.csv"))
name_patients_DETERIORO_OAF_0_8 <- name_patients_DETERIORO_OAF_0_8$x
## Deterioro and Not deterioro
file_patient_name_NO_DETERIORO <- data.frame(read_csv("../../data/info-patients/file_patient_name_NO_DETERIORO.csv"))
file_patient_name_NO_DETERIORO <- file_patient_name_NO_DETERIORO$x
file_patient_name_DETERIORO <- data.frame(read_csv("../../data/info-patients/file_patient_name_DETERIORO.csv"))
file_patient_name_DETERIORO <- file_patient_name_DETERIORO$xDescriptive Data for Discriminant analysis
valid_patients_P2 <- data.frame(read_xlsx("../../data/clean-data/valid_patients_P2.xlsx"))
valid_patients_P2 <- valid_patients_P2$x
valid_patients_P2 <- valid_patients_P2[! valid_patients_P2 %in% union(name_patients_DETERIORO_OAF_0,name_patients_DETERIORO_OAF_0_8)]
file_patient_name <- data.frame(read_csv("../../data/clean-data/file_patient_name.csv", show_col_types = FALSE))
file_patient_name <- file_patient_name$x
df1 <- data.frame(read_xlsx("../../data/clean-data/descriptive-data/descriptive_data.xlsx"))
rownames(df1) <- file_patient_name
df1 <- df1[valid_patients_P2,]df_descriptive <- data.frame(read_xlsx("../../data/clean-data/descriptive-data/descriptive_data_imputed.xlsx"), row.names = TRUE)
rownames(df_descriptive) <- file_patient_nameDeleting variables that occur after the 8 first hours
df_descriptive <- df_descriptive %>% select(-c(FR_8_16h, FR_16_24h, FLUJO2_8_16h,FLUJO2_16_24h,SCORE_WOOD_DOWNES_24H,SAPI_16_24h, SAPI_8_16h))# Class
pos_1 = get_column_position(df_descriptive,"SAPI_0_8h")
pos_2 = get_column_position(df_descriptive,"PAUSAS_APNEA")
df_descriptive[,c(pos_1:pos_2)] <- lapply(df_descriptive[,c(pos_1:pos_2)], as.factor)
#lapply(df_descriptive,class)
df_descriptive <- df_descriptive[valid_patients_P2,]Create a dataframe with ACF [Heart Rate ]
FC_TS_HR_P2 <- FC_TS_HR_P2[,valid_patients_P2]Restando Media
#FC_TS_HR_P2 = data.frame(scale(FC_TS_HR_P2))dimension_col <- dim(FC_TS_HR_P2)[2]
dimension_row <- 480 #lag.max -1
# Heart Rate
FC_TS_HR_P2_ACF <- data.frame(matrix(nrow = dimension_row, ncol = dimension_col))
colnames(FC_TS_HR_P2_ACF) <- names(FC_TS_HR_P2)[1:dimension_col]for (i in names(FC_TS_HR_P2_ACF)) {
acf_result_FC <- forecast::Acf(FC_TS_HR_P2[[i]], lag.max = (dimension_row - 1), plot = FALSE, drop.lag.0 = FALSE)
FC_TS_HR_P2_ACF[, i] <- acf_result_FC$acf
}Create a dataframe with peridiogram
# Generar un dataset con varias series temporales
df <- FC_TS_HR_P2
# Crear una matriz para almacenar los periodogramas
pg_mat <- data.frame(matrix(nrow = nrow(df), ncol = ncol(df)))
colnames(pg_mat) = colnames(FC_TS_HR_P2)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
library(stats)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
for (i in colnames(FC_TS_HR_P2)) {
pg_mat[,i] <- stats::spec.pgram(FC_TS_HR_P2[,i], plot = FALSE)$spec
}TsClust Comprobation
datos <- FC_TS_HR_P2diss.ACF Computes the dissimilarity between two time
series as the distance between their estimated simple (ACF) or partial
(PACF) autocorrelation coefficients
DD_ACF <- diss(datos, "ACF", lag.max = 50)
DD_ACF_matrix <- as.matrix(DD_ACF)diss.EUCL
DD_EUCL <- diss(datos, "EUCL")
DD_EUCL_matrix <- as.matrix(DD_EUCL)diss.PER
DD_PER <- diss(datos, "PER")
DD_PER_matrix <- as.matrix(DD_PER)Euclidean Distance first 50 ACF
datos_ACF = t(FC_TS_HR_P2_ACF[c(1:51),])
distance <- dist(t(FC_TS_HR_P2_ACF[c(1:51),]), method = "euclidean")
distance_matrix_ACF <- as.matrix(distance)Euclidean Distance
datos_EUCL <- t(datos)
distance <- dist(datos_EUCL, method = "euclidean")
distance_matrix_EUCL <- as.matrix(distance)Eculidean PER Distance
datos_PER <- t(pg_mat)
distance_PER <- dist(t(pg_mat), method = "euclidean")
distance_matrix_PER <- as.matrix(distance_PER)
distance_matrix_PER_NORM = distance_matrix_PER / 480TSCLust in Action
ACF TSclust
# DD_ACF <- diss(datos, "ACF", lag.max = 50)Agnes study
To find which hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.
#method to assess
m <- c("average", "single","complete","ward")
names(m) <- c("average", "single","complete","ward.D2")
#function to compute coefficient
ac <- function(x){agnes(datos_ACF, method = x)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.8704366 0.6767717 0.9196194 0.9584022NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_ACF
res<-NbClust(datos_ACF, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.4944 0.3520 0.3354 0.2960res$Best.nc## Number_clusters Value_Index
## 2.0000 0.4944#res$Best.partitionhcintper_ACF <- hclust(DD_ACF, "ward.D2")
fviz_dend(hcintper_ACF, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
DDclust_ACF_FC <- cutree( hclust(DD_ACF, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_ACF_FC))
fviz_silhouette(silhouette(DDclust_ACF_FC, DD_ACF))## cluster size ave.sil.width
## 1 1 37 0.45
## 2 2 21 0.58
Contingency ACF lag.max = 50
DETERIORO_CLUST <- union(intersect(file_patient_name_DETERIORO,names_1),intersect(file_patient_name_DETERIORO,names_2))
NO_DETERIORO_CLUST <- union(intersect(file_patient_name_NO_DETERIORO,names_1),intersect(file_patient_name_NO_DETERIORO,names_2))#DETERIORO
DETERIORO_patients = data.frame(t(rep("#4A235A", length(DETERIORO_CLUST))))
colnames(DETERIORO_patients)<- DETERIORO_CLUST
#NO DETERIORO
NO_DETERIORO_patients = data.frame(t(rep("#117864", length(NO_DETERIORO_CLUST))))
colnames(NO_DETERIORO_patients)<- NO_DETERIORO_CLUST
COLOR_ACF <- cbind(DETERIORO_patients,NO_DETERIORO_patients)order_ACF <- union(names(DDclust_ACF_FC[DDclust_ACF_FC == 2]),names(DDclust_ACF_FC[DDclust_ACF_FC == 1]))
fviz_dend(hcintper_ACF, k = 2,
k_colors = c("blue", "green"),
label_cols = as.vector(COLOR_ACF[,order_ACF]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLUST1" = c(n1,n3), "CLUST2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLUST1 | CLUST2 | |
|---|---|---|
| DETERIORO | 4 | 2 |
| NO DETERIORO | 33 | 19 |
conttingency_table_prop <- data.frame(c(n1,n3)/(n1+n3),c(n2,n4)/(n2+n4))
rownames(conttingency_table_prop) <- c("DETERIORO","NO DETERIORO")
colnames(conttingency_table_prop) <- c("CLUST1","CLUST2")
knitr::kable(conttingency_table_prop, align = "lccrr")| CLUST1 | CLUST2 | |
|---|---|---|
| DETERIORO | 0.1081081 | 0.0952381 |
| NO DETERIORO | 0.8918919 | 0.9047619 |
Random Forest: Discriminant TSCLust ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_FC)
data_frame2 = df_descriptive
data_frame_merge_ACF <-
merge(data_frame1_ACF, data_frame2, by = 'row.names', all = TRUE)
data_frame_merge_ACF <- data_frame_merge_ACF[, 2:dim(data_frame_merge_ACF)[2]]
data_frame_merge_ACF$CLUSTER = factor(data_frame_merge_ACF$CLUSTER)table(data_frame_merge_ACF$CLUSTER)##
## 1 2
## 37 21data_frame_merge_ACF[,c(1:dim(data_frame_merge_ACF)[2])]<- lapply(data_frame_merge_ACF[,c(1:dim(data_frame_merge_ACF)[2])], as.numeric)
head(data_frame_merge_ACF)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 1 13.0 7.78 40 56 2.00 2 2 0
## 3 1 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 1 15.0 7.00 34 37 2.00 4 4 0
## 6 2 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_ACF$CLUSTER <- factor(data_frame_merge_ACF$CLUSTER)
newSMOTE_ACF <- data_frame_merge_ACF
table(newSMOTE_ACF$CLUSTER)##
## 1 2
## 37 21set.seed(123)
pos_1 = get_column_position(newSMOTE_ACF, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_ACF, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_ACF[pos_1:pos_2])
newSMOTE_ACF[col_names_factor] <- lapply(newSMOTE_ACF[col_names_factor] , factor)
RF_ACF <- randomForest(CLUSTER ~ ., data = newSMOTE_ACF)
print(RF_ACF)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_ACF)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 48.28%
## Confusion matrix:
## 1 2 class.error
## 1 26 11 0.2972973
## 2 17 4 0.8095238Importance
kable(RF_ACF$importance[order(RF_ACF$importance, decreasing = TRUE),])| x | |
|---|---|
| EDAD | 2.6328715 |
| SCORE_WOOD_DOWNES_INGRESO | 2.5338316 |
| PESO | 2.3967813 |
| SCORE_CRUCES_INGRESO | 2.3630299 |
| FR_0_8h | 1.7007907 |
| SAPI_0_8h | 1.6392267 |
| DIAS_O2_TOTAL | 1.5501284 |
| EG | 1.4672375 |
| DIAS_GN | 1.3476104 |
| FLUJO2_0_8H | 1.3447355 |
| RADIOGRAFIA | 1.0722420 |
| SEXO | 0.7386009 |
| ETIOLOGIA | 0.7294733 |
| ALIMENTACION | 0.6162708 |
| LM | 0.4676251 |
| ANALITICA | 0.3376106 |
| DIAS_OAF | 0.3297077 |
| PREMATURIDAD | 0.3288944 |
| TABACO | 0.3274340 |
| ENFERMEDAD_BASE | 0.3257954 |
| SUERO | 0.3135917 |
| ALERGIAS | 0.3089164 |
| GN_INGRESO | 0.2477224 |
| SNG | 0.2476302 |
| DERMATITIS | 0.1752274 |
| OAF | 0.1405188 |
| PALIVIZUMAB | 0.1345768 |
| OAF_TRAS_INGRESO | 0.1273862 |
| DETERIORO | 0.1157549 |
| PAUSAS_APNEA | 0.0759802 |
| UCIP | 0.0517007 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of first 50 ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_FC)
data_frame2_ACF = data.frame(t(FC_TS_HR_P2_ACF[c(1:51),]))
data_frame_merge_ACF <-
merge(data_frame1_ACF, data_frame2_ACF, by = 'row.names', all = TRUE)
data_frame_merge_ACF <- data_frame_merge_ACF[, 2:dim(data_frame_merge_ACF)[2]]set.seed(123)
data_frame_merge_ACF$CLUSTER <- as.factor(data_frame_merge_ACF$CLUSTER)
RF_0_ACF <- randomForest(CLUSTER ~ ., data = data_frame_merge_ACF)
print(RF_0_ACF)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_ACF)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 7
##
## OOB estimate of error rate: 1.72%
## Confusion matrix:
## 1 2 class.error
## 1 37 0 0.00000000
## 2 1 20 0.04761905plot(RF_0_ACF$importance, type = "h")
### ACF by clusters
plot_data_ACF <- data.frame(datos_ACF)
cluster_data_ACF <- data.frame(DDclust_ACF_FC)
plotting_ACF <- cbind(plot_data_ACF, cluster_data_ACF)
head(plotting_ACF)## X1 X2 X3 X4 X5 X6 X7
## ACR_11231843 1 0.5747954 0.4244149 0.3898310 0.3054550 0.2987537 0.2466085
## ADAO_11159808 1 0.6805727 0.5935279 0.5085316 0.4365390 0.3660983 0.3061974
## AGG_11236448 1 0.7659893 0.6522822 0.5752187 0.5026580 0.4312281 0.4003839
## AHL_11239959 1 0.7330013 0.6576631 0.6158813 0.5836972 0.5097892 0.4615090
## AJGD_11119689 1 0.4856503 0.4165173 0.3766304 0.3176037 0.3071462 0.2873193
## AMP_11228639 1 0.6595950 0.6178051 0.6037129 0.5651124 0.5755787 0.5525003
## X8 X9 X10 X11 X12 X13
## ACR_11231843 0.1833401 0.1800060 0.1590625 0.1193108 0.1028016 0.08907378
## ADAO_11159808 0.2645815 0.2238202 0.1822452 0.1658125 0.1617351 0.14100383
## AGG_11236448 0.3616214 0.3484616 0.3680116 0.3937240 0.3530302 0.34635666
## AHL_11239959 0.4253346 0.3663603 0.3350366 0.3211704 0.3012808 0.29708129
## AJGD_11119689 0.2504552 0.2382239 0.2213956 0.1841389 0.1555994 0.19398733
## AMP_11228639 0.5484309 0.5151089 0.5260231 0.5356568 0.5412524 0.53812733
## X14 X15 X16 X17 X18
## ACR_11231843 0.02692387 0.02098007 0.01292424 0.0006154294 0.004233393
## ADAO_11159808 0.13026706 0.13321015 0.13066704 0.1285902414 0.110254753
## AGG_11236448 0.35754880 0.32532530 0.27518679 0.2299503431 0.206868669
## AHL_11239959 0.26719489 0.25676612 0.24100162 0.2420480921 0.198732377
## AJGD_11119689 0.15437946 0.18278084 0.17649073 0.1750699756 0.190130736
## AMP_11228639 0.53846799 0.54239706 0.55188989 0.5360942030 0.531989902
## X19 X20 X21 X22 X23
## ACR_11231843 -0.006757205 -0.007036055 -0.01496791 -0.02636549 -0.001402886
## ADAO_11159808 0.127414548 0.087625607 0.07760646 0.09671878 0.104156536
## AGG_11236448 0.184265903 0.151694667 0.14302509 0.12456011 0.131032068
## AHL_11239959 0.197691766 0.174912058 0.19319050 0.21205775 0.206312141
## AJGD_11119689 0.148089255 0.169290584 0.18741791 0.23496989 0.197737591
## AMP_11228639 0.471925620 0.484153622 0.49282071 0.49739289 0.498560780
## X24 X25 X26 X27 X28
## ACR_11231843 0.01324269 0.02086305 0.02388871 -0.01035748 -0.03510893
## ADAO_11159808 0.10125899 0.07996171 0.07672970 0.09223638 0.06817663
## AGG_11236448 0.15374218 0.13654930 0.11173266 0.10480140 0.09948863
## AHL_11239959 0.18457331 0.17656272 0.19223655 0.17530399 0.13561442
## AJGD_11119689 0.19868995 0.18905268 0.22099258 0.18826589 0.20099552
## AMP_11228639 0.47651016 0.47433498 0.49124269 0.46617148 0.47121446
## X29 X30 X31 X32 X33
## ACR_11231843 -0.04784269 -0.05506440 -0.008593307 0.04976843 0.09740572
## ADAO_11159808 0.09274641 0.06786673 0.071169346 0.07319753 0.07839764
## AGG_11236448 0.06725227 0.07242530 0.075139440 0.09715155 0.11831388
## AHL_11239959 0.14198217 0.15516364 0.104568650 0.08413169 0.10251633
## AJGD_11119689 0.18028969 0.14769550 0.165832019 0.11516172 0.12166889
## AMP_11228639 0.49358464 0.45935156 0.460468415 0.43672572 0.43495816
## X34 X35 X36 X37 X38 X39
## ACR_11231843 0.06713914 0.04049820 0.01295611 0.03353650 0.02670075 0.05435805
## ADAO_11159808 0.04368361 0.05899972 0.06957130 0.06502349 0.04428139 0.08720689
## AGG_11236448 0.15119247 0.16481099 0.17926504 0.17486282 0.17939105 0.16038387
## AHL_11239959 0.09851785 0.13082361 0.13043217 0.13487428 0.11851440 0.13412057
## AJGD_11119689 0.13947771 0.13992780 0.11603972 0.12556575 0.13822745 0.08786577
## AMP_11228639 0.42293683 0.41076414 0.39748507 0.38724889 0.38415061 0.37327204
## X40 X41 X42 X43 X44
## ACR_11231843 0.05163509 0.04581221 0.03625811 0.006093607 0.015911426
## ADAO_11159808 0.08245975 0.07325789 0.02199331 0.010335726 0.005374176
## AGG_11236448 0.14700455 0.14244461 0.15426774 0.162140790 0.167102275
## AHL_11239959 0.14253952 0.14241159 0.13874827 0.152605118 0.122308403
## AJGD_11119689 0.11241510 0.12355936 0.14482627 0.151343806 0.125769167
## AMP_11228639 0.38356428 0.34057877 0.35432214 0.350061870 0.362196920
## X45 X46 X47 X48 X49
## ACR_11231843 0.065718135 -0.044529286 0.02727482 0.03598471 0.01538854
## ADAO_11159808 0.008207619 0.005354707 0.04384913 0.07181361 0.07248583
## AGG_11236448 0.193588145 0.189538586 0.17553872 0.17449402 0.19356802
## AHL_11239959 0.116989766 0.097048192 0.09234721 0.08137636 0.05817498
## AJGD_11119689 0.114462985 0.147833838 0.11542335 0.14404906 0.07111346
## AMP_11228639 0.348115576 0.328500927 0.32673600 0.28805596 0.28570880
## X50 X51 DDclust_ACF_FC
## ACR_11231843 0.03556238 0.03345494 1
## ADAO_11159808 0.09748668 0.12009315 1
## AGG_11236448 0.19300176 0.17783030 1
## AHL_11239959 0.05753825 0.06746329 1
## AJGD_11119689 0.10372669 0.07286803 1
## AMP_11228639 0.27435691 0.26466781 2## Mean by groups
rp_tbl_ACF <- aggregate(plotting_ACF, by = list(plotting_ACF$DDclust_ACF_FC), mean)
row.names(rp_tbl_ACF) <- paste0("Group",rp_tbl_ACF$DDclust_ACF_FC)
rp_tbl_ACF <- rp_tbl_ACF %>%
select(starts_with('X'))
rp_tbl_ACF <- data.frame(t(rp_tbl_ACF))
head(rp_tbl_ACF)## Group1 Group2
## X1 1.0000000 1.0000000
## X2 0.7226921 0.8428105
## X3 0.6451763 0.7923087
## X4 0.5790438 0.7612588
## X5 0.5311166 0.7314445
## X6 0.4874229 0.7109206# Create plotting data-frame
ACF_values_by_group <- data.frame("value_ACF" = c(rp_tbl_ACF$Group1,rp_tbl_ACF$Group2),
"cluster" = c(rep("Group1", times = length(rp_tbl_ACF$Group1)),
rep("Group2", times = length(rp_tbl_ACF$Group2))),
"index" = c(c(1:length(rp_tbl_ACF$Group1)),c(1:length(rp_tbl_ACF$Group2))))
p <- ggplot(ACF_values_by_group, aes(x = index, y = value_ACF, group = cluster)) +
geom_line(aes(color=cluster)) +
scale_color_brewer(palette="Paired") + theme_minimal()
p
EUCL TSclust
# DD_EUCL <- diss(datos, "EUCL")Agnes study
To find which hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.
#method to assess
m <- c("average", "single","complete","ward")
names(m) <- c("average", "single","complete","ward.D2")
#function to compute coefficient
ac <- function(x){agnes(datos, method = x)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.6500602 0.5153046 0.7432321 0.9500249NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_EUCL
res<-NbClust(datos, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.2401 0.1263 0.1082 0.1131res$Best.nc## Number_clusters Value_Index
## 2.0000 0.2401#res$Best.partitionhcintper_EUCL <- hclust(DD_EUCL, "ward.D2")
fviz_dend(hcintper_EUCL, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)
DDclust_EUCL_FC <- cutree( hclust(DD_EUCL, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_EUCL_FC))
fviz_silhouette(silhouette(DDclust_EUCL_FC, DD_EUCL))## cluster size ave.sil.width
## 1 1 31 0.25
## 2 2 27 0.22
Contingency EUCL
DETERIORO_CLUST <- union(intersect(file_patient_name_DETERIORO,names_1),intersect(file_patient_name_DETERIORO,names_2))
NO_DETERIORO_CLUST <- union(intersect(file_patient_name_NO_DETERIORO,names_1),intersect(file_patient_name_NO_DETERIORO,names_2))#DETERIORO
DETERIORO_patients = data.frame(t(rep("#4A235A", length(DETERIORO_CLUST))))
colnames(DETERIORO_patients)<- DETERIORO_CLUST
#NO DETERIORO
NO_DETERIORO_patients = data.frame(t(rep("#117864", length(NO_DETERIORO_CLUST))))
colnames(NO_DETERIORO_patients)<- NO_DETERIORO_CLUST
COLOR_EUCL <- cbind(DETERIORO_patients,NO_DETERIORO_patients)order_EUCL <- union(names(DDclust_EUCL_FC[DDclust_EUCL_FC == 2]),names(DDclust_EUCL_FC[DDclust_EUCL_FC == 1]))
fviz_dend(hcintper_EUCL, k = 2,
k_colors = c("blue", "green3"),
label_cols = as.vector(COLOR_EUCL[,order_EUCL]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLUST1" = c(n1,n3), "CLUST2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLUST1 | CLUST2 | |
|---|---|---|
| DETERIORO | 5 | 1 |
| NO DETERIORO | 26 | 26 |
conttingency_table_prop <- data.frame(c(n1,n3)/(n1+n3),c(n2,n4)/(n2+n4))
rownames(conttingency_table_prop) <- c("DETERIORO","NO DETERIORO")
colnames(conttingency_table_prop) <- c("CLUST1","CLUST2")
knitr::kable(conttingency_table_prop, align = "lccrr")| CLUST1 | CLUST2 | |
|---|---|---|
| DETERIORO | 0.1612903 | 0.037037 |
| NO DETERIORO | 0.8387097 | 0.962963 |
Random Forest: Discriminant TSCLust EUCL
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_FC)
data_frame2 = df_descriptive
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]
data_frame_merge_EUCL$CLUSTER = factor(data_frame_merge_EUCL$CLUSTER)table(data_frame_merge_EUCL$CLUSTER)##
## 1 2
## 31 27data_frame_merge_EUCL[,c(1:dim(data_frame_merge_EUCL)[2])]<- lapply(data_frame_merge_EUCL[,c(1:dim(data_frame_merge_EUCL)[2])], as.numeric)
head(data_frame_merge_EUCL)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 2 13.0 7.78 40 56 2.00 2 2 0
## 3 2 3.1 5.66 37 44 1.00 4 4 0
## 4 2 5.3 8.44 38 65 0.40 3 3 0
## 5 2 15.0 7.00 34 37 2.00 4 4 0
## 6 1 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_EUCL$CLUSTER <- factor(data_frame_merge_EUCL$CLUSTER)
newSMOTE_EUCL <- data_frame_merge_EUCL
table(newSMOTE_EUCL$CLUSTER)##
## 1 2
## 31 27set.seed(123)
pos_1 = get_column_position(newSMOTE_EUCL, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_EUCL, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_EUCL[pos_1:pos_2])
newSMOTE_EUCL[col_names_factor] <- lapply(newSMOTE_EUCL[col_names_factor] , factor)
RF_EUCL <- randomForest(CLUSTER ~ ., data = newSMOTE_EUCL)
print(RF_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 39.66%
## Confusion matrix:
## 1 2 class.error
## 1 19 12 0.3870968
## 2 11 16 0.4074074Importance
kable(RF_EUCL$importance[order(RF_EUCL$importance, decreasing = TRUE),])| x | |
|---|---|
| PESO | 3.4318174 |
| SCORE_WOOD_DOWNES_INGRESO | 3.3955906 |
| EDAD | 3.2490817 |
| SCORE_CRUCES_INGRESO | 2.5964057 |
| FR_0_8h | 1.9810445 |
| DIAS_O2_TOTAL | 1.5827384 |
| EG | 1.3904874 |
| DIAS_GN | 1.3142980 |
| FLUJO2_0_8H | 1.2558653 |
| SAPI_0_8h | 1.1587460 |
| TABACO | 0.9726009 |
| ETIOLOGIA | 0.8091265 |
| SEXO | 0.6762767 |
| LM | 0.5816534 |
| ENFERMEDAD_BASE | 0.5106344 |
| ALIMENTACION | 0.4480972 |
| RADIOGRAFIA | 0.4361207 |
| ANALITICA | 0.3000126 |
| ALERGIAS | 0.2458116 |
| GN_INGRESO | 0.2291434 |
| SUERO | 0.2111681 |
| DERMATITIS | 0.1924494 |
| DIAS_OAF | 0.1634307 |
| PREMATURIDAD | 0.1561542 |
| PALIVIZUMAB | 0.1529051 |
| PAUSAS_APNEA | 0.1406046 |
| OAF | 0.1380559 |
| DETERIORO | 0.1107912 |
| OAF_TRAS_INGRESO | 0.1019522 |
| SNG | 0.0984322 |
| UCIP | 0.0300226 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the TS-data
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_FC)
data_frame2_EUCL = data.frame(datos_EUCL)
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2_EUCL, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]set.seed(123)
data_frame_merge_EUCL$CLUSTER <- as.factor(data_frame_merge_EUCL$CLUSTER)
RF_0_EUCL <- randomForest(CLUSTER ~ ., data = data_frame_merge_EUCL)
print(RF_0_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 6.9%
## Confusion matrix:
## 1 2 class.error
## 1 29 2 0.06451613
## 2 2 25 0.07407407plot(RF_0_EUCL$importance, type = "h")
EUCL by clusters
plot_data_EUCL <- data.frame(t(datos))
cluster_data_EUCL <- data.frame(DDclust_EUCL_FC)
plotting_EUCL <- cbind(plot_data_EUCL, cluster_data_EUCL)
head(plotting_EUCL)## X1 X2 X3 X4 X5 X6 X7
## ACR_11231843 145.0000 141.0000 149.0000 167.0000 179.0000 167.0000 160.0000
## ADAO_11159808 128.0000 131.0000 125.0000 131.0000 125.0000 120.0000 122.0000
## AGG_11236448 131.1166 124.4614 121.7418 128.0487 140.0153 122.1811 122.1771
## AHL_11239959 149.0000 149.0000 131.0000 153.0000 158.0000 154.0000 158.0000
## AJGD_11119689 120.4000 113.6000 120.2000 130.8000 106.6000 110.4000 130.2000
## AMP_11228639 173.0000 148.8234 147.7584 140.0000 144.0000 147.0000 135.0000
## X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21
## ACR_11231843 164.0 165 158.0 151.0 150 151.0 149 164 164 156 150 151 149 148
## ADAO_11159808 122.0 120 119.0 121.0 117 115.0 120 118 121 140 129 126 121 124
## AGG_11236448 130.0 129 125.0 138.0 135 127.0 140 147 156 156 146 143 127 122
## AHL_11239959 146.0 160 163.0 161.0 162 169.0 172 168 163 158 138 131 128 148
## AJGD_11119689 126.2 119 122.4 115.6 109 113.2 129 114 93 111 101 118 97 89
## AMP_11228639 168.0 151 163.0 151.0 155 147.0 142 148 148 152 160 155 159 160
## X22 X23 X24 X25 X26 X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37
## ACR_11231843 151 147 151 156 149 150 146 148 150 159 161 152 150 155 151 157
## ADAO_11159808 116 125 119 124 121 117 116 117 134 128 130 125 131 131 137 135
## AGG_11236448 141 164 165 173 173 172 168 174 159 156 181 139 129 142 129 124
## AHL_11239959 128 133 130 161 145 149 167 151 167 168 169 176 167 151 159 149
## AJGD_11119689 141 111 94 111 108 105 89 141 98 131 87 114 149 87 119 159
## AMP_11228639 159 159 163 171 163 172 163 154 151 155 165 155 149 151 153 150
## X38 X39 X40 X41 X42 X43 X44 X45 X46 X47 X48 X49 X50 X51 X52 X53
## ACR_11231843 159 147 152 149 165 160 160 167 170 173 173 178 171 169 167 166
## ADAO_11159808 139 146 139 138 145 134 134 140 131 147 132 132 125 129 139 121
## AGG_11236448 129 123 140 133 140 116 103 116 134 112 120 121 120 118 127 113
## AHL_11239959 150 164 153 155 154 152 149 144 156 141 145 148 142 144 142 142
## AJGD_11119689 164 152 159 112 107 124 95 99 111 106 128 81 88 88 98 115
## AMP_11228639 157 157 155 152 157 156 157 162 163 161 161 162 160 164 161 169
## X54 X55 X56 X57 X58 X59 X60 X61 X62 X63 X64 X65 X66 X67 X68 X69
## ACR_11231843 164 163 162 159 159 159 156 157 158 159 159 158 153 156 156 161
## ADAO_11159808 132 142 132 126 127 131 133 143 149 139 132 149 134 139 135 136
## AGG_11236448 107 109 135 114 131 134 123 127 120 112 118 135 115 119 130 138
## AHL_11239959 166 135 137 137 138 141 139 137 139 140 138 137 149 156 161 154
## AJGD_11119689 100 81 96 99 98 150 161 132 169 126 171 113 90 97 95 97
## AMP_11228639 164 163 164 159 157 156 159 164 160 160 164 165 167 161 159 167
## X70 X71 X72 X73 X74 X75 X76 X77 X78 X79 X80 X81 X82 X83 X84 X85
## ACR_11231843 162 164 167 164 168 163 162 166 166 169 167 167 163 167 167 167
## ADAO_11159808 153 141 134 146 141 134 137 140 138 128 129 137 135 138 135 135
## AGG_11236448 130 139 132 122 127 153 139 136 131 136 129 132 129 133 140 145
## AHL_11239959 172 158 174 163 164 170 170 160 160 168 161 170 175 159 157 147
## AJGD_11119689 95 94 97 104 96 92 135 95 132 135 148 166 156 146 152 166
## AMP_11228639 162 161 167 159 164 162 156 157 171 165 154 156 157 154 174 161
## X86 X87 X88 X89 X90 X91 X92 X93 X94 X95 X96 X97 X98 X99 X100 X101
## ACR_11231843 164 160 163 161 159 158 158 159 159 160 158 159 157 162 154 158
## ADAO_11159808 135 134 142 133 130 134 143 130 139 134 137 136 134 143 132 131
## AGG_11236448 122 107 107 92 123 103 104 96 99 113 104 97 100 100 102 97
## AHL_11239959 155 146 155 144 149 149 144 142 172 171 141 137 140 138 135 140
## AJGD_11119689 168 137 151 163 120 130 117 121 136 113 168 165 127 87 97 101
## AMP_11228639 171 162 179 168 175 177 157 163 167 168 163 172 161 163 161 170
## X102 X103 X104 X105 X106 X107 X108 X109 X110 X111 X112 X113 X114
## ACR_11231843 157 156 159 157 160 154 164 162 159 164 162 161 164
## ADAO_11159808 136 134 131 128 127 125 144 131 132 132 137 133 131
## AGG_11236448 98 98 95 94 96 91 102 84 97 85 99 101 93
## AHL_11239959 137 133 159 142 136 141 136 134 161 146 133 131 131
## AJGD_11119689 118 142 133 160 120 102 102 105 138 153 118 150 122
## AMP_11228639 170 168 166 168 163 170 165 163 162 163 167 165 164
## X115 X116 X117 X118 X119 X120 X121 X122 X123 X124 X125 X126 X127
## ACR_11231843 161 163 167 167 161 159 161 162 165 160 163 158 162
## ADAO_11159808 125 127 126 133 143 138 146 146 144 152 140 185 178
## AGG_11236448 98 145 161 156 157 155 114 104 128 110 102 109 124
## AHL_11239959 130 131 130 131 133 130 131 135 127 130 131 130 130
## AJGD_11119689 113 156 164 159 128 125 162 161 155 155 155 150 144
## AMP_11228639 156 168 159 166 170 158 156 170 176 163 162 163 169
## X128 X129 X130 X131 X132 X133 X134 X135 X136 X137 X138 X139
## ACR_11231843 158 157 164 159 158 160 166 163 157 157 164 161
## ADAO_11159808 185 149 146 149 133 137 136 135 130 129 131 130
## AGG_11236448 114 124 131 110 109 115 111 115 113 111 111 115
## AHL_11239959 130 129 123 117 125 125 123 122 126 126 151 160
## AJGD_11119689 137 121 135 135 129 126 134 128 131 128 117 118
## AMP_11228639 156 170 164 154 165 159 173 168 171 163 172 165
## X140 X141 X142 X143 X144 X145 X146 X147
## ACR_11231843 156.0000 160.000 155.0000 158.0000 157.0000 160.0000 156 154
## ADAO_11159808 130.0000 125.000 123.0000 128.0000 131.0000 126.0000 118 119
## AGG_11236448 117.0000 120.000 114.0000 111.0000 115.0000 113.0000 120 110
## AHL_11239959 139.6583 137.713 140.2028 140.9207 143.7436 142.2225 160 165
## AJGD_11119689 120.0000 149.000 119.0000 123.0000 119.0000 128.0000 121 126
## AMP_11228639 142.0000 156.000 148.0000 149.0000 147.0000 142.0000 154 157
## X148 X149 X150 X151 X152 X153 X154 X155 X156 X157 X158 X159 X160
## ACR_11231843 156 159 161 156 151 159 178 182 164 156 155 152 151
## ADAO_11159808 127 127 117 128 130 127 137 129 132 134 135 131 139
## AGG_11236448 103 109 111 123 106 113 113 109 110 104 104 100 101
## AHL_11239959 148 138 153 159 164 150 156 115 107 123 123 134 136
## AJGD_11119689 148 140 127 135 164 133 113 116 104 122 167 113 124
## AMP_11228639 150 142 164 150 154 168 148 146 146 139 134 151 149
## X161 X162 X163 X164 X165 X166 X167 X168 X169 X170 X171 X172 X173
## ACR_11231843 150 147 150 153 150 151 149 152 155 156 154 159 159
## ADAO_11159808 130 139 122 121 125 143 121 130 129 129 125 130 135
## AGG_11236448 107 92 105 105 103 101 104 103 105 100 105 107 98
## AHL_11239959 148 137 141 146 143 132 141 158 134 132 142 148 148
## AJGD_11119689 169 136 158 161 128 110 120 154 108 144 144 140 116
## AMP_11228639 150 154 160 164 146 150 146 146 146 143 144 141 151
## X174 X175 X176 X177 X178 X179 X180 X181 X182 X183 X184 X185 X186
## ACR_11231843 163 161 155 155 165 156 156 143 151 157 153 155 152
## ADAO_11159808 160 164 160 161 137 143 140 140 135 130 157 143 134
## AGG_11236448 106 103 103 100 101 103 106 97 104 99 96 103 98
## AHL_11239959 130 157 114 124 133 121 125 121 125 134 128 128 132
## AJGD_11119689 158 128 155 111 127 103 142 104 98 106 144 113 121
## AMP_11228639 163 160 148 157 144 145 143 158 137 143 144 151 138
## X187 X188 X189 X190 X191 X192 X193 X194 X195 X196 X197 X198 X199
## ACR_11231843 168 163 159 152 151 153 147 150 154 154 153 163 160
## ADAO_11159808 138 147 142 130 123 120 116 127 143 123 123 129 121
## AGG_11236448 130 113 132 112 102 114 108 99 99 99 103 102 104
## AHL_11239959 131 134 127 130 130 131 130 131 127 123 134 130 130
## AJGD_11119689 99 105 93 97 122 100 103 98 98 103 157 118 135
## AMP_11228639 143 144 149 150 153 138 147 143 146 162 145 148 150
## X200 X201 X202 X203 X204 X205 X206 X207 X208 X209 X210 X211 X212
## ACR_11231843 171 167 167 167 171 174 171 168 165 167 169 166 163
## ADAO_11159808 123 121 121 118 122 123 119 118 121 117 114 117 124
## AGG_11236448 106 106 103 101 97 98 100 104 102 103 104 102 108
## AHL_11239959 130 126 127 128 132 126 127 130 130 132 132 126 122
## AJGD_11119689 102 101 97 98 154 127 92 90 98 95 100 98 106
## AMP_11228639 138 135 148 138 139 144 145 139 137 140 133 133 137
## X213 X214 X215 X216 X217 X218 X219 X220 X221 X222 X223 X224 X225
## ACR_11231843 159 157 155 155 153 151 154 153 153 153 150 155 150
## ADAO_11159808 121 112 115 116 120 115 117 118 115 112 136 111 117
## AGG_11236448 126 109 99 108 103 97 105 111 116 113 110 111 112
## AHL_11239959 123 124 128 128 130 131 133 132 127 127 131 127 122
## AJGD_11119689 132 130 111 116 145 125 96 103 113 97 115 110 145
## AMP_11228639 133 137 139 138 135 130 139 140 137 138 139 138 136
## X226 X227 X228 X229 X230 X231 X232 X233 X234 X235 X236 X237 X238
## ACR_11231843 152 149 143 153 158 160 157 157 157 154 167 157 153
## ADAO_11159808 122 123 125 117 117 122 121 122 135 135 118 120 121
## AGG_11236448 106 111 122 118 106 119 122 113 120 112 110 116 118
## AHL_11239959 131 130 133 131 128 129 145 130 131 134 143 129 133
## AJGD_11119689 107 135 119 154 101 121 152 118 103 133 108 82 139
## AMP_11228639 137 138 141 134 141 136 140 137 137 141 142 154 144
## X239 X240 X241 X242 X243 X244 X245 X246 X247 X248 X249 X250 X251
## ACR_11231843 158 151 154 160 158 154 158 161 157 159 149 160 162
## ADAO_11159808 117 123 116 121 120 118 116 114 115 115 116 113 115
## AGG_11236448 110 105 107 112 106 109 113 109 111 110 107 104 105
## AHL_11239959 131 160 121 125 128 148 126 128 130 130 129 129 149
## AJGD_11119689 145 95 158 117 108 159 105 159 101 89 105 154 101
## AMP_11228639 135 147 133 133 139 130 128 129 138 153 143 138 146
## X252 X253 X254 X255 X256 X257 X258 X259 X260 X261 X262 X263 X264
## ACR_11231843 136 141 169 165 161 158 155 151 149 149 157 153 152
## ADAO_11159808 114 114 114 115 114 113 114 126 136 148 146 137 132
## AGG_11236448 107 101 104 101 109 108 109 111 105 105 100 105 108
## AHL_11239959 123 121 125 130 127 127 129 129 130 129 133 132 126
## AJGD_11119689 98 99 99 99 115 95 92 138 139 126 120 116 121
## AMP_11228639 144 129 132 138 136 147 136 141 136 136 153 158 153
## X265 X266 X267 X268 X269 X270 X271 X272 X273 X274 X275 X276 X277
## ACR_11231843 150 161 160 161 162 160 153 153 153 155 153 155 157
## ADAO_11159808 141 140 125 118 118 118 119 130 132 123 132 128 120
## AGG_11236448 112 104 109 103 103 105 106 108 107 106 100 112 104
## AHL_11239959 132 134 131 133 164 169 163 147 157 164 130 125 132
## AJGD_11119689 130 123 108 104 98 102 101 104 97 93 96 105 99
## AMP_11228639 158 157 143 157 152 163 166 145 139 141 133 134 156
## X278 X279 X280 X281 X282 X283 X284 X285 X286 X287 X288 X289 X290
## ACR_11231843 157 158 155 155 157 154 151 154 158 154 155 155 157
## ADAO_11159808 124 127 120 136 127 128 151 157 130 137 129 136 122
## AGG_11236448 105 104 95 92 130 125 157 133 118 122 113 102 126
## AHL_11239959 128 132 156 130 130 130 135 145 174 166 139 139 158
## AJGD_11119689 104 101 102 98 97 94 92 93 100 98 95 95 92
## AMP_11228639 149 144 132 138 161 143 148 151 146 168 140 138 152
## X291 X292 X293 X294 X295 X296 X297 X298 X299 X300 X301 X302 X303
## ACR_11231843 151 149 158 154 156 156 156 163 156 155 151 155 156
## ADAO_11159808 129 122 124 123 124 141 131 127 136 141 152 141 133
## AGG_11236448 107 102 112 119 114 151 170 158 165 144 146 137 125
## AHL_11239959 160 159 153 164 175 169 151 166 142 136 129 129 129
## AJGD_11119689 94 96 93 91 92 88 88 86 86 91 89 85 91
## AMP_11228639 148 145 148 150 148 152 146 148 157 153 146 148 147
## X304 X305 X306 X307 X308 X309 X310 X311 X312 X313 X314 X315 X316
## ACR_11231843 154 157 152 156 156 159 153 154 157 156 160 157 153
## ADAO_11159808 122 126 127 122 123 112 123 129 130 130 129 134 131
## AGG_11236448 119 112 115 102 101 135 109 107 109 131 136 114 112
## AHL_11239959 127 126 127 128 156 120 127 128 166 126 126 140 122
## AJGD_11119689 80 85 83 88 88 89 89 87 90 92 88 87 87
## AMP_11228639 143 150 147 148 140 147 148 150 150 148 147 148 152
## X317 X318 X319 X320 X321 X322 X323 X324 X325 X326 X327 X328 X329
## ACR_11231843 154 156 152 152 156 151 154 154 152 157 154 146 155
## ADAO_11159808 135 134 136 133 132 133 142 133 134 131 129 135 133
## AGG_11236448 122 109 120 121 118 121 114 116 114 120 120 122 122
## AHL_11239959 126 127 125 127 125 131 127 147 123 125 122 132 128
## AJGD_11119689 87 87 86 83 86 86 91 106 119 133 124 96 89
## AMP_11228639 145 146 147 147 145 147 148 146 148 147 150 144 144
## X330 X331 X332 X333 X334 X335 X336 X337 X338 X339 X340 X341 X342
## ACR_11231843 151 150 146 147 157 150 147 151 149 149 147 153 147
## ADAO_11159808 124 134 126 127 124 123 120 121 119 127 123 122 121
## AGG_11236448 114 125 105 115 128 120 116 120 113 120 120 105 113
## AHL_11239959 130 131 133 131 138 137 128 135 132 125 123 123 123
## AJGD_11119689 128 123 103 95 73 136 104 139 97 128 149 98 108
## AMP_11228639 148 146 146 143 144 141 137 154 149 151 150 153 162
## X343 X344 X345 X346 X347 X348 X349 X350 X351 X352 X353 X354 X355
## ACR_11231843 152 148 153 152 168 165 156 153 151 150 160 150 149
## ADAO_11159808 121 120 122 122 122 122 122 124 121 121 120 120 119
## AGG_11236448 110 112 117 120 122 120 120 118 118 117 117 117 113
## AHL_11239959 122 127 127 125 132 130 132 131 130 130 131 152 121
## AJGD_11119689 106 118 135 93 115 103 103 102 86 112 134 106 125
## AMP_11228639 138 146 146 140 154 148 139 141 144 158 139 141 139
## X356 X357 X358 X359 X360 X361 X362 X363 X364 X365 X366 X367 X368
## ACR_11231843 147 147 148 150 147 146 151 147 151 150 148 149 148
## ADAO_11159808 120 120 121 121 136 133 132 146 121 135 125 131 126
## AGG_11236448 117 117 118 111 100 114 104 108 108 114 120 107 119
## AHL_11239959 130 141 127 138 118 121 127 149 132 119 122 125 122
## AJGD_11119689 119 122 122 115 131 104 122 118 107 121 110 111 113
## AMP_11228639 154 137 146 137 141 147 158 136 141 143 140 145 146
## X369 X370 X371 X372 X373 X374 X375 X376 X377 X378 X379 X380 X381
## ACR_11231843 149 149 151 151 154 151 151 151 151 150 153 150 154
## ADAO_11159808 137 126 127 118 120 125 122 123 120 129 123 121 126
## AGG_11236448 113 129 117 119 117 123 119 121 116 107 127 121 123
## AHL_11239959 127 125 115 125 119 149 128 124 126 127 127 123 120
## AJGD_11119689 130 127 128 108 105 116 96 109 123 115 90 106 97
## AMP_11228639 146 147 147 148 146 147 148 147 150 145 145 143 144
## X382 X383 X384 X385 X386 X387 X388 X389 X390 X391 X392 X393 X394
## ACR_11231843 154 153 150 181 156 158 159 155 151 155 153 159 165
## ADAO_11159808 119 120 120 120 120 121 130 133 126 154 134 143 151
## AGG_11236448 116 118 117 110 115 122 120 119 118 117 116 119 118
## AHL_11239959 116 122 122 122 143 147 158 151 147 159 169 176 159
## AJGD_11119689 119 119 120 117 108 111 104 103 92 99 108 98 100
## AMP_11228639 141 139 153 154 143 138 152 145 140 154 153 153 155
## X395 X396 X397 X398 X399 X400 X401 X402 X403 X404 X405 X406 X407
## ACR_11231843 163 166 167 167 172 170 171 173 168 165 162 159 154
## ADAO_11159808 146 135 129 133 127 123 132 130 122 126 129 131 126
## AGG_11236448 108 114 118 110 119 117 117 105 111 112 116 112 124
## AHL_11239959 182 171 176 164 149 141 137 150 151 127 143 140 127
## AJGD_11119689 97 98 141 102 129 106 134 141 132 138 139 134 129
## AMP_11228639 166 150 154 159 142 165 145 145 142 147 167 166 145
## X408 X409 X410 X411 X412 X413 X414 X415 X416
## ACR_11231843 156 157 159 155 149.0000 153.0000 154.0000 159.0000 159.0000
## ADAO_11159808 126 127 135 123 127.0000 126.0000 127.0000 125.0000 124.0000
## AGG_11236448 107 120 130 113 126.0000 112.0000 126.0000 113.0000 130.0000
## AHL_11239959 149 138 138 111 119.0000 117.0000 111.0000 115.0000 116.0000
## AJGD_11119689 131 131 114 124 115.0000 115.0000 153.0000 100.0000 87.0000
## AMP_11228639 152 146 172 167 157.2825 159.1097 157.1008 163.4221 156.5953
## X417 X418 X419 X420 X421 X422 X423
## ACR_11231843 156.0000 153.0000 156.0000 157.0000 157.000 151.0000 155.0000
## ADAO_11159808 125.0000 126.0000 124.0000 126.0000 123.000 122.0000 122.0000
## AGG_11236448 153.0000 148.0000 171.0000 152.0000 145.000 133.0000 128.0000
## AHL_11239959 115.0000 123.0000 117.0000 122.0000 121.000 125.0000 126.0000
## AJGD_11119689 97.0000 102.0000 101.0000 108.0000 106.000 104.0000 92.0000
## AMP_11228639 158.7459 153.3412 154.3436 158.5246 158.935 155.9522 153.4337
## X424 X425 X426 X427 X428 X429 X430 X431 X432 X433 X434 X435 X436
## ACR_11231843 155 155 160 153 156 170 88 143 156 148 153 147 143
## ADAO_11159808 123 122 145 135 137 131 136 140 145 153 145 124 129
## AGG_11236448 117 115 115 109 122 121 147 151 142 116 107 118 124
## AHL_11239959 127 120 114 121 123 119 117 122 121 140 124 127 127
## AJGD_11119689 101 102 104 103 104 104 104 115 100 118 98 105 111
## AMP_11228639 155 158 160 162 152 146 166 152 152 150 156 158 157
## X437 X438 X439 X440 X441 X442 X443 X444 X445 X446 X447 X448 X449
## ACR_11231843 145 145 145 147 151 152 157 154 151 161 153 155 152
## ADAO_11159808 123 121 124 122 125 129 131 135 144 131 136 142 147
## AGG_11236448 109 101 114 122 152 117 105 106 148 131 127 127 128
## AHL_11239959 127 132 122 126 124 125 119 126 127 122 125 121 125
## AJGD_11119689 102 106 95 109 112 123 134 109 125 105 106 107 110
## AMP_11228639 163 161 161 151 164 162 174 156 142 144 151 145 159
## X450 X451 X452 X453 X454 X455 X456 X457 X458 X459 X460 X461 X462
## ACR_11231843 154 161 152 152 156 154 165 178 178 184 180 160 157
## ADAO_11159808 156 133 138 128 135 127 133 139 135 130 133 129 129
## AGG_11236448 119 147 119 151 165 168 142 134 130 130 126 115 120
## AHL_11239959 121 119 125 127 126 125 125 129 124 125 131 114 117
## AJGD_11119689 113 98 91 118 151 135 118 115 111 122 123 138 125
## AMP_11228639 155 158 153 156 178 154 163 158 149 163 159 154 159
## X463 X464 X465 X466 X467 X468 X469 X470 X471 X472 X473 X474 X475
## ACR_11231843 157 157 164 152 158 158 149 149 148 153 150 146 149
## ADAO_11159808 124 130 116 135 135 124 128 140 127 126 132 121 138
## AGG_11236448 142 131 134 121 131 143 126 119 128 122 123 120 122
## AHL_11239959 124 121 119 126 128 129 153 122 135 125 123 121 125
## AJGD_11119689 126 132 112 100 98 152 119 150 148 144 101 109 110
## AMP_11228639 158 157 150 164 151 149 153 150 154 152 150 153 155
## X476 X477 X478 X479 X480 DDclust_EUCL_FC
## ACR_11231843 149 148 153 150 153 1
## ADAO_11159808 122 127 135 128 124 2
## AGG_11236448 122 117 118 114 112 2
## AHL_11239959 112 120 114 120 123 2
## AJGD_11119689 97 99 84 103 143 2
## AMP_11228639 153 153 148 152 151 1## Mean by groups
rp_tbl_EUCL <- aggregate(plotting_EUCL, by = list(plotting_EUCL$DDclust_EUCL_FC), mean)
row.names(rp_tbl_EUCL) <- paste0("Group",rp_tbl_EUCL$DDclust_EUCL_FC)
rp_tbl_EUCL <- rp_tbl_EUCL %>%
select(starts_with('X'))
rp_tbl_EUCL <- data.frame(t(rp_tbl_EUCL))
head(rp_tbl_EUCL)## Group1 Group2
## X1 167.4070 153.6074
## X2 167.6501 152.7367
## X3 166.1007 152.9036
## X4 168.8262 153.6395
## X5 167.6267 151.1619
## X6 164.4864 150.4965# Create plotting data-frame
EUCL_values_by_group <- data.frame("value_EUCL" = c(rp_tbl_EUCL$Group1,rp_tbl_EUCL$Group2),
"cluster" = c(rep("Group1", times = length(rp_tbl_EUCL$Group1)),
rep("Group2", times = length(rp_tbl_EUCL$Group2))),
"index" = c(c(1:length(rp_tbl_EUCL$Group1)),c(1:length(rp_tbl_EUCL$Group2))))
p <- ggplot(EUCL_values_by_group, aes(x = index, y = value_EUCL, group = cluster)) +
geom_line(aes(color=cluster)) +
scale_color_brewer(palette="Paired") + theme_minimal()
p
PER TSclust
# DD_PER <- diss(datos, "PER")
DD_PER <- distance_PERAgnes study
To find which hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.
#method to assess
m <- c("average", "single","complete","ward")
names(m) <- c("average", "single","complete","ward.D2")
#function to compute coefficient
ac <- function(x){agnes(datos_PER, method = x)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.7642414 0.6328132 0.8636011 0.9278630NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_PER
res<-NbClust(datos_PER, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.5349 0.2270 0.2618 0.2698res$Best.nc## Number_clusters Value_Index
## 2.0000 0.5349#res$Best.partitionhcintper_PER <- hclust(DD_PER, "ward.D2")
fviz_dend(hcintper_PER, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)
DDclust_PER_FC <- cutree( hclust(DD_PER, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_PER_FC))
fviz_silhouette(silhouette(DDclust_PER_FC, DD_PER))## cluster size ave.sil.width
## 1 1 50 0.56
## 2 2 8 0.35
Contingency PER
DETERIORO_CLUST <- union(intersect(file_patient_name_DETERIORO,names_1),intersect(file_patient_name_DETERIORO,names_2))
NO_DETERIORO_CLUST <- union(intersect(file_patient_name_NO_DETERIORO,names_1),intersect(file_patient_name_NO_DETERIORO,names_2))#DETERIORO
DETERIORO_patients = data.frame(t(rep("#4A235A", length(DETERIORO_CLUST))))
colnames(DETERIORO_patients)<- DETERIORO_CLUST
#NO DETERIORO
NO_DETERIORO_patients = data.frame(t(rep("#117864", length(NO_DETERIORO_CLUST))))
colnames(NO_DETERIORO_patients)<- NO_DETERIORO_CLUST
COLOR_PER <- cbind(NO_DETERIORO_patients,DETERIORO_patients)order_PER <- union(names(DDclust_PER_FC[DDclust_PER_FC == 2]),names(DDclust_PER_FC[DDclust_PER_FC == 1]))
fviz_dend(hcintper_PER, k = 2,
k_colors = c("blue", "green3"),
label_cols = as.vector(COLOR_PER[,order_PER]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLUST1" = c(n1,n3), "CLUST2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLUST1 | CLUST2 | |
|---|---|---|
| DETERIORO | 5 | 1 |
| NO DETERIORO | 45 | 7 |
conttingency_table_prop <- data.frame(c(n1,n3)/(n1+n3),c(n2,n4)/(n2+n4))
rownames(conttingency_table_prop) <- c("DETERIORO","NO DETERIORO")
colnames(conttingency_table_prop) <- c("CLUST1","CLUST2")
knitr::kable(conttingency_table_prop, align = "lccrr")| CLUST1 | CLUST2 | |
|---|---|---|
| DETERIORO | 0.1 | 0.125 |
| NO DETERIORO | 0.9 | 0.875 |
Random Forest: Discriminant TSCLust PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_FC)
data_frame2_PER = df_descriptive
data_frame_merge_PER <-
merge(data_frame1_PER, data_frame2_PER, by = 'row.names', all = TRUE)
data_frame_merge_PER <- data_frame_merge_PER[, 2:dim(data_frame_merge_PER)[2]]
data_frame_merge_PER$CLUSTER = factor(data_frame_merge_PER$CLUSTER)table(data_frame_merge_PER$CLUSTER)##
## 1 2
## 50 8data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])]<- lapply(data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])], as.numeric)
head(data_frame_merge_PER)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 1 13.0 7.78 40 56 2.00 2 2 0
## 3 1 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 1 15.0 7.00 34 37 2.00 4 4 0
## 6 1 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_PER$CLUSTER <- factor(data_frame_merge_PER$CLUSTER)
newSMOTE_PER <- oversample(data_frame_merge_PER, ratio = 0.85, method = "SMOTE", classAttr = "CLUSTER")
newSMOTE_PER <- data.frame(newSMOTE_PER)
pos_1 <- get_column_position(newSMOTE_PER, "SAPI_0_8h")
pos_2 <- get_column_position(newSMOTE_PER, "PAUSAS_APNEA")
columns_to_round <- c(pos_1:pos_2)
newSMOTE_PER[, columns_to_round] <- lapply(newSMOTE_PER[, columns_to_round], function(x) round(x, 1))
table(newSMOTE_PER$CLUSTER)##
## 1 2
## 50 43set.seed(123)
pos_1 = get_column_position(newSMOTE_PER, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_PER, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_PER[pos_1:pos_2])
newSMOTE_PER[col_names_factor] <- lapply(newSMOTE_PER[col_names_factor] , factor)
RF_PER <- randomForest(CLUSTER ~ ., data = newSMOTE_PER)
print(RF_PER)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_PER)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 6.45%
## Confusion matrix:
## 1 2 class.error
## 1 49 1 0.0200000
## 2 5 38 0.1162791Importance
kable(RF_PER$importance[order(RF_PER$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_CRUCES_INGRESO | 8.9852612 |
| SCORE_WOOD_DOWNES_INGRESO | 5.2582849 |
| RADIOGRAFIA | 5.1448815 |
| SAPI_0_8h | 4.2663169 |
| PESO | 2.6165359 |
| ETIOLOGIA | 2.1637110 |
| EDAD | 1.9886479 |
| LM | 1.9671169 |
| FR_0_8h | 1.5698333 |
| DIAS_O2_TOTAL | 1.5663579 |
| TABACO | 1.4980119 |
| DIAS_GN | 1.3807663 |
| PREMATURIDAD | 1.2288103 |
| FLUJO2_0_8H | 1.0161275 |
| EG | 0.8699305 |
| SEXO | 0.6483764 |
| ENFERMEDAD_BASE | 0.6423322 |
| ALIMENTACION | 0.6027475 |
| ANALITICA | 0.5729907 |
| SUERO | 0.3472359 |
| DETERIORO | 0.1834765 |
| OAF_TRAS_INGRESO | 0.1731880 |
| DIAS_OAF | 0.1689132 |
| OAF | 0.1644906 |
| PALIVIZUMAB | 0.1419329 |
| ALERGIAS | 0.1404927 |
| GN_INGRESO | 0.0912084 |
| SNG | 0.0738434 |
| UCIP | 0.0392519 |
| DERMATITIS | 0.0361894 |
| PAUSAS_APNEA | 0.0097405 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_FC)
data_frame2_PER = data.frame(datos_PER)
data_frame_merge_PER <-
merge(data_frame1_PER, data_frame2_PER, by = 'row.names', all = TRUE)
data_frame_merge_PER <- data_frame_merge_PER[, 2:dim(data_frame_merge_PER)[2]]set.seed(123)
data_frame_merge_PER$CLUSTER <- as.factor(data_frame_merge_PER$CLUSTER)
RF_0_PER <- randomForest(CLUSTER ~ ., data = data_frame_merge_PER)
print(RF_0_PER)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_PER)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 13.79%
## Confusion matrix:
## 1 2 class.error
## 1 50 0 0
## 2 8 0 1plot(RF_0_PER$importance, type = "h")
### PER by clusters
plot_data_PER <- data.frame(datos_PER)
cluster_data_PER <- data.frame(DDclust_PER_FC)
plotting_PER <- cbind(plot_data_PER, cluster_data_PER)
head(plotting_PER)## X1 X2 X3 X4 X5 X6
## ACR_11231843 348.3863 22.29866 409.3245 72.24925 321.9848 424.0674
## ADAO_11159808 2044.5415 1265.47449 1351.9694 1210.22532 264.4413 140.8006
## AGG_11236448 11268.6872 2030.76692 151.7232 1046.26609 1854.7914 991.9672
## AHL_11239959 2249.7759 2031.91033 811.1544 3220.16092 1938.9812 1994.9735
## AJGD_11119689 7094.5509 9873.09102 1052.2402 6077.83782 1206.4612 1990.7986
## AMP_11228639 7892.0056 2259.48911 3012.6365 508.49958 33.9347 288.1758
## X7 X8 X9 X10 X11 X12
## ACR_11231843 910.26473 131.74415 239.92825 252.02903 11.21741 264.25020
## ADAO_11159808 831.15521 467.38213 1167.68952 826.15021 644.38731 270.67798
## AGG_11236448 771.80575 950.16815 3312.61080 1473.54570 2661.50832 222.57669
## AHL_11239959 516.10161 2656.34800 2446.43229 449.74646 324.75012 1989.55872
## AJGD_11119689 13.33513 1318.46805 744.23940 1610.32601 233.77550 2882.88625
## AMP_11228639 114.02795 11.89135 31.15406 16.77033 86.46627 41.75092
## X13 X14 X15 X16 X17 X18
## ACR_11231843 293.95507 262.6576 401.07634 117.71846 55.742958 358.00352
## ADAO_11159808 33.56197 479.9267 541.05419 505.06088 687.619889 1011.34009
## AGG_11236448 1080.22766 263.2256 192.20104 1205.69580 92.992757 130.49854
## AHL_11239959 938.55828 237.5895 2160.15214 111.39618 81.379991 59.10440
## AJGD_11119689 764.40399 1459.6717 283.15964 497.35252 1873.122731 1893.23851
## AMP_11228639 114.75954 329.9692 28.11677 52.24031 7.547195 76.49546
## X19 X20 X21 X22 X23 X24
## ACR_11231843 182.10648 91.87253 22.88719 154.8910 65.780868 1.392005
## ADAO_11159808 30.92282 35.95631 96.13376 715.9380 235.995670 76.638298
## AGG_11236448 289.75392 16.92866 1037.73923 154.4910 37.582031 299.865090
## AHL_11239959 878.51500 678.09376 199.54941 264.0684 663.414926 1222.355550
## AJGD_11119689 713.25739 103.53796 3136.60321 1180.6349 472.554167 2468.332790
## AMP_11228639 43.59810 115.46191 30.76334 291.0160 5.110457 29.276310
## X25 X26 X27 X28 X29 X30
## ACR_11231843 139.23794 29.93685 300.09123 34.73585 58.27851 121.99195
## ADAO_11159808 256.39953 242.84672 403.21566 354.81157 117.40582 365.15476
## AGG_11236448 35.42327 912.72521 972.98997 243.81064 855.80175 85.08751
## AHL_11239959 83.65540 246.20807 61.77270 200.20723 66.16912 65.75803
## AJGD_11119689 549.96549 240.86774 71.69257 281.69095 660.69968 600.30732
## AMP_11228639 21.23936 118.88873 71.04967 41.36850 19.34578 199.24587
## X31 X32 X33 X34 X35 X36
## ACR_11231843 55.03412 45.01887 17.322892 53.609133 25.8584217 10.547630
## ADAO_11159808 183.38619 33.25127 297.206476 10.987478 0.5893427 367.872991
## AGG_11236448 47.04513 1065.92072 145.952794 475.608572 353.1913409 385.175211
## AHL_11239959 100.63393 226.66159 7.226275 242.673177 234.0012916 620.656751
## AJGD_11119689 732.01483 237.03530 324.118121 656.379251 464.4854942 54.184125
## AMP_11228639 76.55375 100.20715 368.261970 9.144192 130.1483208 5.504135
## X37 X38 X39 X40 X41 X42
## ACR_11231843 66.3661560 48.73835 7.406187 24.98301 141.908019 103.44403
## ADAO_11159808 126.3487258 214.57486 334.953092 77.57094 1.206459 55.61927
## AGG_11236448 814.8109073 108.27561 223.124670 1595.16871 176.687613 217.54232
## AHL_11239959 0.6775345 254.95084 304.048318 126.36189 161.317064 127.92756
## AJGD_11119689 183.8970065 926.80438 326.484165 366.73191 19.894166 334.65882
## AMP_11228639 17.9157013 180.80658 133.113999 52.81269 33.336818 16.36910
## X43 X44 X45 X46 X47 X48
## ACR_11231843 107.09436 30.18085 29.956095 43.623334 5.294672 134.86186
## ADAO_11159808 110.84276 101.29729 66.900785 8.721455 15.386726 33.79442
## AGG_11236448 849.35169 228.76225 4.944967 596.828911 10.508435 338.28683
## AHL_11239959 374.98924 95.78602 70.158977 76.632370 79.045753 13.85959
## AJGD_11119689 1056.17735 899.47948 123.079813 97.678292 182.962674 152.11053
## AMP_11228639 75.59077 42.01278 50.651694 49.651401 52.198570 10.27032
## X49 X50 X51 X52 X53 X54
## ACR_11231843 20.576737 11.091409 6.799941 18.56097 5.541049 3.523825
## ADAO_11159808 264.224969 213.497924 66.440252 63.61097 3.736728 1.310419
## AGG_11236448 110.025742 3.770932 111.458221 71.38763 180.316146 62.557459
## AHL_11239959 188.979711 6.748206 4.650608 12.32290 47.488803 89.957310
## AJGD_11119689 7.949797 347.317863 201.999642 458.85538 408.574619 206.410862
## AMP_11228639 2.359794 31.600895 11.512225 37.65142 23.078457 17.967709
## X55 X56 X57 X58 X59 X60
## ACR_11231843 15.428820 28.423519 62.58629 55.52865 38.627533 60.41682
## ADAO_11159808 25.504181 12.329279 10.31265 202.36457 63.931030 37.66273
## AGG_11236448 217.735580 344.587158 172.43477 207.71999 2.512771 20.91686
## AHL_11239959 110.727335 8.553674 183.86477 60.16570 55.081875 77.44528
## AJGD_11119689 39.029241 443.559422 123.61696 531.79592 558.466318 352.54997
## AMP_11228639 1.869597 26.363299 33.22283 53.74638 14.006948 12.35452
## X61 X62 X63 X64 X65 X66
## ACR_11231843 43.57834 162.5350377 4.260936 9.763429 2.884342 4.190182
## ADAO_11159808 226.70469 0.4796529 48.541290 100.452810 12.011168 18.078309
## AGG_11236448 184.92266 30.1281999 29.557721 82.162732 170.161284 54.051784
## AHL_11239959 53.79810 63.0062963 13.620459 43.802454 68.817901 187.043727
## AJGD_11119689 371.78956 46.4856410 54.587319 123.442734 874.985366 272.193179
## AMP_11228639 61.64142 7.9180100 5.658281 133.367049 94.294497 20.336480
## X67 X68 X69 X70 X71 X72
## ACR_11231843 24.18499 4.473226 2.649457 27.801453 4.2577475 13.11485
## ADAO_11159808 110.77871 24.824655 40.491695 26.450267 28.8174499 117.66970
## AGG_11236448 100.44518 47.006286 139.818855 127.433481 0.3425516 68.59093
## AHL_11239959 144.61331 274.585219 213.665734 7.945876 17.7810437 81.82055
## AJGD_11119689 1107.80888 243.429501 123.239037 276.139301 808.6988609 740.86028
## AMP_11228639 97.12129 66.474995 46.722698 72.819738 59.8111572 45.83738
## X73 X74 X75 X76 X77 X78
## ACR_11231843 80.340543 30.9414819 23.32626 71.70075 47.57655 90.945173
## ADAO_11159808 39.729756 14.7744220 84.15867 10.08191 27.55966 9.833679
## AGG_11236448 22.449675 102.0842652 275.07424 78.53754 37.65235 48.110982
## AHL_11239959 186.459352 5.6321980 100.18736 42.57903 1.94692 43.275164
## AJGD_11119689 89.824216 297.7107049 204.90412 351.52788 152.03528 178.623753
## AMP_11228639 3.731377 0.3142418 95.66031 26.04847 13.48961 65.946607
## X79 X80 X81 X82 X83 X84
## ACR_11231843 56.205045 14.07852 59.996110 5.234073 14.58238 23.804173
## ADAO_11159808 69.787965 78.58678 33.916417 4.374651 45.80617 11.389666
## AGG_11236448 220.862364 176.82135 24.815956 25.165631 139.19039 2.202261
## AHL_11239959 54.528927 26.54190 37.707648 277.502678 40.18441 69.493727
## AJGD_11119689 3.730390 68.51892 330.765304 499.236006 485.44468 338.021945
## AMP_11228639 8.157112 10.64790 8.876751 24.767964 19.87097 104.040998
## X85 X86 X87 X88 X89 X90
## ACR_11231843 67.37103 19.76578 10.108986 44.80942 0.4314662 87.882562
## ADAO_11159808 137.47895 135.60058 64.826560 32.44564 20.8145411 6.439392
## AGG_11236448 55.20123 170.47665 20.455483 178.58071 159.2572140 55.610500
## AHL_11239959 208.67442 141.40959 152.585528 19.73715 4.5394263 79.177857
## AJGD_11119689 62.29809 339.71690 2.727953 481.45007 97.3236436 375.512934
## AMP_11228639 112.38317 25.45809 43.797483 17.98346 63.7051146 138.293797
## X91 X92 X93 X94 X95 X96
## ACR_11231843 16.18456 11.76147 22.427247 43.67267 48.39041 33.036283
## ADAO_11159808 47.18262 85.92498 13.648709 96.17456 32.88860 8.504681
## AGG_11236448 78.39920 26.68607 2.392703 76.18619 245.14011 58.998472
## AHL_11239959 108.99752 57.88870 174.788483 145.52832 37.69597 125.125240
## AJGD_11119689 283.42605 398.29423 358.027899 656.51214 117.22896 7.411211
## AMP_11228639 88.24497 85.74665 19.915753 37.40505 18.55393 27.084072
## X97 X98 X99 X100 X101 X102
## ACR_11231843 21.888520 18.316180 12.71874 83.05418 3.663976 110.40502
## ADAO_11159808 9.335965 6.623210 25.48751 96.25737 69.779461 8.80107
## AGG_11236448 142.155138 8.761375 79.86648 98.99652 328.377704 101.01476
## AHL_11239959 31.860271 228.695673 45.84267 41.28917 3.617375 107.36383
## AJGD_11119689 342.796759 51.722356 219.81387 37.52951 168.992870 488.87355
## AMP_11228639 130.119225 42.936500 27.04814 98.96809 18.160248 23.60377
## X103 X104 X105 X106 X107 X108
## ACR_11231843 16.295266 32.72415 17.13538 9.19236758 48.088190 83.458860
## ADAO_11159808 2.260767 10.18050 23.39041 36.71574871 105.205348 18.074168
## AGG_11236448 29.243827 89.11253 13.59382 68.54142938 87.994157 125.289677
## AHL_11239959 2.821220 43.86895 38.60649 0.07107527 8.740787 97.574032
## AJGD_11119689 289.537274 91.99522 419.42506 52.01094497 17.069693 488.817218
## AMP_11228639 5.902560 34.66156 41.70199 20.35818452 7.135223 5.080574
## X109 X110 X111 X112 X113 X114
## ACR_11231843 3.954442 13.32374 35.683155 19.314187 32.106229 50.732030
## ADAO_11159808 51.568126 154.49904 1.201206 1.037543 20.856590 5.612337
## AGG_11236448 150.820895 72.53790 66.829812 14.957264 5.539851 104.319852
## AHL_11239959 25.338729 35.78403 5.447354 85.197355 252.798229 314.904281
## AJGD_11119689 186.138506 293.40227 96.733057 23.704723 648.571418 5.075168
## AMP_11228639 40.644134 27.92608 9.675836 2.386868 116.830721 44.099029
## X115 X116 X117 X118 X119 X120
## ACR_11231843 17.892616 41.28745 7.190733 5.8304104 41.478701 22.913039
## ADAO_11159808 25.139582 15.08121 6.315504 0.3793686 67.800729 1.642036
## AGG_11236448 40.076611 24.12978 14.640944 29.1616442 100.143411 31.107951
## AHL_11239959 104.820918 34.38167 10.476464 52.2497851 135.810815 117.801275
## AJGD_11119689 587.259468 473.45879 567.828200 154.4196451 46.270791 463.647626
## AMP_11228639 6.883874 29.07419 125.420924 0.9831770 7.714643 52.370693
## X121 X122 X123 X124 X125 X126
## ACR_11231843 95.685037 55.164721 2.253739 57.609520 13.632755 1.457037
## ADAO_11159808 8.385626 19.518276 46.366626 62.786694 148.567132 51.578888
## AGG_11236448 29.621578 0.366469 40.439735 35.122426 8.885634 14.310926
## AHL_11239959 19.089222 81.433967 9.801134 41.734388 61.152809 67.267496
## AJGD_11119689 412.657057 53.604567 33.056229 6.821248 103.978128 116.484169
## AMP_11228639 2.229305 12.658934 112.011633 18.779024 8.087187 18.272481
## X127 X128 X129 X130 X131 X132
## ACR_11231843 40.739244 17.96603 24.548548 13.463209 6.443771 52.93993
## ADAO_11159808 22.563617 15.07311 9.448053 29.363498 9.081545 23.66586
## AGG_11236448 27.790489 4.70232 176.780377 115.812240 37.916712 16.81850
## AHL_11239959 91.652274 56.31386 67.965190 48.584645 22.535853 60.84074
## AJGD_11119689 7.737438 279.41383 233.624164 7.219063 267.589323 132.96953
## AMP_11228639 22.472939 11.36108 3.557244 36.921134 39.827388 54.13716
## X133 X134 X135 X136 X137 X138
## ACR_11231843 49.676277 5.641364 46.11750 9.153074 10.49148 21.856224
## ADAO_11159808 34.462758 181.632461 80.21512 57.532356 43.14911 4.493098
## AGG_11236448 9.118927 10.440550 39.79872 6.111265 135.59287 65.210129
## AHL_11239959 334.385808 222.688753 39.28932 10.192787 197.00301 24.440151
## AJGD_11119689 19.737670 121.741480 211.80330 89.732031 292.11711 415.326869
## AMP_11228639 3.211792 54.266953 88.02787 4.271698 31.32815 98.605797
## X139 X140 X141 X142 X143 X144
## ACR_11231843 0.4032183 32.366806 4.537283 32.501481 15.25671 34.347901
## ADAO_11159808 17.8035188 29.661011 10.772769 2.536657 23.16254 53.783266
## AGG_11236448 171.5105109 3.143752 70.907509 227.299673 132.95188 38.082128
## AHL_11239959 98.4440230 23.826309 49.229495 62.303999 10.43516 47.539190
## AJGD_11119689 137.3527313 57.163021 48.727410 715.352654 277.44958 232.809748
## AMP_11228639 91.3998161 8.655655 28.503759 14.966507 23.16132 8.718949
## X145 X146 X147 X148 X149 X150
## ACR_11231843 38.736026 3.039466 15.829168 12.04144 11.25694 2.49266
## ADAO_11159808 8.566305 26.694952 2.788876 22.52548 39.91460 19.05122
## AGG_11236448 40.513373 51.430609 136.823679 73.10300 359.23883 43.09692
## AHL_11239959 60.008177 16.024650 49.176827 133.04377 50.62884 318.84514
## AJGD_11119689 79.012414 350.071917 222.105037 181.73419 16.83085 303.02044
## AMP_11228639 20.990693 25.795104 132.746096 25.33968 15.37677 22.50900
## X151 X152 X153 X154 X155 X156
## ACR_11231843 47.30072 44.742466 12.29814 75.785426 15.96699 27.1083640
## ADAO_11159808 49.46168 7.997823 60.55102 41.161244 30.13339 0.2616995
## AGG_11236448 17.81332 61.163396 22.00824 16.030331 90.02268 85.2159661
## AHL_11239959 36.33479 37.717913 108.72114 31.216102 99.32177 84.2191571
## AJGD_11119689 316.98400 61.223728 40.63928 4.103047 535.83938 436.6038876
## AMP_11228639 28.39629 42.714160 10.09216 90.568972 85.35638 11.9370369
## X157 X158 X159 X160 X161 X162
## ACR_11231843 45.95646 0.1210798 40.776904 22.383541 2.766273 43.239353
## ADAO_11159808 22.03423 17.9781397 5.267311 1.781083 30.538443 42.411914
## AGG_11236448 44.33659 41.2570209 40.349971 11.659568 52.620328 54.495582
## AHL_11239959 44.76072 33.3372597 26.706942 30.653957 2.112994 42.865180
## AJGD_11119689 336.68747 544.1652639 14.026822 347.342580 162.041963 538.640098
## AMP_11228639 27.89045 41.0011328 44.213850 12.061124 69.523363 8.805054
## X163 X164 X165 X166 X167 X168
## ACR_11231843 13.741907 84.529370 117.250227 13.375122 42.56509 34.28050
## ADAO_11159808 9.076199 40.126273 29.157689 97.221461 21.91342 34.18008
## AGG_11236448 44.830644 61.830382 100.366014 8.326845 86.93767 59.60235
## AHL_11239959 91.793861 66.893313 23.298347 4.215710 40.84039 27.42377
## AJGD_11119689 457.466282 22.333463 4.725218 256.760241 283.43004 119.09126
## AMP_11228639 20.486618 4.577062 44.024013 23.415008 58.19166 51.67915
## X169 X170 X171 X172 X173 X174
## ACR_11231843 3.639098 21.704368 0.192723 64.316913 40.330819 23.68798
## ADAO_11159808 8.405364 5.502808 11.255454 21.466028 15.964233 28.37875
## AGG_11236448 15.444968 7.849136 27.965611 5.293785 76.802196 12.51821
## AHL_11239959 119.537582 16.328687 48.784038 15.199396 3.107826 24.73594
## AJGD_11119689 709.467437 22.684400 184.051175 313.218699 207.957911 548.57790
## AMP_11228639 11.790435 63.440258 70.809161 8.405446 24.443895 129.71361
## X175 X176 X177 X178 X179 X180
## ACR_11231843 40.82167 23.7900347 45.11949 43.50358 22.723639 9.313692
## ADAO_11159808 50.24005 0.8022005 38.26750 8.10573 1.043840 9.889475
## AGG_11236448 48.48196 9.8416125 63.80561 30.89745 2.358648 23.467832
## AHL_11239959 30.18812 2.8722651 10.76408 22.97082 51.281025 2.852812
## AJGD_11119689 343.39759 242.4180358 310.77116 115.87615 140.270056 351.047693
## AMP_11228639 103.70657 27.9670488 14.14361 20.41486 48.662240 17.969358
## X181 X182 X183 X184 X185 X186
## ACR_11231843 1.261902 0.2159353 50.72558 11.13419 16.524788 19.21838
## ADAO_11159808 82.896544 73.8020506 35.26160 39.24148 25.660765 18.83864
## AGG_11236448 32.083150 92.0116763 93.78611 8.51574 16.965149 160.07034
## AHL_11239959 49.575698 47.2374400 122.62801 19.53437 10.900321 80.35366
## AJGD_11119689 56.982064 125.1029347 116.17819 280.47515 500.866847 69.95154
## AMP_11228639 17.465273 11.1884816 36.33052 29.72711 9.163052 22.25266
## X187 X188 X189 X190 X191 X192
## ACR_11231843 46.67731 16.65716 42.450086 15.496901 17.29504 11.312551
## ADAO_11159808 11.04706 76.41117 78.737583 56.400966 12.47871 7.081923
## AGG_11236448 16.66365 77.90671 28.822138 6.382938 85.66442 37.560970
## AHL_11239959 104.13201 28.04031 7.124077 99.566978 26.11656 12.453738
## AJGD_11119689 177.28188 92.95068 9.012681 185.005304 390.75076 30.257458
## AMP_11228639 22.56451 59.58724 21.441088 8.855096 108.24073 24.596916
## X193 X194 X195 X196 X197 X198
## ACR_11231843 6.423861 5.103376 76.85062 33.791074 47.24277 11.550629
## ADAO_11159808 16.240655 32.553304 13.09777 1.082000 66.07276 9.416056
## AGG_11236448 169.461545 1.593565 11.13518 62.070057 108.68741 15.528712
## AHL_11239959 1.954401 27.508108 29.90202 191.247570 50.96500 40.047723
## AJGD_11119689 466.351893 128.532334 1033.09546 70.705233 40.93867 212.422835
## AMP_11228639 32.705333 28.530489 37.58171 7.525454 51.46679 82.671648
## X199 X200 X201 X202 X203 X204
## ACR_11231843 20.45810 2.044473 38.11751 24.17440 2.754627 14.83970
## ADAO_11159808 15.54622 17.713935 14.34175 14.94304 63.483565 34.69222
## AGG_11236448 16.25954 24.129652 71.18947 61.40976 1.233862 16.70792
## AHL_11239959 19.19769 19.630410 50.63597 45.89591 56.783553 41.78972
## AJGD_11119689 180.31412 68.118085 454.83126 158.04834 269.832741 247.36795
## AMP_11228639 12.62736 21.278924 16.14847 3.61352 69.298266 16.83437
## X205 X206 X207 X208 X209 X210
## ACR_11231843 11.7386256 20.06877 30.63068 48.34034 23.095155 34.76825
## ADAO_11159808 1.8427945 77.84928 19.00848 17.77677 60.824843 21.68477
## AGG_11236448 12.2833741 42.79839 37.38761 56.59146 61.740393 17.08210
## AHL_11239959 13.1560874 118.70570 52.25965 10.92810 91.639242 150.01977
## AJGD_11119689 140.5103623 380.77492 36.76533 73.45558 847.077743 192.62255
## AMP_11228639 0.9535721 32.15501 15.35024 88.76655 6.892712 63.45883
## X211 X212 X213 X214 X215 X216
## ACR_11231843 3.765127 21.344978 4.382055 3.2698891 3.518082 27.367185
## ADAO_11159808 19.360370 23.004067 47.709387 34.6111094 72.841930 18.029128
## AGG_11236448 10.535383 5.502814 115.456440 0.5054104 21.200621 72.049143
## AHL_11239959 22.209518 72.000782 59.353560 151.5447884 44.098118 60.903592
## AJGD_11119689 56.414859 95.926016 185.776814 134.6671586 333.583200 7.460378
## AMP_11228639 77.712952 12.077628 2.629013 16.8087723 11.356860 6.894547
## X217 X218 X219 X220 X221 X222
## ACR_11231843 13.038922 32.785810 5.580766 24.089625 11.3470921 12.209197
## ADAO_11159808 14.561528 32.577316 13.149922 19.286577 25.3912123 65.508151
## AGG_11236448 73.363762 99.824142 56.525109 17.274241 6.7123029 10.930148
## AHL_11239959 6.395764 60.947743 65.357533 13.389794 18.3347190 9.866878
## AJGD_11119689 19.070149 15.364078 68.087119 59.023463 29.7326854 57.498607
## AMP_11228639 5.599156 7.966483 88.547157 9.948009 0.2207513 107.832025
## X223 X224 X225 X226 X227 X228
## ACR_11231843 4.124942 6.827982 5.361808 12.814461 25.56484 38.318821
## ADAO_11159808 27.275495 1.664038 6.619855 26.082063 85.50181 4.853115
## AGG_11236448 95.120345 24.029547 37.855375 23.374974 44.25705 26.732488
## AHL_11239959 29.876886 6.153856 32.738844 70.750631 62.59665 6.557981
## AJGD_11119689 470.777734 143.579219 133.435738 648.487750 246.67728 391.322987
## AMP_11228639 22.569353 20.324252 40.527527 8.577763 12.56300 69.127249
## X229 X230 X231 X232 X233 X234
## ACR_11231843 16.532789 32.45485 10.666852 8.063589 12.089071 0.2940308
## ADAO_11159808 28.342357 17.69800 9.368646 8.319161 9.376816 36.5407158
## AGG_11236448 8.557814 132.34965 53.546957 150.030046 6.847152 67.1188863
## AHL_11239959 105.841453 34.36436 11.436903 25.907073 15.661661 91.9498010
## AJGD_11119689 74.135001 85.20711 30.730083 449.690930 150.600359 110.6486653
## AMP_11228639 3.021287 28.40044 33.414244 2.055479 63.697277 5.7611365
## X235 X236 X237 X238 X239 X240
## ACR_11231843 7.829537 3.590331 1.761903 28.144237 52.684300 22.4052473
## ADAO_11159808 9.074494 1.693016 47.775288 63.874484 95.862826 0.1187122
## AGG_11236448 14.751612 62.377294 22.337921 95.888412 9.063011 5.3631811
## AHL_11239959 38.359346 32.298068 25.367348 132.067756 19.903425 288.6416172
## AJGD_11119689 727.449872 458.129634 2.086416 4.638376 242.900491 34.7156950
## AMP_11228639 4.687741 24.896320 27.021008 12.723358 19.118679 7.5705866
## X241 X242 X243 X244 X245 X246
## ACR_11231843 348.3863 22.29866 409.3245 72.24925 321.9848 424.0674
## ADAO_11159808 2044.5415 1265.47449 1351.9694 1210.22532 264.4413 140.8006
## AGG_11236448 11268.6872 2030.76692 151.7232 1046.26609 1854.7914 991.9672
## AHL_11239959 2249.7759 2031.91033 811.1544 3220.16092 1938.9812 1994.9735
## AJGD_11119689 7094.5509 9873.09102 1052.2402 6077.83782 1206.4612 1990.7986
## AMP_11228639 7892.0056 2259.48911 3012.6365 508.49958 33.9347 288.1758
## X247 X248 X249 X250 X251 X252
## ACR_11231843 910.26473 131.74415 239.92825 252.02903 11.21741 264.25020
## ADAO_11159808 831.15521 467.38213 1167.68952 826.15021 644.38731 270.67798
## AGG_11236448 771.80575 950.16815 3312.61080 1473.54570 2661.50832 222.57669
## AHL_11239959 516.10161 2656.34800 2446.43229 449.74646 324.75012 1989.55872
## AJGD_11119689 13.33513 1318.46805 744.23940 1610.32601 233.77550 2882.88625
## AMP_11228639 114.02795 11.89135 31.15406 16.77033 86.46627 41.75092
## X253 X254 X255 X256 X257 X258
## ACR_11231843 293.95507 262.6576 401.07634 117.71846 55.742958 358.00352
## ADAO_11159808 33.56197 479.9267 541.05419 505.06088 687.619889 1011.34009
## AGG_11236448 1080.22766 263.2256 192.20104 1205.69580 92.992757 130.49854
## AHL_11239959 938.55828 237.5895 2160.15214 111.39618 81.379991 59.10440
## AJGD_11119689 764.40399 1459.6717 283.15964 497.35252 1873.122731 1893.23851
## AMP_11228639 114.75954 329.9692 28.11677 52.24031 7.547195 76.49546
## X259 X260 X261 X262 X263 X264
## ACR_11231843 182.10648 91.87253 22.88719 154.8910 65.780868 1.392005
## ADAO_11159808 30.92282 35.95631 96.13376 715.9380 235.995670 76.638298
## AGG_11236448 289.75392 16.92866 1037.73923 154.4910 37.582031 299.865090
## AHL_11239959 878.51500 678.09376 199.54941 264.0684 663.414926 1222.355550
## AJGD_11119689 713.25739 103.53796 3136.60321 1180.6349 472.554167 2468.332790
## AMP_11228639 43.59810 115.46191 30.76334 291.0160 5.110457 29.276310
## X265 X266 X267 X268 X269 X270
## ACR_11231843 139.23794 29.93685 300.09123 34.73585 58.27851 121.99195
## ADAO_11159808 256.39953 242.84672 403.21566 354.81157 117.40582 365.15476
## AGG_11236448 35.42327 912.72521 972.98997 243.81064 855.80175 85.08751
## AHL_11239959 83.65540 246.20807 61.77270 200.20723 66.16912 65.75803
## AJGD_11119689 549.96549 240.86774 71.69257 281.69095 660.69968 600.30732
## AMP_11228639 21.23936 118.88873 71.04967 41.36850 19.34578 199.24587
## X271 X272 X273 X274 X275 X276
## ACR_11231843 55.03412 45.01887 17.322892 53.609133 25.8584217 10.547630
## ADAO_11159808 183.38619 33.25127 297.206476 10.987478 0.5893427 367.872991
## AGG_11236448 47.04513 1065.92072 145.952794 475.608572 353.1913409 385.175211
## AHL_11239959 100.63393 226.66159 7.226275 242.673177 234.0012916 620.656751
## AJGD_11119689 732.01483 237.03530 324.118121 656.379251 464.4854942 54.184125
## AMP_11228639 76.55375 100.20715 368.261970 9.144192 130.1483208 5.504135
## X277 X278 X279 X280 X281 X282
## ACR_11231843 66.3661560 48.73835 7.406187 24.98301 141.908019 103.44403
## ADAO_11159808 126.3487258 214.57486 334.953092 77.57094 1.206459 55.61927
## AGG_11236448 814.8109073 108.27561 223.124670 1595.16871 176.687613 217.54232
## AHL_11239959 0.6775345 254.95084 304.048318 126.36189 161.317064 127.92756
## AJGD_11119689 183.8970065 926.80438 326.484165 366.73191 19.894166 334.65882
## AMP_11228639 17.9157013 180.80658 133.113999 52.81269 33.336818 16.36910
## X283 X284 X285 X286 X287 X288
## ACR_11231843 107.09436 30.18085 29.956095 43.623334 5.294672 134.86186
## ADAO_11159808 110.84276 101.29729 66.900785 8.721455 15.386726 33.79442
## AGG_11236448 849.35169 228.76225 4.944967 596.828911 10.508435 338.28683
## AHL_11239959 374.98924 95.78602 70.158977 76.632370 79.045753 13.85959
## AJGD_11119689 1056.17735 899.47948 123.079813 97.678292 182.962674 152.11053
## AMP_11228639 75.59077 42.01278 50.651694 49.651401 52.198570 10.27032
## X289 X290 X291 X292 X293 X294
## ACR_11231843 20.576737 11.091409 6.799941 18.56097 5.541049 3.523825
## ADAO_11159808 264.224969 213.497924 66.440252 63.61097 3.736728 1.310419
## AGG_11236448 110.025742 3.770932 111.458221 71.38763 180.316146 62.557459
## AHL_11239959 188.979711 6.748206 4.650608 12.32290 47.488803 89.957310
## AJGD_11119689 7.949797 347.317863 201.999642 458.85538 408.574619 206.410862
## AMP_11228639 2.359794 31.600895 11.512225 37.65142 23.078457 17.967709
## X295 X296 X297 X298 X299 X300
## ACR_11231843 15.428820 28.423519 62.58629 55.52865 38.627533 60.41682
## ADAO_11159808 25.504181 12.329279 10.31265 202.36457 63.931030 37.66273
## AGG_11236448 217.735580 344.587158 172.43477 207.71999 2.512771 20.91686
## AHL_11239959 110.727335 8.553674 183.86477 60.16570 55.081875 77.44528
## AJGD_11119689 39.029241 443.559422 123.61696 531.79592 558.466318 352.54997
## AMP_11228639 1.869597 26.363299 33.22283 53.74638 14.006948 12.35452
## X301 X302 X303 X304 X305 X306
## ACR_11231843 43.57834 162.5350377 4.260936 9.763429 2.884342 4.190182
## ADAO_11159808 226.70469 0.4796529 48.541290 100.452810 12.011168 18.078309
## AGG_11236448 184.92266 30.1281999 29.557721 82.162732 170.161284 54.051784
## AHL_11239959 53.79810 63.0062963 13.620459 43.802454 68.817901 187.043727
## AJGD_11119689 371.78956 46.4856410 54.587319 123.442734 874.985366 272.193179
## AMP_11228639 61.64142 7.9180100 5.658281 133.367049 94.294497 20.336480
## X307 X308 X309 X310 X311 X312
## ACR_11231843 24.18499 4.473226 2.649457 27.801453 4.2577475 13.11485
## ADAO_11159808 110.77871 24.824655 40.491695 26.450267 28.8174499 117.66970
## AGG_11236448 100.44518 47.006286 139.818855 127.433481 0.3425516 68.59093
## AHL_11239959 144.61331 274.585219 213.665734 7.945876 17.7810437 81.82055
## AJGD_11119689 1107.80888 243.429501 123.239037 276.139301 808.6988609 740.86028
## AMP_11228639 97.12129 66.474995 46.722698 72.819738 59.8111572 45.83738
## X313 X314 X315 X316 X317 X318
## ACR_11231843 80.340543 30.9414819 23.32626 71.70075 47.57655 90.945173
## ADAO_11159808 39.729756 14.7744220 84.15867 10.08191 27.55966 9.833679
## AGG_11236448 22.449675 102.0842652 275.07424 78.53754 37.65235 48.110982
## AHL_11239959 186.459352 5.6321980 100.18736 42.57903 1.94692 43.275164
## AJGD_11119689 89.824216 297.7107049 204.90412 351.52788 152.03528 178.623753
## AMP_11228639 3.731377 0.3142418 95.66031 26.04847 13.48961 65.946607
## X319 X320 X321 X322 X323 X324
## ACR_11231843 56.205045 14.07852 59.996110 5.234073 14.58238 23.804173
## ADAO_11159808 69.787965 78.58678 33.916417 4.374651 45.80617 11.389666
## AGG_11236448 220.862364 176.82135 24.815956 25.165631 139.19039 2.202261
## AHL_11239959 54.528927 26.54190 37.707648 277.502678 40.18441 69.493727
## AJGD_11119689 3.730390 68.51892 330.765304 499.236006 485.44468 338.021945
## AMP_11228639 8.157112 10.64790 8.876751 24.767964 19.87097 104.040998
## X325 X326 X327 X328 X329 X330
## ACR_11231843 67.37103 19.76578 10.108986 44.80942 0.4314662 87.882562
## ADAO_11159808 137.47895 135.60058 64.826560 32.44564 20.8145411 6.439392
## AGG_11236448 55.20123 170.47665 20.455483 178.58071 159.2572140 55.610500
## AHL_11239959 208.67442 141.40959 152.585528 19.73715 4.5394263 79.177857
## AJGD_11119689 62.29809 339.71690 2.727953 481.45007 97.3236436 375.512934
## AMP_11228639 112.38317 25.45809 43.797483 17.98346 63.7051146 138.293797
## X331 X332 X333 X334 X335 X336
## ACR_11231843 16.18456 11.76147 22.427247 43.67267 48.39041 33.036283
## ADAO_11159808 47.18262 85.92498 13.648709 96.17456 32.88860 8.504681
## AGG_11236448 78.39920 26.68607 2.392703 76.18619 245.14011 58.998472
## AHL_11239959 108.99752 57.88870 174.788483 145.52832 37.69597 125.125240
## AJGD_11119689 283.42605 398.29423 358.027899 656.51214 117.22896 7.411211
## AMP_11228639 88.24497 85.74665 19.915753 37.40505 18.55393 27.084072
## X337 X338 X339 X340 X341 X342
## ACR_11231843 21.888520 18.316180 12.71874 83.05418 3.663976 110.40502
## ADAO_11159808 9.335965 6.623210 25.48751 96.25737 69.779461 8.80107
## AGG_11236448 142.155138 8.761375 79.86648 98.99652 328.377704 101.01476
## AHL_11239959 31.860271 228.695673 45.84267 41.28917 3.617375 107.36383
## AJGD_11119689 342.796759 51.722356 219.81387 37.52951 168.992870 488.87355
## AMP_11228639 130.119225 42.936500 27.04814 98.96809 18.160248 23.60377
## X343 X344 X345 X346 X347 X348
## ACR_11231843 16.295266 32.72415 17.13538 9.19236758 48.088190 83.458860
## ADAO_11159808 2.260767 10.18050 23.39041 36.71574871 105.205348 18.074168
## AGG_11236448 29.243827 89.11253 13.59382 68.54142938 87.994157 125.289677
## AHL_11239959 2.821220 43.86895 38.60649 0.07107527 8.740787 97.574032
## AJGD_11119689 289.537274 91.99522 419.42506 52.01094497 17.069693 488.817218
## AMP_11228639 5.902560 34.66156 41.70199 20.35818452 7.135223 5.080574
## X349 X350 X351 X352 X353 X354
## ACR_11231843 3.954442 13.32374 35.683155 19.314187 32.106229 50.732030
## ADAO_11159808 51.568126 154.49904 1.201206 1.037543 20.856590 5.612337
## AGG_11236448 150.820895 72.53790 66.829812 14.957264 5.539851 104.319852
## AHL_11239959 25.338729 35.78403 5.447354 85.197355 252.798229 314.904281
## AJGD_11119689 186.138506 293.40227 96.733057 23.704723 648.571418 5.075168
## AMP_11228639 40.644134 27.92608 9.675836 2.386868 116.830721 44.099029
## X355 X356 X357 X358 X359 X360
## ACR_11231843 17.892616 41.28745 7.190733 5.8304104 41.478701 22.913039
## ADAO_11159808 25.139582 15.08121 6.315504 0.3793686 67.800729 1.642036
## AGG_11236448 40.076611 24.12978 14.640944 29.1616442 100.143411 31.107951
## AHL_11239959 104.820918 34.38167 10.476464 52.2497851 135.810815 117.801275
## AJGD_11119689 587.259468 473.45879 567.828200 154.4196451 46.270791 463.647626
## AMP_11228639 6.883874 29.07419 125.420924 0.9831770 7.714643 52.370693
## X361 X362 X363 X364 X365 X366
## ACR_11231843 95.685037 55.164721 2.253739 57.609520 13.632755 1.457037
## ADAO_11159808 8.385626 19.518276 46.366626 62.786694 148.567132 51.578888
## AGG_11236448 29.621578 0.366469 40.439735 35.122426 8.885634 14.310926
## AHL_11239959 19.089222 81.433967 9.801134 41.734388 61.152809 67.267496
## AJGD_11119689 412.657057 53.604567 33.056229 6.821248 103.978128 116.484169
## AMP_11228639 2.229305 12.658934 112.011633 18.779024 8.087187 18.272481
## X367 X368 X369 X370 X371 X372
## ACR_11231843 40.739244 17.96603 24.548548 13.463209 6.443771 52.93993
## ADAO_11159808 22.563617 15.07311 9.448053 29.363498 9.081545 23.66586
## AGG_11236448 27.790489 4.70232 176.780377 115.812240 37.916712 16.81850
## AHL_11239959 91.652274 56.31386 67.965190 48.584645 22.535853 60.84074
## AJGD_11119689 7.737438 279.41383 233.624164 7.219063 267.589323 132.96953
## AMP_11228639 22.472939 11.36108 3.557244 36.921134 39.827388 54.13716
## X373 X374 X375 X376 X377 X378
## ACR_11231843 49.676277 5.641364 46.11750 9.153074 10.49148 21.856224
## ADAO_11159808 34.462758 181.632461 80.21512 57.532356 43.14911 4.493098
## AGG_11236448 9.118927 10.440550 39.79872 6.111265 135.59287 65.210129
## AHL_11239959 334.385808 222.688753 39.28932 10.192787 197.00301 24.440151
## AJGD_11119689 19.737670 121.741480 211.80330 89.732031 292.11711 415.326869
## AMP_11228639 3.211792 54.266953 88.02787 4.271698 31.32815 98.605797
## X379 X380 X381 X382 X383 X384
## ACR_11231843 0.4032183 32.366806 4.537283 32.501481 15.25671 34.347901
## ADAO_11159808 17.8035188 29.661011 10.772769 2.536657 23.16254 53.783266
## AGG_11236448 171.5105109 3.143752 70.907509 227.299673 132.95188 38.082128
## AHL_11239959 98.4440230 23.826309 49.229495 62.303999 10.43516 47.539190
## AJGD_11119689 137.3527313 57.163021 48.727410 715.352654 277.44958 232.809748
## AMP_11228639 91.3998161 8.655655 28.503759 14.966507 23.16132 8.718949
## X385 X386 X387 X388 X389 X390
## ACR_11231843 38.736026 3.039466 15.829168 12.04144 11.25694 2.49266
## ADAO_11159808 8.566305 26.694952 2.788876 22.52548 39.91460 19.05122
## AGG_11236448 40.513373 51.430609 136.823679 73.10300 359.23883 43.09692
## AHL_11239959 60.008177 16.024650 49.176827 133.04377 50.62884 318.84514
## AJGD_11119689 79.012414 350.071917 222.105037 181.73419 16.83085 303.02044
## AMP_11228639 20.990693 25.795104 132.746096 25.33968 15.37677 22.50900
## X391 X392 X393 X394 X395 X396
## ACR_11231843 47.30072 44.742466 12.29814 75.785426 15.96699 27.1083640
## ADAO_11159808 49.46168 7.997823 60.55102 41.161244 30.13339 0.2616995
## AGG_11236448 17.81332 61.163396 22.00824 16.030331 90.02268 85.2159661
## AHL_11239959 36.33479 37.717913 108.72114 31.216102 99.32177 84.2191571
## AJGD_11119689 316.98400 61.223728 40.63928 4.103047 535.83938 436.6038876
## AMP_11228639 28.39629 42.714160 10.09216 90.568972 85.35638 11.9370369
## X397 X398 X399 X400 X401 X402
## ACR_11231843 45.95646 0.1210798 40.776904 22.383541 2.766273 43.239353
## ADAO_11159808 22.03423 17.9781397 5.267311 1.781083 30.538443 42.411914
## AGG_11236448 44.33659 41.2570209 40.349971 11.659568 52.620328 54.495582
## AHL_11239959 44.76072 33.3372597 26.706942 30.653957 2.112994 42.865180
## AJGD_11119689 336.68747 544.1652639 14.026822 347.342580 162.041963 538.640098
## AMP_11228639 27.89045 41.0011328 44.213850 12.061124 69.523363 8.805054
## X403 X404 X405 X406 X407 X408
## ACR_11231843 13.741907 84.529370 117.250227 13.375122 42.56509 34.28050
## ADAO_11159808 9.076199 40.126273 29.157689 97.221461 21.91342 34.18008
## AGG_11236448 44.830644 61.830382 100.366014 8.326845 86.93767 59.60235
## AHL_11239959 91.793861 66.893313 23.298347 4.215710 40.84039 27.42377
## AJGD_11119689 457.466282 22.333463 4.725218 256.760241 283.43004 119.09126
## AMP_11228639 20.486618 4.577062 44.024013 23.415008 58.19166 51.67915
## X409 X410 X411 X412 X413 X414
## ACR_11231843 3.639098 21.704368 0.192723 64.316913 40.330819 23.68798
## ADAO_11159808 8.405364 5.502808 11.255454 21.466028 15.964233 28.37875
## AGG_11236448 15.444968 7.849136 27.965611 5.293785 76.802196 12.51821
## AHL_11239959 119.537582 16.328687 48.784038 15.199396 3.107826 24.73594
## AJGD_11119689 709.467437 22.684400 184.051175 313.218699 207.957911 548.57790
## AMP_11228639 11.790435 63.440258 70.809161 8.405446 24.443895 129.71361
## X415 X416 X417 X418 X419 X420
## ACR_11231843 40.82167 23.7900347 45.11949 43.50358 22.723639 9.313692
## ADAO_11159808 50.24005 0.8022005 38.26750 8.10573 1.043840 9.889475
## AGG_11236448 48.48196 9.8416125 63.80561 30.89745 2.358648 23.467832
## AHL_11239959 30.18812 2.8722651 10.76408 22.97082 51.281025 2.852812
## AJGD_11119689 343.39759 242.4180358 310.77116 115.87615 140.270056 351.047693
## AMP_11228639 103.70657 27.9670488 14.14361 20.41486 48.662240 17.969358
## X421 X422 X423 X424 X425 X426
## ACR_11231843 1.261902 0.2159353 50.72558 11.13419 16.524788 19.21838
## ADAO_11159808 82.896544 73.8020506 35.26160 39.24148 25.660765 18.83864
## AGG_11236448 32.083150 92.0116763 93.78611 8.51574 16.965149 160.07034
## AHL_11239959 49.575698 47.2374400 122.62801 19.53437 10.900321 80.35366
## AJGD_11119689 56.982064 125.1029347 116.17819 280.47515 500.866847 69.95154
## AMP_11228639 17.465273 11.1884816 36.33052 29.72711 9.163052 22.25266
## X427 X428 X429 X430 X431 X432
## ACR_11231843 46.67731 16.65716 42.450086 15.496901 17.29504 11.312551
## ADAO_11159808 11.04706 76.41117 78.737583 56.400966 12.47871 7.081923
## AGG_11236448 16.66365 77.90671 28.822138 6.382938 85.66442 37.560970
## AHL_11239959 104.13201 28.04031 7.124077 99.566978 26.11656 12.453738
## AJGD_11119689 177.28188 92.95068 9.012681 185.005304 390.75076 30.257458
## AMP_11228639 22.56451 59.58724 21.441088 8.855096 108.24073 24.596916
## X433 X434 X435 X436 X437 X438
## ACR_11231843 6.423861 5.103376 76.85062 33.791074 47.24277 11.550629
## ADAO_11159808 16.240655 32.553304 13.09777 1.082000 66.07276 9.416056
## AGG_11236448 169.461545 1.593565 11.13518 62.070057 108.68741 15.528712
## AHL_11239959 1.954401 27.508108 29.90202 191.247570 50.96500 40.047723
## AJGD_11119689 466.351893 128.532334 1033.09546 70.705233 40.93867 212.422835
## AMP_11228639 32.705333 28.530489 37.58171 7.525454 51.46679 82.671648
## X439 X440 X441 X442 X443 X444
## ACR_11231843 20.45810 2.044473 38.11751 24.17440 2.754627 14.83970
## ADAO_11159808 15.54622 17.713935 14.34175 14.94304 63.483565 34.69222
## AGG_11236448 16.25954 24.129652 71.18947 61.40976 1.233862 16.70792
## AHL_11239959 19.19769 19.630410 50.63597 45.89591 56.783553 41.78972
## AJGD_11119689 180.31412 68.118085 454.83126 158.04834 269.832741 247.36795
## AMP_11228639 12.62736 21.278924 16.14847 3.61352 69.298266 16.83437
## X445 X446 X447 X448 X449 X450
## ACR_11231843 11.7386256 20.06877 30.63068 48.34034 23.095155 34.76825
## ADAO_11159808 1.8427945 77.84928 19.00848 17.77677 60.824843 21.68477
## AGG_11236448 12.2833741 42.79839 37.38761 56.59146 61.740393 17.08210
## AHL_11239959 13.1560874 118.70570 52.25965 10.92810 91.639242 150.01977
## AJGD_11119689 140.5103623 380.77492 36.76533 73.45558 847.077743 192.62255
## AMP_11228639 0.9535721 32.15501 15.35024 88.76655 6.892712 63.45883
## X451 X452 X453 X454 X455 X456
## ACR_11231843 3.765127 21.344978 4.382055 3.2698891 3.518082 27.367185
## ADAO_11159808 19.360370 23.004067 47.709387 34.6111094 72.841930 18.029128
## AGG_11236448 10.535383 5.502814 115.456440 0.5054104 21.200621 72.049143
## AHL_11239959 22.209518 72.000782 59.353560 151.5447884 44.098118 60.903592
## AJGD_11119689 56.414859 95.926016 185.776814 134.6671586 333.583200 7.460378
## AMP_11228639 77.712952 12.077628 2.629013 16.8087723 11.356860 6.894547
## X457 X458 X459 X460 X461 X462
## ACR_11231843 13.038922 32.785810 5.580766 24.089625 11.3470921 12.209197
## ADAO_11159808 14.561528 32.577316 13.149922 19.286577 25.3912123 65.508151
## AGG_11236448 73.363762 99.824142 56.525109 17.274241 6.7123029 10.930148
## AHL_11239959 6.395764 60.947743 65.357533 13.389794 18.3347190 9.866878
## AJGD_11119689 19.070149 15.364078 68.087119 59.023463 29.7326854 57.498607
## AMP_11228639 5.599156 7.966483 88.547157 9.948009 0.2207513 107.832025
## X463 X464 X465 X466 X467 X468
## ACR_11231843 4.124942 6.827982 5.361808 12.814461 25.56484 38.318821
## ADAO_11159808 27.275495 1.664038 6.619855 26.082063 85.50181 4.853115
## AGG_11236448 95.120345 24.029547 37.855375 23.374974 44.25705 26.732488
## AHL_11239959 29.876886 6.153856 32.738844 70.750631 62.59665 6.557981
## AJGD_11119689 470.777734 143.579219 133.435738 648.487750 246.67728 391.322987
## AMP_11228639 22.569353 20.324252 40.527527 8.577763 12.56300 69.127249
## X469 X470 X471 X472 X473 X474
## ACR_11231843 16.532789 32.45485 10.666852 8.063589 12.089071 0.2940308
## ADAO_11159808 28.342357 17.69800 9.368646 8.319161 9.376816 36.5407158
## AGG_11236448 8.557814 132.34965 53.546957 150.030046 6.847152 67.1188863
## AHL_11239959 105.841453 34.36436 11.436903 25.907073 15.661661 91.9498010
## AJGD_11119689 74.135001 85.20711 30.730083 449.690930 150.600359 110.6486653
## AMP_11228639 3.021287 28.40044 33.414244 2.055479 63.697277 5.7611365
## X475 X476 X477 X478 X479 X480
## ACR_11231843 7.829537 3.590331 1.761903 28.144237 52.684300 22.4052473
## ADAO_11159808 9.074494 1.693016 47.775288 63.874484 95.862826 0.1187122
## AGG_11236448 14.751612 62.377294 22.337921 95.888412 9.063011 5.3631811
## AHL_11239959 38.359346 32.298068 25.367348 132.067756 19.903425 288.6416172
## AJGD_11119689 727.449872 458.129634 2.086416 4.638376 242.900491 34.7156950
## AMP_11228639 4.687741 24.896320 27.021008 12.723358 19.118679 7.5705866
## DDclust_PER_FC
## ACR_11231843 1
## ADAO_11159808 1
## AGG_11236448 1
## AHL_11239959 1
## AJGD_11119689 1
## AMP_11228639 1## Mean by groups
rp_tbl_PER <- aggregate(plotting_PER, by = list(plotting_PER$DDclust_PER_FC), mean)
row.names(rp_tbl_PER) <- paste0("Group",rp_tbl_PER$DDclust_PER_FC)
rp_tbl_PER <- rp_tbl_PER %>%
select(starts_with('X'))
rp_tbl_PER <- data.frame(t(rp_tbl_PER))
head(rp_tbl_PER)## Group1 Group2
## X1 4779.459 27881.160
## X2 4698.322 15936.959
## X3 2683.336 3897.343
## X4 2617.546 2794.041
## X5 1807.812 1869.057
## X6 1216.101 2047.405# Create plotting data-frame
PER_values_by_group <- data.frame("value_PER" = c(rp_tbl_PER$Group1,rp_tbl_PER$Group2),
"cluster" = c(rep("Group1", times = length(rp_tbl_PER$Group1)),
rep("Group2", times = length(rp_tbl_PER$Group2))),
"index" = c(c(1:length(rp_tbl_PER$Group1)),c(1:length(rp_tbl_PER$Group2))))
p <- ggplot(PER_values_by_group, aes(x = index, y = value_PER, group = cluster)) +
geom_line(aes(color=cluster)) +
scale_color_brewer(palette="Paired") + theme_minimal()
p
Adjusted Rand Index
rand_index_table_FC = data.frame(matrix(ncol = 3 , nrow = 3))
colnames(rand_index_table_FC) <- c("DDclust_ACF_FC", "DDclust_EUCL_FC", "DDclust_PER_FC")
rownames(rand_index_table_FC) <- c("DDclust_ACF_FC", "DDclust_EUCL_FC", "DDclust_PER_FC")
cluster_study_FC <- list(DDclust_ACF_FC, DDclust_EUCL_FC, DDclust_PER_FC)for (i in c(1:length(cluster_study_FC))) {
for (j in c(1:length(cluster_study_FC))){
rand_index_table_FC[i,j] <- adjustedRandIndex(cluster_study_FC[[i]], cluster_study_FC[[j]])
}}head(rand_index_table_FC)## DDclust_ACF_FC DDclust_EUCL_FC DDclust_PER_FC
## DDclust_ACF_FC 1.000000000 -0.005889219 0.269623205
## DDclust_EUCL_FC -0.005889219 1.000000000 -0.006108376
## DDclust_PER_FC 0.269623205 -0.006108376 1.000000000write.csv(cluster_study_FC, "../../data/clusters/cluster_study_FC.csv")