Clustering_Time_Series-TSClust-Study-03HRCuant
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: cuantiles Heart Rate
cuantiles_TS_HR_P2 = data.frame(read_xlsx("../../data/clean-data/BoxBasedImputation/cuantiles_TS_HR_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_namedf_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 ]
cuantiles_TS_HR_P2 <- cuantiles_TS_HR_P2[,valid_patients_P2]Restando Media
#cuantiles_TS_HR_P2 = data.frame(scale(cuantiles_TS_HR_P2))dimension_col <- dim(cuantiles_TS_HR_P2)[2]
dimension_row <- 480 #lag.max -1
# Heart Rate
cuantiles_TS_HR_P2_ACF <- data.frame(matrix(nrow = dimension_row, ncol = dimension_col))
colnames(cuantiles_TS_HR_P2_ACF) <- names(cuantiles_TS_HR_P2)[1:dimension_col]for (i in names(cuantiles_TS_HR_P2_ACF)) {
acf_result_FC <- forecast::Acf(cuantiles_TS_HR_P2[[i]], lag.max = (dimension_row - 1), plot = FALSE, drop.lag.0 = FALSE)
cuantiles_TS_HR_P2_ACF[, i] <- acf_result_FC$acf
}Create a dataframe with peridiogram
# Generar un dataset con varias series temporales
df <- cuantiles_TS_HR_P2
# Crear una matriz para almacenar los periodogramas
pg_mat <- data.frame(matrix(nrow = nrow(df), ncol = ncol(df)))
colnames(pg_mat) = colnames(cuantiles_TS_HR_P2)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
library(stats)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
for (i in colnames(cuantiles_TS_HR_P2)) {
pg_mat[,i] <- stats::spec.pgram(cuantiles_TS_HR_P2[,i], plot = FALSE)$spec
}TsClust Comprobation
datos <- cuantiles_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(cuantiles_TS_HR_P2_ACF[c(1:51),])
distance <- dist(t(cuantiles_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)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.7926496 0.5811764 0.9143518 0.9552261NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_ACF
res<-NbClust(datos_ACF, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.4721 0.3380 0.3412 0.3014res$Best.nc## Number_clusters Value_Index
## 2.0000 0.4721#res$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_cuantiles <- cutree( hclust(DD_ACF, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_ACF_cuantiles))
fviz_silhouette(silhouette(DDclust_ACF_cuantiles, DD_ACF))## cluster size ave.sil.width
## 1 1 37 0.45
## 2 2 21 0.50
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_cuantiles[DDclust_ACF_cuantiles == 2]),names(DDclust_ACF_cuantiles[DDclust_ACF_cuantiles == 1]))
fviz_dend(hcintper_ACF, k = 2,
k_colors = c("blue", "green3"),
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 | 5 | 1 |
| NO DETERIORO | 32 | 20 |
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 | 0.1351351 | 0.047619 |
| NO DETERIORO | 0.8648649 | 0.952381 |
Random Forest: Discriminant TSCLust ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_cuantiles)
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$CLUSTER)
newMWMOTE_ACF <- data_frame_merge_ACF
table(newMWMOTE_ACF$CLUSTER)##
## 1 2
## 37 21set.seed(123)
pos_1 = get_column_position(newMWMOTE_ACF, "SAPI_0_8h")
pos_2 = get_column_position(newMWMOTE_ACF, "PAUSAS_APNEA")
col_names_factor <- names(newMWMOTE_ACF[pos_1:pos_2])
newMWMOTE_ACF[col_names_factor] <- lapply(newMWMOTE_ACF[col_names_factor] , factor)
RF_ACF <- randomForest(CLUSTER ~ ., data = newMWMOTE_ACF)
print(RF_ACF)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newMWMOTE_ACF)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 32.76%
## Confusion matrix:
## 1 2 class.error
## 1 30 7 0.1891892
## 2 12 9 0.5714286Importance
kable(RF_ACF$importance[order(RF_ACF$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_WOOD_DOWNES_INGRESO | 3.1336476 |
| PESO | 3.1080877 |
| EDAD | 2.5533093 |
| SCORE_CRUCES_INGRESO | 2.3892629 |
| FR_0_8h | 1.7130177 |
| FLUJO2_0_8H | 1.6930335 |
| DIAS_O2_TOTAL | 1.4336942 |
| EG | 1.3948105 |
| DIAS_GN | 1.2519954 |
| SAPI_0_8h | 1.2253858 |
| ALERGIAS | 0.6168551 |
| LM | 0.5958568 |
| SUERO | 0.5354617 |
| RADIOGRAFIA | 0.5114914 |
| ETIOLOGIA | 0.4907219 |
| ALIMENTACION | 0.4453140 |
| SEXO | 0.4107877 |
| ANALITICA | 0.3519205 |
| TABACO | 0.3216323 |
| ENFERMEDAD_BASE | 0.3182639 |
| GN_INGRESO | 0.2200032 |
| SNG | 0.2189779 |
| PREMATURIDAD | 0.1942708 |
| DIAS_OAF | 0.1886144 |
| DERMATITIS | 0.1428792 |
| PALIVIZUMAB | 0.1408059 |
| UCIP | 0.0917510 |
| OAF_TRAS_INGRESO | 0.0853065 |
| OAF | 0.0790360 |
| PAUSAS_APNEA | 0.0565229 |
| DETERIORO | 0.0443805 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of first 50 ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_cuantiles)
data_frame2_ACF = data.frame(t(cuantiles_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: 3.45%
## Confusion matrix:
## 1 2 class.error
## 1 36 1 0.02702703
## 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_cuantiles)
plotting_ACF <- cbind(plot_data_ACF, cluster_data_ACF)
head(plotting_ACF)## X1 X2 X3 X4 X5 X6 X7
## ACR_11231843 1 0.2901794 0.1445375 0.1826255 0.1156195 0.1595519 0.1686698
## ADAO_11159808 1 0.6822253 0.6154150 0.5577162 0.4866438 0.4073035 0.3684635
## AGG_11236448 1 0.7487812 0.6219794 0.5160688 0.4487149 0.3684359 0.3383680
## AHL_11239959 1 0.7216901 0.6564194 0.6221447 0.5964008 0.5225439 0.4811643
## AJGD_11119689 1 0.4386855 0.4019033 0.3268365 0.2817262 0.2650908 0.2667877
## AMP_11228639 1 0.6734335 0.6218323 0.6125705 0.5636459 0.5638071 0.5570929
## X8 X9 X10 X11 X12 X13
## ACR_11231843 0.1356246 0.1593997 0.1304298 0.09766236 0.07545705 0.09004254
## ADAO_11159808 0.3012719 0.2706732 0.2346336 0.22413207 0.22797769 0.20471643
## AGG_11236448 0.2973079 0.3062705 0.3328523 0.37472707 0.32405037 0.33429534
## AHL_11239959 0.4616813 0.4034425 0.3739435 0.36798988 0.36492713 0.35433190
## AJGD_11119689 0.2261258 0.2193981 0.2139133 0.18065194 0.11638789 0.16416913
## AMP_11228639 0.5537405 0.5428936 0.5372326 0.53136078 0.52779325 0.53920328
## X14 X15 X16 X17 X18
## ACR_11231843 0.02677344 0.01490945 0.01974594 0.005618861 0.02212149
## ADAO_11159808 0.19577193 0.19279894 0.19305812 0.179245516 0.16695388
## AGG_11236448 0.34838297 0.31867504 0.26084171 0.210527705 0.18253497
## AHL_11239959 0.31897291 0.31616950 0.29874085 0.281647474 0.24081307
## AJGD_11119689 0.12788930 0.14870347 0.13484193 0.124073446 0.14696333
## AMP_11228639 0.54645529 0.57013229 0.55182133 0.526359661 0.52717011
## X19 X20 X21 X22 X23
## ACR_11231843 0.03849127 0.03282626 0.003204873 -0.004249007 0.02073291
## ADAO_11159808 0.17580519 0.14710489 0.129015944 0.170782789 0.17189297
## AGG_11236448 0.14966877 0.12161145 0.103789401 0.094552738 0.10727757
## AHL_11239959 0.23067472 0.22553114 0.237133230 0.240184218 0.24375071
## AJGD_11119689 0.11087734 0.13298761 0.136165841 0.195137849 0.14619485
## AMP_11228639 0.48020686 0.48482374 0.509726715 0.507047223 0.50808288
## X24 X25 X26 X27 X28
## ACR_11231843 0.02466074 0.02713623 0.02043259 -0.01376917 -0.01695976
## ADAO_11159808 0.15012713 0.13506276 0.13217362 0.14923846 0.11850756
## AGG_11236448 0.14700980 0.13544661 0.10712478 0.09278461 0.09252869
## AHL_11239959 0.22432351 0.21361062 0.22892399 0.19922700 0.17224130
## AJGD_11119689 0.15342557 0.13240827 0.17937112 0.14096474 0.16992387
## AMP_11228639 0.48679282 0.47317239 0.50401209 0.46815872 0.47064157
## X29 X30 X31 X32 X33
## ACR_11231843 -0.02843619 -0.03187011 -0.02893051 -0.005813193 0.02020365
## ADAO_11159808 0.13790996 0.10955812 0.11022192 0.104883947 0.10514811
## AGG_11236448 0.06886411 0.07736897 0.06724418 0.084017832 0.10480111
## AHL_11239959 0.17028158 0.18203776 0.13996073 0.115134809 0.13422126
## AJGD_11119689 0.14982150 0.11141859 0.12558391 0.092275395 0.09375120
## AMP_11228639 0.49272159 0.47053684 0.47026545 0.444397748 0.43388579
## X34 X35 X36 X37 X38
## ACR_11231843 0.004588561 0.001866749 -0.00523324 0.01269177 -0.004989279
## ADAO_11159808 0.079929832 0.081289713 0.10772381 0.11303981 0.107168473
## AGG_11236448 0.150856337 0.164490458 0.17492251 0.16206226 0.167132500
## AHL_11239959 0.136755465 0.168351378 0.17532094 0.17146793 0.152687949
## AJGD_11119689 0.113251976 0.126423103 0.09910879 0.09405352 0.141273644
## AMP_11228639 0.423813059 0.415792782 0.40051054 0.38992792 0.376832472
## X39 X40 X41 X42 X43
## ACR_11231843 0.02563682 0.05039609 0.05110837 0.02674054 -0.01933926
## ADAO_11159808 0.12992229 0.12115335 0.10100243 0.05577251 0.02655678
## AGG_11236448 0.14424185 0.13613581 0.12698526 0.14086689 0.14920918
## AHL_11239959 0.14823746 0.16937696 0.15642745 0.15168477 0.16870278
## AJGD_11119689 0.08395321 0.11352537 0.12545195 0.14934838 0.16166699
## AMP_11228639 0.36484784 0.37445809 0.33887233 0.34244924 0.36487856
## X44 X45 X46 X47 X48
## ACR_11231843 -0.006509836 0.03850558 -0.01982911 0.03179517 0.03820350
## ADAO_11159808 0.019706334 0.01700378 0.00151395 0.01989839 0.03861521
## AGG_11236448 0.154220525 0.17388005 0.16654921 0.15016039 0.15284631
## AHL_11239959 0.133464536 0.13227583 0.11398564 0.11309738 0.08914764
## AJGD_11119689 0.131890865 0.11416541 0.14351939 0.12466630 0.15183576
## AMP_11228639 0.361474591 0.34070437 0.31903501 0.32854206 0.29887161
## X49 X50 X51 DDclust_ACF_cuantiles
## ACR_11231843 -0.003788488 0.001135697 -0.0008443659 1
## ADAO_11159808 0.008661457 0.027097721 0.0707360476 1
## AGG_11236448 0.157301581 0.151940117 0.1402599507 1
## AHL_11239959 0.068708135 0.072864831 0.0817941981 1
## AJGD_11119689 0.080465356 0.107842017 0.0613762537 1
## AMP_11228639 0.271305894 0.276383614 0.2682469887 2## Mean by groups
rp_tbl_ACF <- aggregate(plotting_ACF, by = list(plotting_ACF$DDclust_ACF_cuantiles), mean)
row.names(rp_tbl_ACF) <- paste0("Group",rp_tbl_ACF$DDclust_ACF_cuantiles)
rp_tbl_ACF <- rp_tbl_ACF %>%
select(starts_with('X'))
rp_tbl_ACF <- data.frame(t(rp_tbl_ACF))
head(rp_tbl_ACF)## Group1 Group2
## X1 1.0000000 1.0000000
## X2 0.6762286 0.8512600
## X3 0.6186539 0.8110216
## X4 0.5652320 0.7819768
## X5 0.5184559 0.7538167
## X6 0.4779945 0.7317472# Create plotting data-frame
ACF_values_by_group <- data.frame("value_ACF" = c(rp_tbl_ACF$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
## 0.6074170 0.3927671 0.6968036 0.9489753NbClust 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$All.index## 2 3 4 5
## 0.3008 0.1657 0.1653 0.1769res$Best.nc## Number_clusters Value_Index
## 2.0000 0.3008#res$Best.partitionhcintper_EUCL <- hclust(DD_EUCL, "ward.D2")
fviz_dend(hcintper_EUCL, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)
DDclust_EUCL_cuantiles <- cutree( hclust(DD_EUCL, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_EUCL_cuantiles))
fviz_silhouette(silhouette(DDclust_EUCL_cuantiles, DD_EUCL))## cluster size ave.sil.width
## 1 1 39 0.37
## 2 2 19 0.15
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_cuantiles[DDclust_EUCL_cuantiles == 2]),names(DDclust_EUCL_cuantiles[DDclust_EUCL_cuantiles == 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 | 6 | 0 |
| 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 | 0.1538462 | 0 |
| NO DETERIORO | 0.8461538 | 1 |
Random Forest: Discriminant TSCLust EUCL
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_cuantiles)
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
## 39 19data_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 1 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)
newMWMOTE_EUCL <- oversample(data_frame_merge_EUCL, ratio = 0.85, method = "MWMOTE", classAttr = "CLUSTER")
newMWMOTE_EUCL <- data.frame(newMWMOTE_EUCL)
pos_1 <- get_column_position(newMWMOTE_EUCL, "SAPI_0_8h")
pos_2 <- get_column_position(newMWMOTE_EUCL, "PAUSAS_APNEA")
columns_to_round <- c(pos_1:pos_2)
newMWMOTE_EUCL[, columns_to_round] <- lapply(newMWMOTE_EUCL[, columns_to_round], function(x) round(x, 1))
table(newMWMOTE_EUCL$CLUSTER)##
## 1 2
## 39 34set.seed(123)
pos_1 = get_column_position(newMWMOTE_EUCL, "SAPI_0_8h")
pos_2 = get_column_position(newMWMOTE_EUCL, "PAUSAS_APNEA")
col_names_factor <- names(newMWMOTE_EUCL[pos_1:pos_2])
newMWMOTE_EUCL[col_names_factor] <- lapply(newMWMOTE_EUCL[col_names_factor] , factor)
RF_EUCL <- randomForest(CLUSTER ~ ., data = newMWMOTE_EUCL)
print(RF_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newMWMOTE_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 36.99%
## Confusion matrix:
## 1 2 class.error
## 1 26 13 0.3333333
## 2 14 20 0.4117647Importance
kable(RF_EUCL$importance[order(RF_EUCL$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_WOOD_DOWNES_INGRESO | 3.8597355 |
| SCORE_CRUCES_INGRESO | 3.3423195 |
| PESO | 3.2757830 |
| DIAS_O2_TOTAL | 2.8530298 |
| FR_0_8h | 2.6567973 |
| EDAD | 2.4953821 |
| SAPI_0_8h | 2.0102588 |
| DIAS_GN | 1.6183414 |
| EG | 1.6005812 |
| FLUJO2_0_8H | 1.4937520 |
| SEXO | 1.1383538 |
| TABACO | 1.1200215 |
| LM | 1.0384205 |
| RADIOGRAFIA | 1.0071133 |
| ANALITICA | 0.8374012 |
| ETIOLOGIA | 0.6741771 |
| SUERO | 0.5873746 |
| ENFERMEDAD_BASE | 0.5808818 |
| GN_INGRESO | 0.5312364 |
| ALERGIAS | 0.4417460 |
| ALIMENTACION | 0.4414810 |
| PREMATURIDAD | 0.4171263 |
| DIAS_OAF | 0.1833326 |
| SNG | 0.1727058 |
| DERMATITIS | 0.1725681 |
| PALIVIZUMAB | 0.1547595 |
| PAUSAS_APNEA | 0.1450905 |
| OAF_TRAS_INGRESO | 0.1229658 |
| OAF | 0.1192774 |
| DETERIORO | 0.0947380 |
| UCIP | 0.0081992 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the TS-data
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_cuantiles)
data_frame2_EUCL = data.frame(datos_EUCL)
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2_EUCL, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]set.seed(123)
data_frame_merge_EUCL$CLUSTER <- as.factor(data_frame_merge_EUCL$CLUSTER)
RF_0_EUCL <- randomForest(CLUSTER ~ ., data = data_frame_merge_EUCL)
print(RF_0_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 5.17%
## Confusion matrix:
## 1 2 class.error
## 1 39 0 0.0000000
## 2 3 16 0.1578947plot(RF_0_EUCL$importance, type = "h")
EUCL by clusters
plot_data_EUCL <- data.frame(t(datos))
cluster_data_EUCL <- data.frame(DDclust_EUCL_cuantiles)
plotting_EUCL <- cbind(plot_data_EUCL, cluster_data_EUCL)
head(plotting_EUCL)## X1 X2 X3 X4 X5 X6
## ACR_11231843 0.8619571 0.7949917 0.9122027 0.9945867 0.9995873 0.9945867
## ADAO_11159808 0.5612683 0.6354365 0.4848774 0.6354365 0.4848774 0.3601399
## AGG_11236448 0.5158019 0.5049792 0.4952942 0.5492029 0.4345703 0.4312846
## AHL_11239959 0.8255615 0.8255615 0.3898689 0.8863008 0.9388251 0.8987865
## AJGD_11119689 0.2944890 0.2304833 0.2059313 0.1170242 0.3613294 0.3320483
## AMP_11228639 0.9691826 0.7481586 0.8339437 0.4286186 0.5272855 0.6005238
## X7 X8 X9 X10 X11 X12
## ACR_11231843 0.9814195 0.9905952 0.9921454 0.9744921 0.9315044 0.9223077
## ADAO_11159808 0.4090392 0.4090392 0.3601399 0.3364692 0.3843594 0.2910958
## AGG_11236448 0.4793684 0.3642054 0.3391369 0.2466285 0.5766754 0.4962728
## AHL_11239959 0.9388251 0.7685352 0.9535354 0.9701419 0.9597441 0.9652613
## AJGD_11119689 0.2205346 0.0773939 0.1578963 0.1405510 0.1472977 0.2872519
## AMP_11228639 0.3119546 0.9404413 0.6925331 0.8939962 0.6925331 0.7737891
## X13 X14 X15 X16 X17 X18
## ACR_11231843 0.9315044 0.9122027 0.99059516 0.99059516 0.9655215 0.92230772
## ADAO_11159808 0.2488054 0.3601399 0.31342953 0.38435943 0.8218898 0.58637089
## AGG_11236448 0.2911670 0.6287538 0.78861097 0.92070450 0.9207045 0.76853521
## AHL_11239959 0.9889417 0.9936325 0.98681707 0.97014186 0.9388251 0.57667541
## AJGD_11119689 0.1702522 0.0958889 0.02506787 0.01844418 0.1750516 0.05765972
## AMP_11228639 0.6005238 0.4777816 0.62429782 0.62429782 0.7140068 0.85583874
## X19 X20 X21 X22 X23 X24
## ACR_11231843 0.9315044 0.91220267 0.901148228 0.9315044 0.8891082 0.93150439
## ADAO_11159808 0.5104184 0.38435943 0.459398309 0.2695349 0.4848774 0.33646919
## AGG_11236448 0.7023921 0.29116698 0.187303691 0.6539969 0.9744395 0.97820653
## AHL_11239959 0.3898689 0.31476118 0.807628499 0.3147612 0.4425521 0.36420541
## AJGD_11119689 0.3134295 0.03356055 0.009561289 0.8380867 0.1750516 0.02153844
## AMP_11228639 0.7737891 0.84127788 0.855838743 0.8412779 0.8412779 0.89399617
## X25 X26 X27 X28 X29 X30
## ACR_11231843 0.9655215 0.9122027 0.92230772 0.876052408 0.9011482 0.92230772
## ADAO_11159808 0.4593983 0.3843594 0.29109580 0.269534949 0.2910958 0.70484551
## AGG_11236448 0.9947464 0.9947464 0.99363255 0.986817068 0.9956835 0.94658044
## AHL_11239959 0.9597441 0.7474389 0.82556153 0.984348540 0.8581240 0.98434854
## AJGD_11119689 0.1750516 0.1299790 0.09365279 0.009561289 0.8380867 0.03862848
## AMP_11228639 0.9594853 0.8939962 0.96460556 0.893996173 0.7546823 0.69253309
## X31 X32 X33 X34 X35 X36
## ACR_11231843 0.9781871 0.984234811 0.9398376 0.9223077 0.960145689 0.9315044
## ADAO_11159808 0.5612683 0.611125290 0.4848774 0.6354365 0.635436457 0.7674546
## AGG_11236448 0.9207045 0.999030151 0.6029441 0.3391369 0.678573488 0.3391369
## AHL_11239959 0.9868171 0.988941703 0.9971224 0.9843485 0.858124002 0.9465804
## AJGD_11119689 0.6354365 0.006733018 0.2289565 0.9330496 0.006733018 0.3364692
## AMP_11228639 0.7737891 0.915004699 0.7737891 0.6476090 0.692533085 0.7347403
## X37 X38 X39 X40 X41 X42
## ACR_11231843 0.9702867 0.9781871 0.8891082 0.9398376 0.9122027 0.9921454
## ADAO_11159808 0.7265534 0.8047076 0.9043587 0.8047076 0.7865546 0.8930219
## AGG_11236448 0.2258099 0.3391369 0.2060235 0.6287538 0.4425521 0.6287538
## AHL_11239959 0.8255615 0.8423949 0.9744395 0.8863008 0.9102420 0.8987865
## AJGD_11119689 0.9837916 0.9930416 0.9545813 0.9837916 0.1920531 0.1169202
## AMP_11228639 0.6703784 0.8093636 0.8093636 0.7737891 0.7140068 0.8093636
## X43 X44 X45 X46 X47 X48
## ACR_11231843 0.98141954 0.98141954 0.99458674 0.9969948 0.99839139 0.9983914
## ADAO_11159808 0.70484551 0.70484551 0.82188985 0.6354365 0.91478567 0.6592147
## AGG_11236448 0.09794449 0.01493212 0.09794449 0.4693427 0.05894743 0.1531456
## AHL_11239959 0.87275440 0.82556153 0.72537103 0.9207045 0.65399692 0.7474389
## AJGD_11119689 0.45939831 0.02506050 0.04430325 0.1750516 0.10481961 0.5612683
## AMP_11228639 0.79202527 0.80936362 0.88218468 0.8939962 0.86947096 0.8694710
## X49 X50 X51 X52 X53
## ACR_11231843 0.999476896 0.997550027 0.996328711 0.99458674 0.99346629
## ADAO_11159808 0.659214659 0.484877378 0.586370888 0.80470761 0.38435943
## AGG_11236448 0.169673669 0.153145625 0.123392425 0.29116698 0.06731491
## AHL_11239959 0.807628499 0.678573488 0.725371032 0.67857349 0.67857349
## AJGD_11119689 0.002149111 0.008038418 0.008038418 0.03862848 0.24880536
## AMP_11228639 0.882184675 0.855838743 0.904927276 0.86947096 0.94744551
## X54 X55 X56 X57 X58 X59
## ACR_11231843 0.99059516 0.988783610 0.98667603 0.97818713 0.97818713 0.9781871
## ADAO_11159808 0.65921466 0.853292327 0.65921466 0.51041836 0.53591668 0.6354365
## AGG_11236448 0.02861380 0.038663594 0.49627279 0.07657227 0.38986888 0.4693427
## AHL_11239959 0.98149352 0.496272787 0.55006125 0.55006125 0.57667541 0.6539969
## AJGD_11119689 0.05063153 0.002149111 0.02905308 0.04430325 0.03862848 0.9409654
## AMP_11228639 0.90492728 0.893996173 0.90492728 0.84127788 0.80936362 0.7920253
## X60 X61 X62 X63 X64 X65
## ACR_11231843 0.9655215 0.9702867 0.9744921 0.97818713 0.9781871 0.9744921
## ADAO_11159808 0.6823764 0.8675089 0.9330496 0.80470761 0.6592147 0.9330496
## AGG_11236448 0.2060235 0.2911670 0.1531456 0.05894743 0.1233924 0.4962728
## AHL_11239959 0.6029441 0.5500612 0.6029441 0.62875379 0.5766754 0.5500612
## AJGD_11119689 0.9883141 0.6592147 0.9972785 0.51041836 0.9981792 0.2100287
## AMP_11228639 0.8412779 0.9049273 0.8558387 0.85583874 0.9049273 0.9150047
## X66 X67 X68 X69 X70 X71
## ACR_11231843 0.94735531 0.96552152 0.9655215 0.98423481 0.9866760 0.99059516
## ADAO_11159808 0.70484551 0.80470761 0.7265534 0.74744014 0.9603730 0.83808673
## AGG_11236448 0.08676751 0.13772126 0.3642054 0.57667541 0.3642054 0.60294413
## AHL_11239959 0.82556153 0.92070450 0.9597441 0.89878648 0.9936325 0.93882508
## AJGD_11119689 0.01133059 0.03356055 0.0250605 0.03356055 0.0250605 0.02153844
## AMP_11228639 0.93272581 0.86947096 0.8412779 0.93272581 0.8821847 0.86947096
## X72 X73 X74 X75 X76 X77
## ACR_11231843 0.99458674 0.99059516 0.99553303 0.98878361 0.9866760 0.9934663
## ADAO_11159808 0.70484551 0.90435865 0.83808673 0.70484551 0.7674546 0.8218898
## AGG_11236448 0.41602180 0.18730369 0.29116698 0.88630083 0.6029441 0.5232199
## AHL_11239959 0.99568352 0.97014186 0.97443947 0.99076202 0.9907620 0.9535354
## AJGD_11119689 0.03356055 0.08338983 0.02905308 0.01573686 0.7265534 0.0250605
## AMP_11228639 0.93272581 0.84127788 0.90492728 0.88218468 0.7920253 0.8093636
## X78 X79 X80 X81 X82 X83
## ACR_11231843 0.9934663 0.9963287 0.9945867 0.9945867 0.9887836 0.9945867
## ADAO_11159808 0.7865546 0.5612683 0.5863709 0.7674546 0.7265534 0.7865546
## AGG_11236448 0.3898689 0.5232199 0.3391369 0.4160218 0.3391369 0.4425521
## AHL_11239959 0.9535354 0.9868171 0.9597441 0.9907620 0.9964683 0.9465804
## AJGD_11119689 0.6592147 0.7265534 0.9243367 0.9951658 0.9742405 0.9043587
## AMP_11228639 0.9594853 0.9150047 0.7546823 0.7920253 0.8093636 0.7546823
## X84 X85 X86 X87 X88 X89
## ACR_11231843 0.9945867 0.9945867 0.9905952 0.9814195 0.9887836 0.984234811
## ADAO_11159808 0.7265534 0.7265534 0.7265534 0.7048455 0.8532923 0.682376447
## AGG_11236448 0.6287538 0.7474389 0.1873037 0.0286138 0.0286138 0.001776958
## AHL_11239959 0.9302165 0.7886110 0.9102420 0.7685352 0.9102420 0.725371032
## AJGD_11119689 0.9545813 0.9951658 0.9966916 0.7674546 0.9481275 0.991698168
## AMP_11228639 0.9732584 0.8694710 0.9594853 0.8821847 0.9875007 0.940441332
## X90 X91 X92 X93 X94
## ACR_11231843 0.9781871 0.97449209 0.97449209 0.978187135 0.978187135
## ADAO_11159808 0.6111253 0.70484551 0.86750890 0.611125290 0.804707611
## AGG_11236448 0.2060235 0.01493212 0.01767585 0.004085238 0.007298943
## AHL_11239959 0.8255615 0.82556153 0.72537103 0.678573488 0.993632550
## AJGD_11119689 0.3601399 0.61112529 0.29109580 0.384359435 0.747440143
## AMP_11228639 0.9768739 0.98288088 0.80936362 0.893996173 0.932725811
## X95 X96 X97 X98 X99
## ACR_11231843 0.98141954 0.97449209 0.978187135 0.9702867 0.986676032
## ADAO_11159808 0.70484551 0.76745456 0.747440143 0.7048455 0.867508904
## AGG_11236448 0.06731491 0.01767585 0.004977864 0.0087834 0.008783400
## AHL_11239959 0.99231451 0.65399692 0.550061248 0.6287538 0.576675409
## AJGD_11119689 0.21002875 0.99669159 0.994189293 0.5359167 0.006733018
## AMP_11228639 0.94044133 0.89399617 0.964605556 0.8694710 0.893996173
## X100 X101 X102 X103 X104
## ACR_11231843 0.95410756 0.974492089 0.970286714 0.965521524 0.978187135
## ADAO_11159808 0.65921466 0.635436457 0.747440143 0.704845513 0.635436457
## AGG_11236448 0.01256281 0.004977864 0.006040257 0.006040257 0.003338666
## AHL_11239959 0.49627279 0.628753792 0.550061248 0.442552103 0.946580443
## AJGD_11119689 0.03356055 0.057659723 0.313429528 0.853292327 0.682376447
## AMP_11228639 0.86947096 0.953779464 0.953779464 0.940441332 0.924259383
## X105 X106 X107 X108 X109
## ACR_11231843 0.970286714 0.981419536 0.954107565 0.99059516 0.986676032
## ADAO_11159808 0.561268274 0.535916678 0.484877378 0.88074642 0.635436457
## AGG_11236448 0.002717094 0.004085238 0.001427936 0.01256281 0.000274233
## AHL_11239959 0.678573488 0.523219920 0.653996918 0.52321992 0.469342657
## AJGD_11119689 0.986212039 0.360139895 0.065433303 0.06543330 0.093652790
## AMP_11228639 0.940441332 0.893996173 0.953779464 0.91500470 0.893996173
## X110 X111 X112 X113 X114
## ACR_11231843 0.978187135 0.9905951610 0.986676032 0.98423481 0.990595161
## ADAO_11159808 0.659214659 0.6592146589 0.767454561 0.68237645 0.635436457
## AGG_11236448 0.004977864 0.0003516147 0.007298943 0.01052615 0.002201951
## AHL_11239959 0.959744055 0.7685352079 0.442552103 0.38986888 0.389868878
## AJGD_11119689 0.786554647 0.9603729609 0.313429528 0.94096537 0.409039171
## AMP_11228639 0.882184675 0.8939961729 0.932725811 0.91500470 0.904927276
## X115 X116 X117 X118 X119 X120
## ACR_11231843 0.984234811 0.9887836 0.9945867 0.9945867 0.9842348 0.9781871
## ADAO_11159808 0.484877378 0.5359167 0.5104184 0.6823764 0.8675089 0.7865546
## AGG_11236448 0.006040257 0.7474389 0.9597441 0.9207045 0.9302165 0.9102420
## AHL_11239959 0.364205405 0.3898689 0.3642054 0.3898689 0.4425521 0.3642054
## AJGD_11119689 0.210028749 0.9742405 0.9930416 0.9837916 0.5612683 0.4848774
## AMP_11228639 0.792025266 0.9404413 0.8412779 0.9242594 0.9537795 0.8257849
## X121 X122 X123 X124 X125 X126
## ACR_11231843 0.98423481 0.98667603 0.9921454 0.98141954 0.98878361 0.97449209
## ADAO_11159808 0.90435865 0.90435865 0.8807464 0.95458129 0.82188985 0.99992884
## AGG_11236448 0.07657227 0.01767585 0.3147612 0.04467562 0.01256281 0.03866359
## AHL_11239959 0.38986888 0.49627279 0.2911670 0.36420541 0.38986888 0.36420541
## AJGD_11119689 0.99013214 0.98831408 0.9701565 0.97015647 0.97015647 0.94096537
## AMP_11228639 0.79202527 0.95377946 0.9800687 0.89399617 0.88218468 0.89399617
## X127 X128 X129 X130 X131 X132
## ACR_11231843 0.9866760 0.97449209 0.9702867 0.9905952 0.97818713 0.97449209
## ADAO_11159808 0.9996044 0.99992884 0.9330496 0.9043587 0.93304961 0.68237645
## AGG_11236448 0.2258099 0.07657227 0.2258099 0.3898689 0.04467562 0.03866359
## AHL_11239959 0.3642054 0.36420541 0.3391369 0.2060235 0.11014195 0.24662852
## AJGD_11119689 0.8807464 0.76745456 0.3843594 0.7265534 0.72655341 0.58637089
## AMP_11228639 0.9474455 0.79202527 0.9537795 0.9049273 0.75468226 0.91500470
## X133 X134 X135 X136 X137 X138
## ACR_11231843 0.98141954 0.99346629 0.98878361 0.97028671 0.97028671 0.99059516
## ADAO_11159808 0.76745456 0.74744014 0.72655341 0.61112529 0.58637089 0.63543646
## AGG_11236448 0.08676751 0.05141874 0.08676751 0.06731491 0.05141874 0.05141874
## AHL_11239959 0.24662852 0.20602351 0.18730369 0.26843330 0.26843330 0.85812400
## AJGD_11119689 0.51041836 0.70484551 0.56126827 0.63543646 0.56126827 0.29109580
## AMP_11228639 0.84127788 0.96918264 0.94044133 0.95948531 0.89399617 0.96460556
## X139 X140 X141 X142 X143 X144
## ACR_11231843 0.98423481 0.9655215 0.9814195 0.96014569 0.97449209 0.97028671
## ADAO_11159808 0.61112529 0.6111253 0.4848774 0.43408498 0.56126827 0.63543646
## AGG_11236448 0.08676751 0.1101420 0.1531456 0.07657227 0.05141874 0.08676751
## AHL_11239959 0.95353537 0.4176306 0.3808987 0.36193905 0.43693368 0.42241424
## AJGD_11119689 0.31342953 0.3601399 0.9330496 0.33646919 0.43408498 0.33646919
## AMP_11228639 0.91500470 0.4777816 0.7920253 0.62429782 0.64760899 0.60052379
## X145 X146 X147 X148 X149 X150
## ACR_11231843 0.98141954 0.9655215 0.95410756 0.96552152 0.97818713 0.98423481
## ADAO_11159808 0.51041836 0.3134295 0.33646919 0.53591668 0.53591668 0.29109580
## AGG_11236448 0.06731491 0.1531456 0.04467562 0.01493212 0.03866359 0.05141874
## AHL_11239959 0.59349343 0.9535354 0.97820653 0.80762850 0.57667541 0.88630083
## AJGD_11119689 0.56126827 0.3843594 0.51041836 0.92433672 0.82188985 0.53591668
## AMP_11228639 0.47778157 0.7546823 0.80936362 0.67037839 0.47778157 0.90492728
## X151 X152 X153 X154 X155 X156
## ACR_11231843 0.9655215 0.93150439 0.97818713 0.99947690 0.99980226 0.99059516
## ADAO_11159808 0.5612683 0.61112529 0.53591668 0.76745456 0.58637089 0.65921466
## AGG_11236448 0.2060235 0.02446806 0.06731491 0.06731491 0.03866359 0.04467562
## AHL_11239959 0.9465804 0.97443947 0.84239487 0.92070450 0.08676751 0.02861380
## AJGD_11119689 0.7265534 0.99304157 0.68237645 0.21002875 0.26953495 0.08338983
## AMP_11228639 0.6703784 0.75468226 0.94044133 0.62429782 0.57637094 0.57637094
## X157 X158 X159 X160 X161 X162
## ACR_11231843 0.96552152 0.96014569 0.9398376 0.93150439 0.9223077 0.889108250
## ADAO_11159808 0.70484551 0.72655341 0.6354365 0.80470761 0.6111253 0.804707611
## AGG_11236448 0.01767585 0.01767585 0.0087834 0.01052615 0.0286138 0.001776958
## AHL_11239959 0.20602351 0.20602351 0.4693427 0.52321992 0.8076285 0.550061248
## AJGD_11119689 0.40903917 0.99599329 0.2100287 0.45939831 0.9972785 0.747440143
## AMP_11228639 0.40439887 0.29033629 0.6925331 0.64760899 0.6703784 0.754682264
## X163 X164 X165 X166 X167 X168
## ACR_11231843 0.92230772 0.94735531 0.92230772 0.93150439 0.91220267 0.93983761
## ADAO_11159808 0.40903917 0.38435943 0.48487738 0.86750890 0.38435943 0.61112529
## AGG_11236448 0.02083869 0.02083869 0.01493212 0.01052615 0.01767585 0.01493212
## AHL_11239959 0.65399692 0.76853521 0.70239212 0.41602180 0.65399692 0.93882508
## AJGD_11119689 0.98101598 0.98831408 0.56126827 0.15903717 0.36013990 0.96554920
## AMP_11228639 0.85583874 0.90492728 0.57637094 0.67037839 0.57637094 0.57637094
## X169 X170 X171 X172 X173 X174
## ACR_11231843 0.96014569 0.9655215 0.95410756 0.9781871 0.978187135 0.98878361
## ADAO_11159808 0.58637089 0.5863709 0.48487738 0.6111253 0.726553413 0.98621204
## AGG_11236448 0.02083869 0.0087834 0.02083869 0.0286138 0.006040257 0.02446806
## AHL_11239959 0.46934266 0.4160218 0.67857349 0.8076285 0.807628499 0.36420541
## AJGD_11119689 0.12997898 0.8807464 0.88074642 0.8218898 0.269534949 0.98101598
## AMP_11228639 0.57637094 0.5025384 0.52728547 0.4531102 0.692533085 0.89399617
## X175 X176 X177 X178 X179 X180
## ACR_11231843 0.98423481 0.96014569 0.9601457 0.99214543 0.96552152 0.96552152
## ADAO_11159808 0.99304157 0.98621204 0.9883141 0.76745456 0.86750890 0.82188985
## AGG_11236448 0.01493212 0.01493212 0.0087834 0.01052615 0.01493212 0.02446806
## AHL_11239959 0.93021654 0.07657227 0.2258099 0.44255210 0.16967367 0.24662852
## AJGD_11119689 0.56126827 0.97015647 0.1750516 0.53591668 0.07399615 0.85329233
## AMP_11228639 0.85583874 0.62429782 0.8093636 0.52728547 0.55192762 0.50253841
## X181 X182 X183 X184 X185
## ACR_11231843 0.830592548 0.93150439 0.970286714 0.947355314 0.96014569
## ADAO_11159808 0.821889846 0.72655341 0.611125290 0.977846026 0.86750890
## AGG_11236448 0.004977864 0.01767585 0.007298943 0.004085238 0.01493212
## AHL_11239959 0.169673669 0.24662852 0.469342657 0.314761178 0.31476118
## AJGD_11119689 0.083389830 0.03862848 0.104819609 0.880746422 0.21002875
## AMP_11228639 0.825784930 0.35712754 0.502538410 0.527285472 0.69253309
## X186 X187 X188 X189 X190
## ACR_11231843 0.939837606 0.99553303 0.98878361 0.97818713 0.93983761
## ADAO_11159808 0.704845513 0.78655465 0.91478567 0.85329233 0.61112529
## AGG_11236448 0.006040257 0.36420541 0.06731491 0.41602180 0.05894743
## AHL_11239959 0.416021797 0.38986888 0.46934266 0.29116698 0.36420541
## AJGD_11119689 0.384359435 0.04430325 0.09365279 0.01844418 0.03356055
## AMP_11228639 0.380540191 0.50253841 0.52728547 0.64760899 0.67037839
## X191 X192 X193 X194 X195
## ACR_11231843 0.93150439 0.94735531 0.88910825 0.922307721 0.954107565
## ADAO_11159808 0.43408498 0.36013990 0.26953495 0.535916678 0.867508904
## AGG_11236448 0.01256281 0.07657227 0.03332782 0.007298943 0.007298943
## AHL_11239959 0.36420541 0.38986888 0.36420541 0.389868878 0.291166982
## AJGD_11119689 0.40903917 0.05063153 0.07399615 0.038628475 0.038628475
## AMP_11228639 0.73474032 0.38054019 0.60052379 0.502538410 0.576370945
## X196 X197 X198 X199 X200
## ACR_11231843 0.954107565 0.94735531 0.98878361 0.98141954 0.99755003
## ADAO_11159808 0.434084976 0.43408498 0.58637089 0.38435943 0.43408498
## AGG_11236448 0.007298943 0.01493212 0.01256281 0.01767585 0.02446806
## AHL_11239959 0.206023506 0.46934266 0.36420541 0.36420541 0.36420541
## AJGD_11119689 0.073996154 0.97784603 0.31342953 0.72655341 0.06543330
## AMP_11228639 0.882184675 0.55192762 0.62429782 0.67037839 0.38054019
## X201 X202 X203 X204 X205
## ACR_11231843 0.99458674 0.99458674 0.99458674 0.997550027 0.998704500
## ADAO_11159808 0.38435943 0.38435943 0.31342953 0.409039171 0.434084976
## AGG_11236448 0.02446806 0.01493212 0.01052615 0.004977864 0.006040257
## AHL_11239959 0.26843330 0.29116698 0.31476118 0.416021797 0.268433300
## AJGD_11119689 0.05765972 0.03356055 0.03862848 0.965549195 0.535916678
## AMP_11228639 0.31195462 0.62429782 0.38054019 0.404398873 0.527285472
## X206 X207 X208 X209 X210 X211
## ACR_11231843 0.99755003 0.99553303 0.99214543 0.99458674 0.99632871 0.99346629
## ADAO_11159808 0.33646919 0.31342953 0.38435943 0.29109580 0.22895652 0.29109580
## AGG_11236448 0.00878340 0.01767585 0.01256281 0.01493212 0.01767585 0.01256281
## AHL_11239959 0.29116698 0.36420541 0.36420541 0.41602180 0.41602180 0.26843330
## AJGD_11119689 0.01573686 0.01133059 0.03862848 0.02506050 0.05063153 0.03862848
## AMP_11228639 0.55192762 0.40439887 0.35712754 0.42861858 0.26944658 0.26944658
## X212 X213 X214 X215 X216 X217
## ACR_11231843 0.98878361 0.9781871 0.97028671 0.960145689 0.96014569 0.94735531
## ADAO_11159808 0.45939831 0.3843594 0.19205313 0.248805364 0.26953495 0.36013990
## AGG_11236448 0.03332782 0.2684333 0.03866359 0.007298943 0.03332782 0.01493212
## AHL_11239959 0.18730369 0.2060235 0.22580994 0.314761178 0.31476118 0.36420541
## AJGD_11119689 0.10481961 0.6592147 0.61112529 0.175051609 0.26953495 0.89302189
## AMP_11228639 0.35712754 0.2694466 0.35712754 0.404398873 0.38054019 0.31195462
## X218 X219 X220 X221 X222
## ACR_11231843 0.931504387 0.95410756 0.94735531 0.94735531 0.94735531
## ADAO_11159808 0.248805364 0.29109580 0.31342953 0.24880536 0.19205313
## AGG_11236448 0.004977864 0.02083869 0.05141874 0.09794449 0.06731491
## AHL_11239959 0.389868878 0.44255210 0.41602180 0.29116698 0.29116698
## AJGD_11119689 0.484877378 0.02905308 0.07399615 0.21002875 0.03356055
## AMP_11228639 0.211640290 0.40439887 0.42861858 0.35712754 0.38054019
## X223 X224 X225 X226 X227 X228
## ACR_11231843 0.92230772 0.96014569 0.92230772 0.93983761 0.91220267 0.8305925
## ADAO_11159808 0.74744014 0.17505161 0.29109580 0.40903917 0.43408498 0.4848774
## AGG_11236448 0.04467562 0.05141874 0.05894743 0.02446806 0.05141874 0.1873037
## AHL_11239959 0.38986888 0.29116698 0.18730369 0.38986888 0.36420541 0.4425521
## AJGD_11119689 0.24880536 0.15903717 0.89302189 0.11692019 0.72655341 0.3364692
## AMP_11228639 0.40439887 0.38054019 0.33424094 0.35712754 0.38054019 0.4531102
## X229 X230 X231 X232 X233 X234
## ACR_11231843 0.9473553 0.97449209 0.9814195 0.9702867 0.97028671 0.97028671
## ADAO_11159808 0.2910958 0.29109580 0.4090392 0.3843594 0.40903917 0.72655341
## AGG_11236448 0.1233924 0.02446806 0.1377213 0.1873037 0.06731491 0.15314562
## AHL_11239959 0.3898689 0.31476118 0.3391369 0.7474389 0.36420541 0.38986888
## AJGD_11119689 0.9655492 0.05765972 0.3843594 0.9545813 0.31342953 0.07399615
## AMP_11228639 0.2903363 0.45311021 0.3342409 0.4286186 0.35712754 0.35712754
## X235 X236 X237 X238 X239 X240
## ACR_11231843 0.95410756 0.99458674 0.970286714 0.9473553 0.97449209 0.93150439
## ADAO_11159808 0.72655341 0.31342953 0.360139895 0.3843594 0.29109580 0.43408498
## AGG_11236448 0.05894743 0.04467562 0.097944494 0.1233924 0.04467562 0.02083869
## AHL_11239959 0.46934266 0.70239212 0.339136925 0.4425521 0.38986888 0.95353537
## AJGD_11119689 0.68237645 0.12997898 0.002624277 0.8047076 0.89302189 0.02506050
## AMP_11228639 0.45311021 0.47778157 0.754682264 0.5272855 0.31195462 0.60052379
## X241 X242 X243 X244 X245 X246
## ACR_11231843 0.9541076 0.98141954 0.97449209 0.95410756 0.97449209 0.98423481
## ADAO_11159808 0.2695349 0.38435943 0.36013990 0.31342953 0.26953495 0.22895652
## AGG_11236448 0.0286138 0.05894743 0.02446806 0.03866359 0.06731491 0.03866359
## AHL_11239959 0.1696737 0.24662852 0.31476118 0.80762850 0.26843330 0.31476118
## AJGD_11119689 0.9810160 0.29109580 0.12997898 0.98379161 0.09365279 0.98379161
## AMP_11228639 0.2694466 0.26944658 0.40439887 0.21164029 0.17750562 0.19411607
## X247 X248 X249 X250 X251
## ACR_11231843 0.97028671 0.978187135 0.91220267 0.98141954 0.98667603
## ADAO_11159808 0.24880536 0.248805364 0.26953495 0.21002875 0.24880536
## AGG_11236448 0.05141874 0.044675617 0.02861380 0.01767585 0.02083869
## AHL_11239959 0.36420541 0.364205405 0.33913693 0.33913693 0.82556153
## AJGD_11119689 0.05765972 0.009561289 0.09365279 0.96554920 0.05765972
## AMP_11228639 0.38054019 0.734740320 0.50253841 0.38054019 0.57637094
## X252 X253 X254 X255 X256 X257
## ACR_11231843 0.68872943 0.79499172 0.99632871 0.99214543 0.98423481 0.97449209
## ADAO_11159808 0.22895652 0.22895652 0.22895652 0.24880536 0.22895652 0.21002875
## AGG_11236448 0.02861380 0.01052615 0.01767585 0.01052615 0.03866359 0.03332782
## AHL_11239959 0.20602351 0.16967367 0.24662852 0.36420541 0.29116698 0.29116698
## AJGD_11119689 0.03862848 0.04430325 0.04430325 0.04430325 0.24880536 0.02506050
## AMP_11228639 0.52728547 0.19411607 0.24933856 0.38054019 0.33424094 0.60052379
## X258 X259 X260 X261 X262 X263
## ACR_11231843 0.96014569 0.93150439 0.91220267 0.91220267 0.9702867 0.94735531
## ADAO_11159808 0.22895652 0.51041836 0.74744014 0.92433672 0.9043587 0.76745456
## AGG_11236448 0.03866359 0.05141874 0.02083869 0.02083869 0.0087834 0.02083869
## AHL_11239959 0.33913693 0.33913693 0.36420541 0.33913693 0.4425521 0.41602180
## AJGD_11119689 0.01573686 0.78655465 0.80470761 0.51041836 0.3601399 0.26953495
## AMP_11228639 0.33424094 0.45311021 0.33424094 0.33424094 0.7347403 0.82578493
## X264 X265 X266 X267 X268 X269
## ACR_11231843 0.93983761 0.92230772 0.98423481 0.98141954 0.98423481 0.98667603
## ADAO_11159808 0.65921466 0.83808673 0.82188985 0.48487738 0.31342953 0.31342953
## AGG_11236448 0.03332782 0.05894743 0.01767585 0.03866359 0.01493212 0.01493212
## AHL_11239959 0.26843330 0.41602180 0.46934266 0.38986888 0.44255210 0.97443947
## AJGD_11119689 0.38435943 0.61112529 0.43408498 0.12997898 0.08338983 0.03862848
## AMP_11228639 0.73474032 0.82578493 0.80936362 0.50253841 0.80936362 0.71400677
## X270 X271 X272 X273 X274 X275
## ACR_11231843 0.98141954 0.94735531 0.94735531 0.94735531 0.96014569 0.94735531
## ADAO_11159808 0.31342953 0.33646919 0.61112529 0.65921466 0.43408498 0.65921466
## AGG_11236448 0.02083869 0.02446806 0.03332782 0.02861380 0.02446806 0.00878340
## AHL_11239959 0.98894170 0.97014186 0.78861097 0.93021654 0.97443947 0.36420541
## AJGD_11119689 0.06543330 0.05765972 0.08338983 0.03356055 0.01844418 0.02905308
## AMP_11228639 0.89399617 0.92425938 0.55192762 0.40439887 0.45311021 0.26944658
## X276 X277 X278 X279 X280
## ACR_11231843 0.96014569 0.97028671 0.97028671 0.97449209 0.960145689
## ADAO_11159808 0.56126827 0.36013990 0.45939831 0.53591668 0.360139895
## AGG_11236448 0.05894743 0.01767585 0.02083869 0.01767585 0.003338666
## AHL_11239959 0.24662852 0.41602180 0.31476118 0.41602180 0.920704503
## AJGD_11119689 0.09365279 0.04430325 0.08338983 0.05765972 0.065433303
## AMP_11228639 0.29033629 0.79202527 0.64760899 0.52728547 0.249338564
## X281 X282 X283 X284 X285
## ACR_11231843 0.960145689 0.97028671 0.95410756 0.93150439 0.95410756
## ADAO_11159808 0.747440143 0.53591668 0.56126827 0.94812752 0.97784603
## AGG_11236448 0.001776958 0.36420541 0.24662852 0.93021654 0.44255210
## AHL_11239959 0.364205405 0.36420541 0.36420541 0.49627279 0.74743887
## AJGD_11119689 0.038628475 0.03356055 0.02153844 0.01573686 0.01844418
## AMP_11228639 0.380540191 0.86947096 0.50253841 0.62429782 0.69253309
## X286 X287 X288 X289 X290 X291
## ACR_11231843 0.97449209 0.95410756 0.96014569 0.96014569 0.97028671 0.93150439
## ADAO_11159808 0.61112529 0.76745456 0.58637089 0.74744014 0.40903917 0.58637089
## AGG_11236448 0.12339242 0.18730369 0.06731491 0.01256281 0.26843330 0.02861380
## AHL_11239959 0.99568352 0.98149352 0.60294413 0.60294413 0.93882508 0.95353537
## AJGD_11119689 0.05063153 0.03862848 0.02506050 0.02506050 0.01573686 0.02153844
## AMP_11228639 0.57637094 0.94044133 0.42861858 0.38054019 0.71400677 0.62429782
## X292 X293 X294 X295 X296
## ACR_11231843 0.91220267 0.97449209 0.9541076 0.96552152 0.965521524
## ADAO_11159808 0.40903917 0.45939831 0.4340850 0.45939831 0.838086734
## AGG_11236448 0.01256281 0.05894743 0.1377213 0.07657227 0.858124002
## AHL_11239959 0.94658044 0.88630083 0.9744395 0.99646826 0.988941703
## AJGD_11119689 0.02905308 0.01844418 0.0133778 0.01573686 0.008038418
## AMP_11228639 0.55192762 0.62429782 0.6703784 0.62429782 0.714006767
## X297 X298 X299 X300 X301
## ACR_11231843 0.965521524 0.988783610 0.965521524 0.9601457 0.931504387
## ADAO_11159808 0.635436457 0.535916678 0.747440143 0.8380867 0.954581290
## AGG_11236448 0.990762024 0.938825080 0.978206535 0.7253710 0.768535208
## AHL_11239959 0.858124002 0.981493519 0.678573488 0.5232199 0.339136925
## AJGD_11119689 0.008038418 0.005618609 0.005618609 0.0133778 0.009561289
## AMP_11228639 0.576370945 0.624297821 0.809363621 0.7347403 0.576370945
## X302 X303 X304 X305 X306
## ACR_11231843 0.960145689 0.9655215 0.954107565 0.970286714 0.939837606
## ADAO_11159808 0.838086734 0.6823764 0.409039171 0.510418361 0.535916678
## AGG_11236448 0.550061248 0.2466285 0.137721255 0.058947431 0.086767509
## AHL_11239959 0.339136925 0.3391369 0.291166982 0.268433300 0.291166982
## AJGD_11119689 0.004671141 0.0133778 0.001753322 0.004671141 0.003192404
## AMP_11228639 0.624297821 0.6005238 0.502538410 0.670378389 0.600523788
## X307 X308 X309 X310 X311
## ACR_11231843 0.965521524 0.965521524 0.978187135 0.947355314 0.954107565
## ADAO_11159808 0.409039171 0.434084976 0.192053134 0.434084976 0.586370888
## AGG_11236448 0.012562813 0.010526149 0.496272787 0.038663594 0.028613803
## AHL_11239959 0.314761178 0.920704503 0.153145625 0.291166982 0.314761178
## AJGD_11119689 0.008038418 0.008038418 0.009561289 0.009561289 0.006733018
## AMP_11228639 0.624297821 0.428618577 0.600523788 0.624297821 0.670378389
## X312 X313 X314 X315 X316
## ACR_11231843 0.97028671 0.96552152 0.981419536 0.970286714 0.947355314
## ADAO_11159808 0.61112529 0.61112529 0.586370888 0.704845513 0.635436457
## AGG_11236448 0.03866359 0.38986888 0.523219920 0.076572273 0.058947431
## AHL_11239959 0.98149352 0.26843330 0.268433300 0.628753792 0.187303691
## AJGD_11119689 0.01133059 0.01573686 0.008038418 0.006733018 0.006733018
## AMP_11228639 0.67037839 0.62429782 0.600523788 0.624297821 0.714006767
## X317 X318 X319 X320 X321
## ACR_11231843 0.954107565 0.965521524 0.939837606 0.939837606 0.965521524
## ADAO_11159808 0.726553413 0.704845513 0.747440143 0.682376447 0.659214659
## AGG_11236448 0.187303691 0.038663594 0.153145625 0.169673669 0.123392425
## AHL_11239959 0.268433300 0.291166982 0.246628519 0.291166982 0.246628519
## AJGD_11119689 0.006733018 0.006733018 0.005618609 0.003192404 0.005618609
## AMP_11228639 0.551927625 0.576370945 0.600523788 0.600523788 0.551927625
## X322 X323 X324 X325 X326 X327
## ACR_11231843 0.931504387 0.95410756 0.95410756 0.93983761 0.9702867 0.9541076
## ADAO_11159808 0.682376447 0.85329233 0.68237645 0.70484551 0.6354365 0.5863709
## AGG_11236448 0.169673669 0.07657227 0.09794449 0.07657227 0.1531456 0.1531456
## AHL_11239959 0.389868878 0.29116698 0.78861097 0.20602351 0.2466285 0.1873037
## AJGD_11119689 0.005618609 0.01337780 0.10481961 0.33646919 0.6823764 0.4593983
## AMP_11228639 0.600523788 0.62429782 0.57637094 0.62429782 0.6005238 0.6703784
## X328 X329 X330 X331 X332 X333
## ACR_11231843 0.87605241 0.960145689 0.93150439 0.9223077 0.87605241 0.88910825
## ADAO_11159808 0.72655341 0.682376447 0.45939831 0.7048455 0.51041836 0.53591668
## AGG_11236448 0.18730369 0.187303691 0.07657227 0.2466285 0.02083869 0.08676751
## AHL_11239959 0.41602180 0.314761178 0.36420541 0.3898689 0.44255210 0.38986888
## AJGD_11119689 0.02905308 0.009561289 0.56126827 0.4340850 0.07399615 0.02506050
## AMP_11228639 0.52728547 0.527285472 0.62429782 0.5763709 0.57637094 0.50253841
## X334 X335 X336 X337 X338 X339
## ACR_11231843 0.9702867137 0.9223077 0.88910825 0.9315044 0.91220267 0.9122027
## ADAO_11159808 0.4593983094 0.4340850 0.36013990 0.3843594 0.33646919 0.5359167
## AGG_11236448 0.3147611783 0.1531456 0.09794449 0.1531456 0.06731491 0.1531456
## AHL_11239959 0.5766754088 0.5500612 0.31476118 0.4962728 0.41602180 0.2466285
## AJGD_11119689 0.0003790265 0.7474401 0.08338983 0.8047076 0.03356055 0.5612683
## AMP_11228639 0.5272854717 0.4531102 0.35712754 0.7546823 0.64760899 0.6925331
## X340 X341 X342 X343 X344 X345
## ACR_11231843 0.8891082 0.94735531 0.88910825 0.93983761 0.90114823 0.9473553
## ADAO_11159808 0.4340850 0.40903917 0.38435943 0.38435943 0.36013990 0.4090392
## AGG_11236448 0.1531456 0.02083869 0.06731491 0.04467562 0.05894743 0.1101420
## AHL_11239959 0.2060235 0.20602351 0.20602351 0.18730369 0.29116698 0.2911670
## AJGD_11119689 0.9330496 0.03862848 0.12997898 0.10481961 0.31342953 0.7265534
## AMP_11228639 0.6703784 0.73474032 0.88218468 0.38054019 0.57637094 0.5763709
## X346 X347 X348 X349 X350 X351
## ACR_11231843 0.93983761 0.9955330 0.99214543 0.96552152 0.9473553 0.931504387
## ADAO_11159808 0.40903917 0.4090392 0.40903917 0.40903917 0.4593983 0.384359435
## AGG_11236448 0.15314562 0.1873037 0.15314562 0.15314562 0.1233924 0.123392425
## AHL_11239959 0.24662852 0.4160218 0.36420541 0.41602180 0.3898689 0.364205405
## AJGD_11119689 0.01844418 0.2488054 0.07399615 0.07399615 0.0654333 0.005618609
## AMP_11228639 0.42861858 0.7546823 0.62429782 0.40439887 0.4531102 0.527285472
## X352 X353 X354 X355 X356 X357
## ACR_11231843 0.9223077 0.9814195 0.9223077 0.91220267 0.8891082 0.8891082
## ADAO_11159808 0.3843594 0.3601399 0.3601399 0.33646919 0.3601399 0.3601399
## AGG_11236448 0.1101420 0.1101420 0.1101420 0.06731491 0.1101420 0.1101420
## AHL_11239959 0.3642054 0.3898689 0.8727544 0.16967367 0.3642054 0.6539969
## AJGD_11119689 0.1920531 0.7048455 0.1048196 0.48487738 0.3364692 0.4090392
## AMP_11228639 0.8257849 0.4043989 0.4531102 0.40439887 0.7546823 0.3571275
## X358 X359 X360 X361 X362 X363
## ACR_11231843 0.9011482 0.92230772 0.8891082 0.87605241 0.93150439 0.88910825
## ADAO_11159808 0.3843594 0.38435943 0.7474401 0.68237645 0.65921466 0.90435865
## AGG_11236448 0.1233924 0.05141874 0.0087834 0.07657227 0.01767585 0.03332782
## AHL_11239959 0.2911670 0.57667541 0.1233924 0.16967367 0.29116698 0.82556153
## AJGD_11119689 0.4090392 0.24880536 0.6354365 0.08338983 0.40903917 0.31342953
## AMP_11228639 0.5763709 0.35712754 0.4531102 0.60052379 0.82578493 0.33424094
## X364 X365 X366 X367 X368 X369
## ACR_11231843 0.93150439 0.92230772 0.9011482 0.9122027 0.9011482 0.91220267
## ADAO_11159808 0.38435943 0.72655341 0.4848774 0.6354365 0.5104184 0.76745456
## AGG_11236448 0.03332782 0.07657227 0.1531456 0.0286138 0.1377213 0.06731491
## AHL_11239959 0.41602180 0.13772126 0.1873037 0.2466285 0.1873037 0.29116698
## AJGD_11119689 0.11692019 0.38435943 0.1590372 0.1750516 0.2100287 0.61112529
## AMP_11228639 0.45311021 0.50253841 0.4286186 0.5519276 0.5763709 0.57637094
## X370 X371 X372 X373 X374 X375
## ACR_11231843 0.9122027 0.93150439 0.9315044 0.95410756 0.9315044 0.93150439
## ADAO_11159808 0.5104184 0.53591668 0.3134295 0.36013990 0.4848774 0.40903917
## AGG_11236448 0.3391369 0.11014195 0.1377213 0.11014195 0.2060235 0.13772126
## AHL_11239959 0.2466285 0.08676751 0.2466285 0.13772126 0.8255615 0.31476118
## AJGD_11119689 0.5359167 0.56126827 0.1299790 0.09365279 0.2695349 0.02905308
## AMP_11228639 0.6005238 0.60052379 0.6242978 0.57637094 0.6005238 0.62429782
## X376 X377 X378 X379 X380 X381
## ACR_11231843 0.9315044 0.93150439 0.9223077 0.94735531 0.9223077 0.95410756
## ADAO_11159808 0.4340850 0.36013990 0.5863709 0.43408498 0.3843594 0.51041836
## AGG_11236448 0.1696737 0.09794449 0.0286138 0.29116698 0.1696737 0.20602351
## AHL_11239959 0.2258099 0.26843330 0.2911670 0.29116698 0.2060235 0.15314562
## AJGD_11119689 0.1440142 0.43408498 0.2488054 0.01133059 0.1048196 0.03356055
## AMP_11228639 0.6005238 0.67037839 0.5519276 0.55192762 0.5025384 0.52728547
## X382 X383 X384 X385 X386 X387
## ACR_11231843 0.95410756 0.9473553 0.9223077 0.99974625 0.96552152 0.9744921
## ADAO_11159808 0.33646919 0.3601399 0.3601399 0.36013990 0.36013990 0.3843594
## AGG_11236448 0.09794449 0.1233924 0.1101420 0.04467562 0.08676751 0.1873037
## AHL_11239959 0.09794449 0.1873037 0.1873037 0.18730369 0.70239212 0.7886110
## AJGD_11119689 0.33646919 0.3364692 0.3601399 0.29109580 0.12997898 0.1750516
## AMP_11228639 0.45311021 0.4043989 0.7347403 0.75468226 0.50253841 0.3805402
## X388 X389 X390 X391 X392 X393
## ACR_11231843 0.97818713 0.96014569 0.93150439 0.96014569 0.94735531 0.97818713
## ADAO_11159808 0.61112529 0.68237645 0.51041836 0.96554920 0.70484551 0.86750890
## AGG_11236448 0.15314562 0.13772126 0.12339242 0.11014195 0.09794449 0.13772126
## AHL_11239959 0.93882508 0.85812400 0.78861097 0.94658044 0.98894170 0.99712243
## AJGD_11119689 0.08338983 0.07399615 0.01573686 0.04430325 0.12997898 0.03862848
## AMP_11228639 0.71400677 0.55192762 0.42861858 0.75468226 0.73474032 0.73474032
## X394 X395 X396 X397 X398 X399
## ACR_11231843 0.99214543 0.98878361 0.99346629 0.9945867 0.99458674 0.9980107
## ADAO_11159808 0.94812752 0.90435865 0.72655341 0.5863709 0.68237645 0.5359167
## AGG_11236448 0.12339242 0.03332782 0.07657227 0.1233924 0.04467562 0.1377213
## AHL_11239959 0.94658044 0.99922961 0.99231451 0.9971224 0.97443947 0.8255615
## AJGD_11119689 0.05063153 0.03356055 0.03862848 0.8380867 0.06543330 0.5863709
## AMP_11228639 0.77378906 0.92425938 0.67037839 0.7546823 0.84127788 0.4777816
## X400 X401 X402 X403 X404 X405
## ACR_11231843 0.9969948 0.9975500 0.99839139 0.99553303 0.99214543 0.98667603
## ADAO_11159808 0.4340850 0.6592147 0.61112529 0.40903917 0.51041836 0.58637089
## AGG_11236448 0.1101420 0.1101420 0.02083869 0.05141874 0.05894743 0.09794449
## AHL_11239959 0.6539969 0.5500612 0.84239487 0.85812400 0.29116698 0.70239212
## AJGD_11119689 0.1048196 0.7048455 0.83808673 0.65921466 0.78655465 0.80470761
## AMP_11228639 0.9150047 0.5519276 0.55192762 0.47778157 0.60052379 0.93272581
## X406 X407 X408 X409 X410 X411
## ACR_11231843 0.97818713 0.9541076 0.9655215 0.9702867 0.9781871 0.96014569
## ADAO_11159808 0.63543646 0.5104184 0.5104184 0.5359167 0.7265534 0.43408498
## AGG_11236448 0.05894743 0.2258099 0.0286138 0.1531456 0.3642054 0.06731491
## AHL_11239959 0.62875379 0.2911670 0.8255615 0.5766754 0.5766754 0.05141874
## AJGD_11119689 0.70484551 0.5863709 0.6354365 0.6354365 0.2289565 0.45939831
## AMP_11228639 0.92425938 0.5519276 0.7140068 0.5763709 0.9646056 0.93272581
## X412 X413 X414 X415 X416 X417
## ACR_11231843 0.9122027 0.94735531 0.95410756 0.97818713 0.978187135 0.96552152
## ADAO_11159808 0.5359167 0.51041836 0.53591668 0.48487738 0.459398309 0.48487738
## AGG_11236448 0.2684333 0.05894743 0.26843330 0.06731491 0.364205405 0.88630083
## AHL_11239959 0.1377213 0.11014195 0.05141874 0.08676751 0.097944494 0.08676751
## AJGD_11119689 0.2488054 0.24880536 0.96037296 0.05063153 0.006733018 0.03356055
## AMP_11228639 0.8129198 0.76315256 0.84756712 0.81885741 0.774884343 0.86235243
## X418 X419 X420 X421 X422 X423
## ACR_11231843 0.9473553 0.96552152 0.9702867 0.9702867 0.93150439 0.96014569
## ADAO_11159808 0.5104184 0.45939831 0.5104184 0.4340850 0.40903917 0.40903917
## AGG_11236448 0.8076285 0.99231451 0.8727544 0.7474389 0.44255210 0.31476118
## AHL_11239959 0.2060235 0.11014195 0.1873037 0.1696737 0.24662852 0.26843330
## AJGD_11119689 0.0654333 0.05765972 0.1299790 0.1048196 0.08338983 0.01573686
## AMP_11228639 0.7987106 0.76916335 0.7357314 0.5907512 0.61606017 0.75995038
## X424 X425 X426 X427 X428 X429
## ACR_11231843 0.96014569 0.96014569 0.98141954 0.94735531 0.96552152 0.99699483
## ADAO_11159808 0.43408498 0.40903917 0.89302189 0.72655341 0.76745456 0.63543646
## AGG_11236448 0.11014195 0.08676751 0.08676751 0.03866359 0.18730369 0.16967367
## AHL_11239959 0.29116698 0.15314562 0.07657227 0.16967367 0.20602351 0.13772126
## AJGD_11119689 0.05765972 0.06543330 0.08338983 0.07399615 0.08338983 0.08338983
## AMP_11228639 0.77378906 0.82578493 0.85583874 0.88218468 0.71400677 0.57637094
## X430 X431 X432 X433 X434 X435
## ACR_11231843 0.003559488 0.8305925 0.96552152 0.90114823 0.94735531 0.88910825
## ADAO_11159808 0.747440143 0.8218898 0.89302189 0.96037296 0.89302189 0.45939831
## AGG_11236448 0.788610967 0.8581240 0.67857349 0.09794449 0.02861380 0.12339242
## AHL_11239959 0.110141952 0.1873037 0.16967367 0.62875379 0.22580994 0.29116698
## AJGD_11119689 0.083389830 0.2488054 0.05063153 0.31342953 0.03862848 0.09365279
## AMP_11228639 0.924259383 0.7140068 0.71400677 0.67037839 0.79202527 0.82578493
## X436 X437 X438 X439 X440 X441
## ACR_11231843 0.8305925 0.86195710 0.86195710 0.86195710 0.8891082 0.9315044
## ADAO_11159808 0.5863709 0.43408498 0.38435943 0.45939831 0.4090392 0.4848774
## AGG_11236448 0.2258099 0.03866359 0.01052615 0.07657227 0.1873037 0.8727544
## AHL_11239959 0.2911670 0.29116698 0.41602180 0.18730369 0.2684333 0.2258099
## AJGD_11119689 0.1750516 0.06543330 0.10481961 0.02506050 0.1440142 0.1920531
## AMP_11228639 0.8093636 0.89399617 0.86947096 0.86947096 0.6925331 0.9049273
## X442 X443 X444 X445 X446 X447
## ACR_11231843 0.9398376 0.97028671 0.95410756 0.9315044 0.98423481 0.9473553
## ADAO_11159808 0.5863709 0.63543646 0.72655341 0.8807464 0.63543646 0.7474401
## AGG_11236448 0.1101420 0.02083869 0.02446806 0.8076285 0.38986888 0.2911670
## AHL_11239959 0.2466285 0.13772126 0.26843330 0.2911670 0.18730369 0.2466285
## AJGD_11119689 0.4340850 0.70484551 0.14401420 0.4848774 0.09365279 0.1048196
## AMP_11228639 0.8821847 0.97325844 0.79202527 0.4777816 0.52728547 0.6925331
## X448 X449 X450 X451 X452 X453
## ACR_11231843 0.9601457 0.9398376 0.9541076 0.98423481 0.9398376 0.9398376
## ADAO_11159808 0.8532923 0.9147857 0.9742405 0.68237645 0.7865546 0.5612683
## AGG_11236448 0.2911670 0.3147612 0.1377213 0.78861097 0.1377213 0.8581240
## AHL_11239959 0.1696737 0.2466285 0.1696737 0.13772126 0.2466285 0.2911670
## AJGD_11119689 0.1169202 0.1590372 0.2100287 0.03862848 0.0133778 0.3134295
## AMP_11228639 0.5519276 0.8412779 0.7737891 0.82578493 0.7347403 0.7920253
## X454 X455 X456 X457 X458 X459
## ACR_11231843 0.9655215 0.9541076 0.9921454 0.9994769 0.9994769 0.9998814
## ADAO_11159808 0.7265534 0.5359167 0.6823764 0.8047076 0.7265534 0.6111253
## AGG_11236448 0.9782065 0.9868171 0.6785735 0.4693427 0.3642054 0.3642054
## AHL_11239959 0.2684333 0.2466285 0.2466285 0.3391369 0.2258099 0.2466285
## AJGD_11119689 0.9481275 0.7265534 0.3134295 0.2488054 0.1750516 0.4090392
## AMP_11228639 0.9853468 0.7546823 0.8939962 0.8257849 0.6476090 0.8939962
## X460 X461 X462 X463 X464 X465
## ACR_11231843 0.9996757 0.98141954 0.9702867 0.9702867 0.9702867 0.9905952
## ADAO_11159808 0.6823764 0.58637089 0.5863709 0.4593983 0.6111253 0.2695349
## AGG_11236448 0.2684333 0.08676751 0.1531456 0.6785735 0.3898689 0.4693427
## AHL_11239959 0.3898689 0.07657227 0.1101420 0.2258099 0.1696737 0.1377213
## AJGD_11119689 0.4340850 0.78655465 0.4848774 0.5104184 0.6592147 0.1920531
## AMP_11228639 0.8412779 0.75468226 0.8412779 0.8257849 0.8093636 0.6703784
## X466 X467 X468 X469 X470 X471
## ACR_11231843 0.93983761 0.97449209 0.9744921 0.9122027 0.9122027 0.9011482
## ADAO_11159808 0.72655341 0.72655341 0.4593983 0.5612683 0.8218898 0.5359167
## AGG_11236448 0.16967367 0.38986888 0.7023921 0.2684333 0.1377213 0.3147612
## AHL_11239959 0.26843330 0.31476118 0.3391369 0.8863008 0.1873037 0.4962728
## AJGD_11119689 0.05063153 0.03862848 0.9545813 0.3364692 0.9409654 0.9243367
## AMP_11228639 0.90492728 0.69253309 0.6476090 0.7347403 0.6703784 0.7546823
## X472 X473 X474 X475 X476 X477
## ACR_11231843 0.9473553 0.92230772 0.8760524 0.9122027 0.91220267 0.90114823
## ADAO_11159808 0.5104184 0.65921466 0.3843594 0.7865546 0.40903917 0.53591668
## AGG_11236448 0.1873037 0.20602351 0.1531456 0.1873037 0.18730369 0.11014195
## AHL_11239959 0.2466285 0.20602351 0.1696737 0.2466285 0.05894743 0.15314562
## AJGD_11119689 0.8807464 0.05765972 0.1440142 0.1590372 0.03356055 0.04430325
## AMP_11228639 0.7140068 0.67037839 0.7347403 0.7737891 0.73474032 0.73474032
## X478 X479 X480 DDclust_EUCL_cuantiles
## ACR_11231843 0.94735531 0.92230772 0.94735531 1
## ADAO_11159808 0.72655341 0.56126827 0.45939831 1
## AGG_11236448 0.12339242 0.07657227 0.05894743 2
## AHL_11239959 0.07657227 0.15314562 0.20602351 2
## AJGD_11119689 0.00386890 0.07399615 0.86750890 2
## AMP_11228639 0.62429782 0.71400677 0.69253309 1## Mean by groups
rp_tbl_EUCL <- aggregate(plotting_EUCL, by = list(plotting_EUCL$DDclust_EUCL_cuantiles), mean)
row.names(rp_tbl_EUCL) <- paste0("Group",rp_tbl_EUCL$DDclust_EUCL_cuantiles)
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 0.8853164 0.6335630
## X2 0.8897355 0.6406352
## X3 0.9071830 0.5938372
## X4 0.8942103 0.6367462
## X5 0.8909859 0.5910105
## X6 0.8531552 0.6037154# 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)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.7836245 0.6756908 0.8445631 0.9099744NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_PER
res<-NbClust(datos_PER, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.4911 0.2491 0.2719 0.3057res$Best.nc## Number_clusters Value_Index
## 2.0000 0.4911#res$Best.partitionhcintper_PER <- hclust(DD_PER, "ward.D2")
fviz_dend(hcintper_PER, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)
DDclust_PER_cuantiles <- cutree( hclust(DD_PER, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_PER_cuantiles))
fviz_silhouette(silhouette(DDclust_PER_cuantiles, DD_PER))## cluster size ave.sil.width
## 1 1 50 0.54
## 2 2 8 0.21
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_cuantiles[DDclust_PER_cuantiles == 2]),names(DDclust_PER_cuantiles[DDclust_PER_cuantiles == 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 | 6 | 0 |
| NO DETERIORO | 44 | 8 |
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 | 0.12 | 0 |
| NO DETERIORO | 0.88 | 1 |
Random Forest: Discriminant TSCLust PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_cuantiles)
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$CLUSTER)
newMWMOTE_PER <- oversample(data_frame_merge_PER, ratio = 0.85, method = "MWMOTE", classAttr = "CLUSTER")
newMWMOTE_PER <- data.frame(newMWMOTE_PER)
pos_1 <- get_column_position(newMWMOTE_PER, "SAPI_0_8h")
pos_2 <- get_column_position(newMWMOTE_PER, "PAUSAS_APNEA")
columns_to_round <- c(pos_1:pos_2)
newMWMOTE_PER[, columns_to_round] <- lapply(newMWMOTE_PER[, columns_to_round], function(x) round(x, 1))
table(newMWMOTE_PER$CLUSTER)##
## 1 2
## 50 43set.seed(123)
pos_1 = get_column_position(newMWMOTE_PER, "SAPI_0_8h")
pos_2 = get_column_position(newMWMOTE_PER, "PAUSAS_APNEA")
col_names_factor <- names(newMWMOTE_PER[pos_1:pos_2])
newMWMOTE_PER[col_names_factor] <- lapply(newMWMOTE_PER[col_names_factor] , factor)
RF_PER <- randomForest(CLUSTER ~ ., data = newMWMOTE_PER)
print(RF_PER)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newMWMOTE_PER)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 4.3%
## Confusion matrix:
## 1 2 class.error
## 1 47 3 0.06000000
## 2 1 42 0.02325581Importance
kable(RF_PER$importance[order(RF_PER$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_WOOD_DOWNES_INGRESO | 7.4074045 |
| SCORE_CRUCES_INGRESO | 6.5504895 |
| SAPI_0_8h | 4.0000376 |
| DIAS_O2_TOTAL | 2.9619896 |
| EDAD | 2.9220530 |
| LM | 2.6634340 |
| FR_0_8h | 2.4910603 |
| PESO | 2.2133767 |
| EG | 2.1269391 |
| FLUJO2_0_8H | 2.0009500 |
| DIAS_GN | 1.7959205 |
| TABACO | 1.4615339 |
| ETIOLOGIA | 1.1743124 |
| ANALITICA | 0.9879309 |
| SUERO | 0.8663997 |
| SEXO | 0.6227099 |
| ENFERMEDAD_BASE | 0.5206316 |
| RADIOGRAFIA | 0.5204119 |
| PREMATURIDAD | 0.4381004 |
| ALERGIAS | 0.3717212 |
| ALIMENTACION | 0.3662804 |
| GN_INGRESO | 0.1921475 |
| DERMATITIS | 0.1474324 |
| SNG | 0.1265385 |
| PALIVIZUMAB | 0.1092629 |
| PAUSAS_APNEA | 0.0736971 |
| DIAS_OAF | 0.0481116 |
| DETERIORO | 0.0418404 |
| OAF_TRAS_INGRESO | 0.0337593 |
| UCIP | 0.0228858 |
| OAF | 0.0210478 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_cuantiles)
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_cuantiles)
plotting_PER <- cbind(plot_data_PER, cluster_data_PER)
head(plotting_PER)## X1 X2 X3 X4 X5
## ACR_11231843 0.007372827 0.00257892 0.007552868 0.001244157 0.020794567
## ADAO_11159808 1.007935650 0.56734822 0.509011178 0.711561876 0.152613273
## AGG_11236448 2.380687539 0.39362122 0.182763413 0.324556130 0.255300891
## AHL_11239959 0.858419217 0.76083264 0.292720772 1.277966489 0.677624620
## AJGD_11119689 1.704367449 1.88947483 0.167634848 1.147035657 0.159517736
## AMP_11228639 3.032791148 0.84461522 0.954845276 0.287926093 0.004228085
## X6 X7 X8 X9 X10
## ACR_11231843 0.02046290 0.024877932 0.006955217 0.00852905 0.005777708
## ADAO_11159808 0.01857926 0.289120346 0.083830140 0.27147799 0.261912313
## AGG_11236448 0.12294867 0.245528755 0.190021222 0.54714897 0.357936063
## AHL_11239959 0.67498060 0.152061973 1.110688407 0.60742582 0.214555627
## AJGD_11119689 0.56597730 0.004861582 0.299313431 0.28474152 0.519772097
## AMP_11228639 0.09564877 0.058631508 0.006046857 0.01212086 0.001940349
## X11 X12 X13 X14 X15
## ACR_11231843 0.00361939 0.00435191 0.01065348 0.01471756 0.0007872641
## ADAO_11159808 0.21446360 0.10918664 0.02806076 0.20192584 0.2692548988
## AGG_11236448 0.85828890 0.06305132 0.29559245 0.06432647 0.0174924957
## AHL_11239959 0.08376783 0.64521099 0.22581482 0.08081757 0.6463845767
## AJGD_11119689 0.08121858 0.77620410 0.19579371 0.42442571 0.0894979525
## AMP_11228639 0.02175067 0.01076778 0.03683358 0.12466185 0.0214858725
## X16 X17 X18 X19 X20
## ACR_11231843 0.01076351 0.0003489152 0.008049351 0.002809187 0.004623149
## ADAO_11159808 0.13938618 0.2108637638 0.290507155 0.003438341 0.019503533
## AGG_11236448 0.30601982 0.0406872503 0.022436947 0.049450815 0.003606647
## AHL_11239959 0.04172188 0.0280735562 0.012784406 0.240191883 0.195912162
## AJGD_11119689 0.12438134 0.5053023159 0.352205824 0.144637524 0.010357443
## AMP_11228639 0.02376122 0.0078033808 0.032811310 0.021214619 0.061530402
## X21 X22 X23 X24 X25
## ACR_11231843 0.005164633 0.004330519 0.0022736838 0.0007047791 0.004851931
## ADAO_11159808 0.027427498 0.188078718 0.1789245805 0.0240478893 0.058047323
## AGG_11236448 0.237265370 0.040406625 0.0095648292 0.1388621834 0.020921329
## AHL_11239959 0.036026329 0.036877536 0.2543045150 0.4392662038 0.021776880
## AJGD_11119689 0.831887336 0.313210727 0.0668612025 0.5372328791 0.065391074
## AMP_11228639 0.007062599 0.093557787 0.0006829918 0.0105028378 0.008146914
## X26 X27 X28 X29 X30
## ACR_11231843 0.002144835 0.008373432 0.0002735066 0.0007708365 0.0008905531
## ADAO_11159808 0.161868011 0.090045009 0.0922563225 0.0651277179 0.1124013509
## AGG_11236448 0.170018443 0.208350085 0.0933683716 0.2468314324 0.0336384608
## AHL_11239959 0.099153264 0.004618835 0.0924651481 0.0301394053 0.0317340710
## AJGD_11119689 0.054890780 0.020306019 0.1012233067 0.1042789768 0.1574517184
## AMP_11228639 0.048240748 0.029180881 0.0190465607 0.0065503979 0.0522698989
## X31 X32 X33 X34 X35
## ACR_11231843 0.002032371 0.002317647 0.0016958597 0.001403045 0.0011982095
## ADAO_11159808 0.022024583 0.006397448 0.0703278891 0.023328410 0.0004846579
## AGG_11236448 0.013009456 0.396338337 0.0348680280 0.114694840 0.0955105787
## AHL_11239959 0.022523293 0.087238858 0.0008727816 0.070438693 0.0611351216
## AJGD_11119689 0.144292622 0.058033610 0.0617754210 0.179342003 0.0909365443
## AMP_11228639 0.035755652 0.049410170 0.1430888749 0.005412508 0.0424986167
## X36 X37 X38 X39 X40
## ACR_11231843 5.750899e-05 0.002079558 0.006312121 0.0006429736 0.002606208
## ADAO_11159808 1.063848e-01 0.127984297 0.081142783 0.0700151157 0.025565200
## AGG_11236448 1.471583e-01 0.255588052 0.046404691 0.0513010272 0.441123074
## AHL_11239959 2.481010e-01 0.004491484 0.064862935 0.1588556211 0.041632279
## AJGD_11119689 1.448960e-02 0.047356598 0.256559916 0.1218822084 0.038561113
## AMP_11228639 2.655492e-03 0.006847402 0.073235289 0.0335647431 0.022256135
## X41 X42 X43 X44 X45
## ACR_11231843 0.005788565 0.002102477 0.007890362 0.0001697639 0.003025530
## ADAO_11159808 0.008170481 0.004617634 0.034721995 0.0531080142 0.024514705
## AGG_11236448 0.025690873 0.027002880 0.316329631 0.0705171116 0.006359791
## AHL_11239959 0.048581902 0.077166941 0.109731714 0.0572732473 0.051898075
## AJGD_11119689 0.001543516 0.052186141 0.328208208 0.2504366359 0.038272278
## AMP_11228639 0.007867459 0.003691957 0.043585810 0.0286199404 0.020216874
## X46 X47 X48 X49 X50
## ACR_11231843 0.0033589622 0.0007003468 0.008267341 0.006361191 0.001096650
## ADAO_11159808 0.0003047917 0.0007115626 0.016639007 0.041382721 0.084623541
## AGG_11236448 0.1416208857 0.0081803384 0.101737710 0.035920671 0.001891900
## AHL_11239959 0.0156443837 0.0346447442 0.005004847 0.028493041 0.001818934
## AJGD_11119689 0.0567372955 0.0445087993 0.019482179 0.018269030 0.096374168
## AMP_11228639 0.0307668071 0.0124602401 0.004365088 0.001924741 0.006483497
## X51 X52 X53 X54 X55
## ACR_11231843 0.001599357 0.004085669 0.001554687 0.0062261567 0.002612204
## ADAO_11159808 0.036127719 0.012062281 0.002093560 0.0001519172 0.003455975
## AGG_11236448 0.029584171 0.023173475 0.037571953 0.0251882617 0.035620593
## AHL_11239959 0.003992290 0.006762357 0.022570057 0.0370565371 0.055114583
## AJGD_11119689 0.085268884 0.119952464 0.095525818 0.0432442829 0.004873117
## AMP_11228639 0.010832400 0.014569236 0.006101093 0.0066324566 0.004557694
## X56 X57 X58 X59 X60
## ACR_11231843 0.0029236565 5.853528e-03 0.0008167224 0.006193239 0.004250135
## ADAO_11159808 0.0008043863 1.576491e-05 0.0391016191 0.009013988 0.007809487
## AGG_11236448 0.1273769527 6.013307e-02 0.0643214016 0.002914453 0.008713679
## AHL_11239959 0.0016425975 6.716833e-02 0.0056750006 0.015766131 0.015340865
## AJGD_11119689 0.1144010217 2.025339e-02 0.1237645838 0.124102715 0.084806695
## AMP_11228639 0.0069791233 1.478991e-02 0.0140747641 0.011746632 0.003877042
## X61 X62 X63 X64 X65
## ACR_11231843 0.002651661 0.010910868 0.001616238 0.003420722 0.002515699
## ADAO_11159808 0.045383793 0.001874113 0.019276946 0.044470679 0.005277178
## AGG_11236448 0.065578386 0.003594029 0.005420103 0.014317012 0.066122365
## AHL_11239959 0.009329212 0.023590614 0.003999922 0.017104330 0.027153837
## AJGD_11119689 0.099636007 0.039945835 0.016521609 0.015431967 0.234383222
## AMP_11228639 0.028274740 0.011954563 0.001584067 0.044551011 0.049355480
## X66 X67 X68 X69 X70
## ACR_11231843 0.0009039589 0.003063311 0.003030873 0.001756251 0.0038989912
## ADAO_11159808 0.0043859102 0.034304900 0.005568489 0.002633567 0.0004644676
## AGG_11236448 0.0194265070 0.036824621 0.002515050 0.053896005 0.0272205325
## AHL_11239959 0.0716518247 0.036962582 0.094575011 0.083662308 0.0015803465
## AJGD_11119689 0.0701006321 0.286845291 0.043471702 0.021095290 0.0926341072
## AMP_11228639 0.0106956760 0.042416791 0.026442545 0.028863922 0.0351532573
## X71 X72 X73 X74 X75
## ACR_11231843 0.002395468 0.0008122179 9.540197e-03 0.002506233 0.00358983
## ADAO_11159808 0.003057918 0.0338378027 1.728320e-02 0.006021544 0.03078244
## AGG_11236448 0.002363956 0.0289872911 1.778069e-02 0.008147051 0.06254596
## AHL_11239959 0.019141441 0.0336587861 6.760815e-02 0.001904680 0.02728088
## AJGD_11119689 0.265119950 0.2075143863 2.285859e-02 0.073221775 0.05640236
## AMP_11228639 0.037213706 0.0247929825 2.625292e-05 0.002655831 0.03184893
## X76 X77 X78 X79 X80
## ACR_11231843 0.004299736 0.0002992080 0.005857324 0.007158136 0.001309858
## ADAO_11159808 0.002051022 0.0004341849 0.010746467 0.009371412 0.015246671
## AGG_11236448 0.007165748 0.0085344047 0.010118452 0.060762956 0.039074871
## AHL_11239959 0.023700272 0.0015598610 0.017390038 0.014537620 0.008198007
## AJGD_11119689 0.058302003 0.0629066762 0.061858529 0.010085570 0.004437876
## AMP_11228639 0.008451500 0.0091357923 0.026687995 0.003048407 0.004590435
## X81 X82 X83 X84 X85
## ACR_11231843 0.006356601 0.0004187628 0.004337170 0.004832361 0.005267023
## ADAO_11159808 0.022354364 0.0053927175 0.008236551 0.005957846 0.035160008
## AGG_11236448 0.028316978 0.0021685459 0.049872617 0.001946517 0.017398491
## AHL_11239959 0.035935927 0.0903719734 0.009380955 0.030338355 0.085156576
## AJGD_11119689 0.070188406 0.1149524177 0.068734097 0.129745412 0.009653725
## AMP_11228639 0.004500632 0.0075628477 0.003249839 0.022403960 0.031478504
## X86 X87 X88 X89 X90
## ACR_11231843 0.003410390 0.001809137 0.001069952 0.0017492336 0.003928266
## ADAO_11159808 0.039673212 0.043270208 0.002897255 0.0165603937 0.003792764
## AGG_11236448 0.056016522 0.002202799 0.027957899 0.0367266751 0.008695934
## AHL_11239959 0.047952959 0.033491404 0.012702894 0.0001972801 0.009670017
## AJGD_11119689 0.037672868 0.006023682 0.103721458 0.0780525664 0.041903822
## AMP_11228639 0.002703966 0.005146231 0.002314646 0.0186744152 0.019734296
## X91 X92 X93 X94 X95
## ACR_11231843 0.001694852 0.003187604 0.002454322 0.000364638 0.003783277
## ADAO_11159808 0.006888415 0.055685426 0.003472036 0.032121020 0.016832547
## AGG_11236448 0.013255778 0.005192139 0.006952358 0.017348862 0.038894720
## AHL_11239959 0.026499130 0.022496787 0.055854269 0.046954475 0.005543831
## AJGD_11119689 0.081385361 0.078940873 0.062760068 0.162238969 0.040715666
## AMP_11228639 0.031642931 0.032744706 0.002342143 0.007432832 0.006110115
## X96 X97 X98 X99 X100
## ACR_11231843 0.001335465 0.0046786558 0.003503886 0.0002060436 0.005161912
## ADAO_11159808 0.002876496 0.0003010287 0.010583644 0.0045792152 0.014628293
## AGG_11236448 0.030438591 0.0559000240 0.008505523 0.0170654900 0.040314073
## AHL_11239959 0.071481852 0.0184163701 0.081021481 0.0148453348 0.010648406
## AJGD_11119689 0.026152737 0.0862358458 0.005516163 0.0767369377 0.002698358
## AMP_11228639 0.012782289 0.0329449212 0.021224332 0.0076970259 0.027708890
## X101 X102 X103 X104 X105
## ACR_11231843 6.037923e-05 0.006914379 0.002666583 0.003519849 0.002355671
## ADAO_11159808 1.110574e-02 0.004439753 0.005664906 0.013966253 0.005963971
## AGG_11236448 1.096872e-01 0.036445572 0.006798097 0.025350519 0.015576249
## AHL_11239959 3.693500e-03 0.032989021 0.004987038 0.019007929 0.007781697
## AJGD_11119689 7.510475e-02 0.152652934 0.094053150 0.038574164 0.111431622
## AMP_11228639 1.458704e-02 0.013307193 0.005716929 0.009836704 0.015710329
## X106 X107 X108 X109 X110
## ACR_11231843 0.0020356429 0.002463969 0.0074877481 0.001019650 0.001286978
## ADAO_11159808 0.0206215366 0.029187967 0.0008514953 0.009641363 0.058081273
## AGG_11236448 0.0180310895 0.015730988 0.0222714636 0.043649470 0.012432768
## AHL_11239959 0.0002570743 0.008511673 0.0254773171 0.003715998 0.001116029
## AJGD_11119689 0.0091147194 0.006570532 0.1253431873 0.075085080 0.069665739
## AMP_11228639 0.0098276367 0.002578411 0.0026826513 0.022808013 0.016483776
## X111 X112 X113 X114 X115
## ACR_11231843 0.003885371 0.001969166 0.003852631 0.004010098 0.0002635215
## ADAO_11159808 0.001823264 0.001524924 0.004364322 0.001985458 0.0117415031
## AGG_11236448 0.023957009 0.002032810 0.012127867 0.012549329 0.0108082001
## AHL_11239959 0.003339720 0.017975773 0.069130370 0.110162806 0.0216373850
## AJGD_11119689 0.035666992 0.011340165 0.149919871 0.004078126 0.1530357733
## AMP_11228639 0.002194981 0.000840148 0.059254492 0.018531260 0.0088048719
## X116 X117 X118 X119 X120
## ACR_11231843 0.004890609 0.0009482344 0.001947810 0.004098602 0.0001479797
## ADAO_11159808 0.005464265 0.0049375102 0.006194246 0.012254741 0.0001599416
## AGG_11236448 0.015290372 0.0138877144 0.002644078 0.021793649 0.0079279084
## AHL_11239959 0.010396478 0.0006460461 0.015821643 0.049768298 0.0346859892
## AJGD_11119689 0.090702020 0.1045917508 0.029625094 0.048202785 0.0617486598
## AMP_11228639 0.016255561 0.0392805684 0.000114624 0.001445594 0.0102398801
## X121 X122 X123 X124 X125
## ACR_11231843 0.0060255859 0.006734871 0.0004745433 0.004380674 0.0012151951
## ADAO_11159808 0.0048772927 0.004911499 0.0150876618 0.016673799 0.0381429395
## AGG_11236448 0.0059132436 0.002409300 0.0008640730 0.016299762 0.0003490109
## AHL_11239959 0.0198951611 0.040296386 0.0075726409 0.035271866 0.0155654821
## AJGD_11119689 0.0425943548 0.012782149 0.0068919722 0.021642983 0.0472674649
## AMP_11228639 0.0009313942 0.007904873 0.0383047482 0.009660896 0.0058499205
## X126 X127 X128 X129 X130
## ACR_11231843 0.0009604692 0.005727827 0.0028767376 0.002220999 0.001709068
## ADAO_11159808 0.0195669205 0.003361383 0.0093018276 0.003318064 0.012014837
## AGG_11236448 0.0015930879 0.003392550 0.0003939956 0.023939065 0.009925056
## AHL_11239959 0.0254271587 0.078917825 0.0400517596 0.016762284 0.013707726
## AJGD_11119689 0.0276650887 0.014867056 0.0890894879 0.032339263 0.001728490
## AMP_11228639 0.0071384168 0.006751390 0.0048473217 0.009463716 0.012304021
## X131 X132 X133 X134 X135
## ACR_11231843 0.001598033 0.004384187 0.0046078676 0.0006473851 0.004004066
## ADAO_11159808 0.007913402 0.020084813 0.0096674215 0.0740894223 0.020697270
## AGG_11236448 0.021746969 0.002871925 0.0002796431 0.0125013608 0.006924342
## AHL_11239959 0.007586180 0.031688417 0.1510475912 0.0770788733 0.012740620
## AJGD_11119689 0.056448938 0.044503098 0.0202317116 0.0238556639 0.029180560
## AMP_11228639 0.007035311 0.012695566 0.0025718235 0.0247545880 0.050474368
## X136 X137 X138 X139 X140
## ACR_11231843 0.001460118 0.001985934 0.0049359951 0.000445727 0.002246056
## ADAO_11159808 0.027198499 0.018825547 0.0017854781 0.003780256 0.008889118
## AGG_11236448 0.007221970 0.026483477 0.0003334471 0.045886596 0.006148049
## AHL_11239959 0.001026920 0.085446263 0.0079458866 0.040514084 0.002561656
## AJGD_11119689 0.042220673 0.066663444 0.0947274340 0.045115969 0.001201257
## AMP_11228639 0.005438497 0.015352743 0.0354741025 0.029045288 0.001831793
## X141 X142 X143 X144 X145
## ACR_11231843 0.001284664 0.0018780730 0.003457497 0.0017815058 0.002766978
## ADAO_11159808 0.004843851 0.0001653445 0.012158313 0.0256914015 0.001542689
## AGG_11236448 0.014116530 0.0647134694 0.051030812 0.0005156368 0.006581315
## AHL_11239959 0.017668493 0.0265028033 0.002592314 0.0228965760 0.020417454
## AJGD_11119689 0.014650768 0.2145563346 0.065345577 0.0751601260 0.028721331
## AMP_11228639 0.012222389 0.0018829781 0.012819968 0.0041239310 0.012761213
## X146 X147 X148 X149 X150
## ACR_11231843 0.002354210 0.001561437 0.002757258 0.002159404 0.001457741
## ADAO_11159808 0.008312850 0.002723231 0.008342768 0.012810271 0.008019787
## AGG_11236448 0.006938600 0.044095657 0.029325799 0.067988476 0.015318743
## AHL_11239959 0.004469129 0.032634469 0.041537663 0.029264165 0.106784599
## AJGD_11119689 0.044765470 0.039006865 0.043600113 0.006072279 0.099345888
## AMP_11228639 0.012020039 0.044838760 0.016297887 0.002426662 0.002276423
## X151 X152 X153 X154 X155
## ACR_11231843 0.003142110 0.005358183 0.0008943519 0.0053795081 0.00132100
## ADAO_11159808 0.022208258 0.001122608 0.0198403034 0.0173150262 0.02440688
## AGG_11236448 0.009916432 0.028126644 0.0102557463 0.0001950864 0.01474133
## AHL_11239959 0.010160859 0.009960969 0.0227869428 0.0155654984 0.02449055
## AJGD_11119689 0.079767795 0.017398940 0.0199185381 0.0087850731 0.13000078
## AMP_11228639 0.004943980 0.010441542 0.0122252081 0.0255304122 0.02743810
## X156 X157 X158 X159 X160
## ACR_11231843 0.004196003 0.005052518 0.001001973 0.004389698 0.0038334117
## ADAO_11159808 0.001599489 0.008940554 0.007895663 0.004946808 0.0001332835
## AGG_11236448 0.021202344 0.010448636 0.022530581 0.001741918 0.0015605163
## AHL_11239959 0.021315728 0.008032114 0.040886533 0.007960652 0.0066909538
## AJGD_11119689 0.098115645 0.119126299 0.188776310 0.004917380 0.1176489568
## AMP_11228639 0.003708940 0.015304572 0.014752058 0.014406330 0.0027626485
## X161 X162 X163 X164 X165
## ACR_11231843 0.0002952432 0.004768847 0.001224221 0.0068927917 0.005624194
## ADAO_11159808 0.0165693463 0.013569218 0.008800430 0.0197832450 0.018056950
## AGG_11236448 0.0103056438 0.018677238 0.010850168 0.0179976895 0.019068281
## AHL_11239959 0.0026379310 0.014454532 0.024134852 0.0290149515 0.009289577
## AJGD_11119689 0.0380581150 0.116335169 0.082150051 0.0096393933 0.009606193
## AMP_11228639 0.0166000399 0.008329422 0.010294196 0.0006590615 0.027891199
## X166 X167 X168 X169 X170
## ACR_11231843 0.001974349 0.003623647 0.003107213 0.0014393661 0.0036844111
## ADAO_11159808 0.027491019 0.007595157 0.015175806 0.0038598000 0.0002076333
## AGG_11236448 0.004422378 0.008873990 0.010825079 0.0005208885 0.0004039750
## AHL_11239959 0.001148526 0.011042458 0.017779335 0.0474332653 0.0068163912
## AJGD_11119689 0.062201542 0.049070762 0.068686369 0.1799354475 0.0225691651
## AMP_11228639 0.011516010 0.025445083 0.022189273 0.0074401981 0.0246708080
## X171 X172 X173 X174 X175
## ACR_11231843 0.0009230126 0.003932500 0.004989508 0.002792348 0.002125151
## ADAO_11159808 0.0036080682 0.005305688 0.008175068 0.016008449 0.033323443
## AGG_11236448 0.0015998478 0.004634592 0.017889973 0.001792301 0.008552444
## AHL_11239959 0.0358267535 0.011143965 0.007233111 0.002212653 0.021987645
## AJGD_11119689 0.0497977292 0.059043601 0.026011510 0.142753897 0.044714370
## AMP_11228639 0.0324164918 0.007812833 0.005009228 0.058143613 0.045523433
## X176 X177 X178 X179 X180
## ACR_11231843 0.0014134924 0.002606901 0.0061348171 0.002534046 0.001527951
## ADAO_11159808 0.0001358812 0.015095114 0.0062381736 0.001875013 0.003922788
## AGG_11236448 0.0019213181 0.013914328 0.0008647023 0.003699604 0.010210024
## AHL_11239959 0.0034717910 0.013281290 0.0127965607 0.017040692 0.005776028
## AJGD_11119689 0.0857140836 0.082088177 0.0424761268 0.012070854 0.068183526
## AMP_11228639 0.0033134470 0.001166984 0.0070391417 0.006086372 0.005196420
## X181 X182 X183 X184 X185
## ACR_11231843 0.001956135 0.0001824963 0.006114042 0.001628895 0.002299060
## ADAO_11159808 0.017725434 0.0332986725 0.019359601 0.016026893 0.010209181
## AGG_11236448 0.021633657 0.0134838636 0.010732386 0.002635290 0.009419816
## AHL_11239959 0.009538895 0.0137761840 0.037468662 0.011361573 0.001937010
## AJGD_11119689 0.025580423 0.0341452621 0.028978066 0.107596794 0.099988858
## AMP_11228639 0.004539961 0.0040345774 0.013231915 0.015428982 0.005955688
## X186 X187 X188 X189 X190
## ACR_11231843 0.001959534 0.002895671 0.002034898 0.0034631728 0.002512409
## ADAO_11159808 0.006444412 0.002147021 0.015923457 0.0253095375 0.010124052
## AGG_11236448 0.033007899 0.014262566 0.002752943 0.0063445744 0.005400672
## AHL_11239959 0.014878974 0.031473715 0.015774219 0.0038368385 0.021070051
## AJGD_11119689 0.011298382 0.084669380 0.036206415 0.0001013254 0.041378800
## AMP_11228639 0.008107092 0.004164672 0.014822942 0.0059739986 0.002861308
## X191 X192 X193 X194 X195
## ACR_11231843 0.002139917 0.0026408516 0.001634892 0.001624848 0.005096672
## ADAO_11159808 0.012346128 0.0020638602 0.004602150 0.009813869 0.005905533
## AGG_11236448 0.012070029 0.0076454850 0.031188190 0.001264647 0.004462875
## AHL_11239959 0.006314724 0.0004838708 0.010997741 0.022556810 0.026950815
## AJGD_11119689 0.095775141 0.0098114106 0.119020878 0.044389731 0.309550487
## AMP_11228639 0.033386155 0.0140436595 0.012662284 0.014224790 0.014325051
## X196 X197 X198 X199 X200
## ACR_11231843 0.001600055 0.004648909 0.0006950449 0.001815009 0.0015097296
## ADAO_11159808 0.002033916 0.014805867 0.0024736123 0.001887653 0.0007154884
## AGG_11236448 0.028966759 0.013782612 0.0033169749 0.002698040 0.0072043440
## AHL_11239959 0.092662759 0.013159547 0.0239483768 0.003870477 0.0025498846
## AJGD_11119689 0.018637848 0.033257713 0.0454344500 0.019045720 0.0101626511
## AMP_11228639 0.001600465 0.019138606 0.0225169957 0.005897850 0.0029549110
## X201 X202 X203 X204 X205
## ACR_11231843 0.002915231 0.004496119 0.0006821579 0.001638866 0.0013166010
## ADAO_11159808 0.006758415 0.010558720 0.0197781308 0.020866383 0.0017031500
## AGG_11236448 0.006553657 0.037836293 0.0019381164 0.002492667 0.0019611384
## AHL_11239959 0.009493808 0.014659568 0.0245564670 0.008468273 0.0068285979
## AJGD_11119689 0.162925836 0.015494936 0.0745746727 0.033645347 0.0315858486
## AMP_11228639 0.002623044 0.001395195 0.0199687908 0.010958935 0.0000414788
## X206 X207 X208 X209 X210
## ACR_11231843 0.001705833 0.002155267 0.002938489 0.001563434 0.0016913359
## ADAO_11159808 0.014284512 0.004356601 0.001580822 0.013794678 0.0122416229
## AGG_11236448 0.005165989 0.001330134 0.029078111 0.017015390 0.0005169822
## AHL_11239959 0.039554307 0.036051450 0.001447832 0.041540078 0.0507737323
## AJGD_11119689 0.174121843 0.006420175 0.011512953 0.262637010 0.0495231170
## AMP_11228639 0.010555567 0.009126864 0.027717068 0.001513354 0.0117047959
## X211 X212 X213 X214 X215
## ACR_11231843 0.0011454122 0.002531867 0.001001203 0.001199534 0.001012105
## ADAO_11159808 0.0044044822 0.004567701 0.014283622 0.002825495 0.017195116
## AGG_11236448 0.0003730042 0.002036017 0.010083894 0.009524501 0.007992632
## AHL_11239959 0.0134972760 0.042595994 0.016019659 0.041293063 0.008845300
## AJGD_11119689 0.0230599369 0.022160424 0.055753435 0.036653655 0.077744872
## AMP_11228639 0.0265067845 0.007537867 0.000134721 0.007991157 0.006805094
## X216 X217 X218 X219 X220
## ACR_11231843 0.002901409 0.0012790861 0.002126382 0.0004990376 0.001286343
## ADAO_11159808 0.007301579 0.0006129838 0.013621005 0.0021834399 0.012427842
## AGG_11236448 0.008663268 0.0179627806 0.012625752 0.0239270580 0.004247566
## AHL_11239959 0.012840700 0.0043720914 0.038017552 0.0409157313 0.010603025
## AJGD_11119689 0.001628251 0.0083552841 0.004008203 0.0473291121 0.014775849
## AMP_11228639 0.002647968 0.0033259286 0.002671043 0.0222067956 0.004585452
## X221 X222 X223 X224 X225
## ACR_11231843 0.001905377 0.001653424 0.001518872 0.001558562 0.0004076182
## ADAO_11159808 0.008359206 0.041643049 0.006474549 0.001995487 0.0001375381
## AGG_11236448 0.000249191 0.001951449 0.017781944 0.011314477 0.0117390360
## AHL_11239959 0.005063536 0.003908493 0.015979987 0.003436973 0.0097358458
## AJGD_11119689 0.008044921 0.021760620 0.133924727 0.042301321 0.0261266792
## AMP_11228639 0.001855153 0.019776755 0.004444629 0.007786362 0.0096147812
## X226 X227 X228 X229 X230
## ACR_11231843 0.001079410 0.002042499 0.002444948 0.0023746838 0.002927610
## ADAO_11159808 0.010970877 0.025893293 0.003932126 0.0096733529 0.007607704
## AGG_11236448 0.015504724 0.008840224 0.012087977 0.0020367127 0.037346086
## AHL_11239959 0.017839258 0.031484905 0.006704746 0.0355160008 0.023771098
## AJGD_11119689 0.196134356 0.075386856 0.133282431 0.0533518708 0.056635072
## AMP_11228639 0.003124835 0.004864384 0.030126939 0.0002340561 0.012994589
## X231 X232 X233 X234 X235
## ACR_11231843 0.0011711000 0.001497628 0.002045806 0.0007346903 0.002849697
## ADAO_11159808 0.0115935515 0.004392633 0.008641028 0.0239998890 0.002308332
## AGG_11236448 0.0274383832 0.054922023 0.001047180 0.0159150738 0.012660231
## AHL_11239959 0.0032921410 0.003425980 0.006887291 0.0427070898 0.029683989
## AJGD_11119689 0.0008970865 0.164458097 0.065914014 0.0796018979 0.191574730
## AMP_11228639 0.0100510939 0.005124847 0.023849634 0.0005273842 0.004260781
## X236 X237 X238 X239 X240
## ACR_11231843 0.0009914444 0.001012983 0.001159732 0.003685346 3.353014e-03
## ADAO_11159808 0.0049430199 0.010582599 0.026189445 0.029035926 3.178474e-04
## AGG_11236448 0.0421503513 0.001919151 0.015872615 0.002443488 2.592733e-03
## AHL_11239959 0.0057535798 0.010793166 0.023248996 0.013417448 8.079576e-02
## AJGD_11119689 0.1305254752 0.007122585 0.001766798 0.077342542 6.035286e-05
## AMP_11228639 0.0129695143 0.012579008 0.004176968 0.014971229 3.595856e-03
## X241 X242 X243 X244 X245
## ACR_11231843 0.007372827 0.00257892 0.007552868 0.001244157 0.020794567
## ADAO_11159808 1.007935650 0.56734822 0.509011178 0.711561876 0.152613273
## AGG_11236448 2.380687539 0.39362122 0.182763413 0.324556130 0.255300891
## AHL_11239959 0.858419217 0.76083264 0.292720772 1.277966489 0.677624620
## AJGD_11119689 1.704367449 1.88947483 0.167634848 1.147035657 0.159517736
## AMP_11228639 3.032791148 0.84461522 0.954845276 0.287926093 0.004228085
## X246 X247 X248 X249 X250
## ACR_11231843 0.02046290 0.024877932 0.006955217 0.00852905 0.005777708
## ADAO_11159808 0.01857926 0.289120346 0.083830140 0.27147799 0.261912313
## AGG_11236448 0.12294867 0.245528755 0.190021222 0.54714897 0.357936063
## AHL_11239959 0.67498060 0.152061973 1.110688407 0.60742582 0.214555627
## AJGD_11119689 0.56597730 0.004861582 0.299313431 0.28474152 0.519772097
## AMP_11228639 0.09564877 0.058631508 0.006046857 0.01212086 0.001940349
## X251 X252 X253 X254 X255
## ACR_11231843 0.00361939 0.00435191 0.01065348 0.01471756 0.0007872641
## ADAO_11159808 0.21446360 0.10918664 0.02806076 0.20192584 0.2692548988
## AGG_11236448 0.85828890 0.06305132 0.29559245 0.06432647 0.0174924957
## AHL_11239959 0.08376783 0.64521099 0.22581482 0.08081757 0.6463845767
## AJGD_11119689 0.08121858 0.77620410 0.19579371 0.42442571 0.0894979525
## AMP_11228639 0.02175067 0.01076778 0.03683358 0.12466185 0.0214858725
## X256 X257 X258 X259 X260
## ACR_11231843 0.01076351 0.0003489152 0.008049351 0.002809187 0.004623149
## ADAO_11159808 0.13938618 0.2108637638 0.290507155 0.003438341 0.019503533
## AGG_11236448 0.30601982 0.0406872503 0.022436947 0.049450815 0.003606647
## AHL_11239959 0.04172188 0.0280735562 0.012784406 0.240191883 0.195912162
## AJGD_11119689 0.12438134 0.5053023159 0.352205824 0.144637524 0.010357443
## AMP_11228639 0.02376122 0.0078033808 0.032811310 0.021214619 0.061530402
## X261 X262 X263 X264 X265
## ACR_11231843 0.005164633 0.004330519 0.0022736838 0.0007047791 0.004851931
## ADAO_11159808 0.027427498 0.188078718 0.1789245805 0.0240478893 0.058047323
## AGG_11236448 0.237265370 0.040406625 0.0095648292 0.1388621834 0.020921329
## AHL_11239959 0.036026329 0.036877536 0.2543045150 0.4392662038 0.021776880
## AJGD_11119689 0.831887336 0.313210727 0.0668612025 0.5372328791 0.065391074
## AMP_11228639 0.007062599 0.093557787 0.0006829918 0.0105028378 0.008146914
## X266 X267 X268 X269 X270
## ACR_11231843 0.002144835 0.008373432 0.0002735066 0.0007708365 0.0008905531
## ADAO_11159808 0.161868011 0.090045009 0.0922563225 0.0651277179 0.1124013509
## AGG_11236448 0.170018443 0.208350085 0.0933683716 0.2468314324 0.0336384608
## AHL_11239959 0.099153264 0.004618835 0.0924651481 0.0301394053 0.0317340710
## AJGD_11119689 0.054890780 0.020306019 0.1012233067 0.1042789768 0.1574517184
## AMP_11228639 0.048240748 0.029180881 0.0190465607 0.0065503979 0.0522698989
## X271 X272 X273 X274 X275
## ACR_11231843 0.002032371 0.002317647 0.0016958597 0.001403045 0.0011982095
## ADAO_11159808 0.022024583 0.006397448 0.0703278891 0.023328410 0.0004846579
## AGG_11236448 0.013009456 0.396338337 0.0348680280 0.114694840 0.0955105787
## AHL_11239959 0.022523293 0.087238858 0.0008727816 0.070438693 0.0611351216
## AJGD_11119689 0.144292622 0.058033610 0.0617754210 0.179342003 0.0909365443
## AMP_11228639 0.035755652 0.049410170 0.1430888749 0.005412508 0.0424986167
## X276 X277 X278 X279 X280
## ACR_11231843 5.750899e-05 0.002079558 0.006312121 0.0006429736 0.002606208
## ADAO_11159808 1.063848e-01 0.127984297 0.081142783 0.0700151157 0.025565200
## AGG_11236448 1.471583e-01 0.255588052 0.046404691 0.0513010272 0.441123074
## AHL_11239959 2.481010e-01 0.004491484 0.064862935 0.1588556211 0.041632279
## AJGD_11119689 1.448960e-02 0.047356598 0.256559916 0.1218822084 0.038561113
## AMP_11228639 2.655492e-03 0.006847402 0.073235289 0.0335647431 0.022256135
## X281 X282 X283 X284 X285
## ACR_11231843 0.005788565 0.002102477 0.007890362 0.0001697639 0.003025530
## ADAO_11159808 0.008170481 0.004617634 0.034721995 0.0531080142 0.024514705
## AGG_11236448 0.025690873 0.027002880 0.316329631 0.0705171116 0.006359791
## AHL_11239959 0.048581902 0.077166941 0.109731714 0.0572732473 0.051898075
## AJGD_11119689 0.001543516 0.052186141 0.328208208 0.2504366359 0.038272278
## AMP_11228639 0.007867459 0.003691957 0.043585810 0.0286199404 0.020216874
## X286 X287 X288 X289 X290
## ACR_11231843 0.0033589622 0.0007003468 0.008267341 0.006361191 0.001096650
## ADAO_11159808 0.0003047917 0.0007115626 0.016639007 0.041382721 0.084623541
## AGG_11236448 0.1416208857 0.0081803384 0.101737710 0.035920671 0.001891900
## AHL_11239959 0.0156443837 0.0346447442 0.005004847 0.028493041 0.001818934
## AJGD_11119689 0.0567372955 0.0445087993 0.019482179 0.018269030 0.096374168
## AMP_11228639 0.0307668071 0.0124602401 0.004365088 0.001924741 0.006483497
## X291 X292 X293 X294 X295
## ACR_11231843 0.001599357 0.004085669 0.001554687 0.0062261567 0.002612204
## ADAO_11159808 0.036127719 0.012062281 0.002093560 0.0001519172 0.003455975
## AGG_11236448 0.029584171 0.023173475 0.037571953 0.0251882617 0.035620593
## AHL_11239959 0.003992290 0.006762357 0.022570057 0.0370565371 0.055114583
## AJGD_11119689 0.085268884 0.119952464 0.095525818 0.0432442829 0.004873117
## AMP_11228639 0.010832400 0.014569236 0.006101093 0.0066324566 0.004557694
## X296 X297 X298 X299 X300
## ACR_11231843 0.0029236565 5.853528e-03 0.0008167224 0.006193239 0.004250135
## ADAO_11159808 0.0008043863 1.576491e-05 0.0391016191 0.009013988 0.007809487
## AGG_11236448 0.1273769527 6.013307e-02 0.0643214016 0.002914453 0.008713679
## AHL_11239959 0.0016425975 6.716833e-02 0.0056750006 0.015766131 0.015340865
## AJGD_11119689 0.1144010217 2.025339e-02 0.1237645838 0.124102715 0.084806695
## AMP_11228639 0.0069791233 1.478991e-02 0.0140747641 0.011746632 0.003877042
## X301 X302 X303 X304 X305
## ACR_11231843 0.002651661 0.010910868 0.001616238 0.003420722 0.002515699
## ADAO_11159808 0.045383793 0.001874113 0.019276946 0.044470679 0.005277178
## AGG_11236448 0.065578386 0.003594029 0.005420103 0.014317012 0.066122365
## AHL_11239959 0.009329212 0.023590614 0.003999922 0.017104330 0.027153837
## AJGD_11119689 0.099636007 0.039945835 0.016521609 0.015431967 0.234383222
## AMP_11228639 0.028274740 0.011954563 0.001584067 0.044551011 0.049355480
## X306 X307 X308 X309 X310
## ACR_11231843 0.0009039589 0.003063311 0.003030873 0.001756251 0.0038989912
## ADAO_11159808 0.0043859102 0.034304900 0.005568489 0.002633567 0.0004644676
## AGG_11236448 0.0194265070 0.036824621 0.002515050 0.053896005 0.0272205325
## AHL_11239959 0.0716518247 0.036962582 0.094575011 0.083662308 0.0015803465
## AJGD_11119689 0.0701006321 0.286845291 0.043471702 0.021095290 0.0926341072
## AMP_11228639 0.0106956760 0.042416791 0.026442545 0.028863922 0.0351532573
## X311 X312 X313 X314 X315
## ACR_11231843 0.002395468 0.0008122179 9.540197e-03 0.002506233 0.00358983
## ADAO_11159808 0.003057918 0.0338378027 1.728320e-02 0.006021544 0.03078244
## AGG_11236448 0.002363956 0.0289872911 1.778069e-02 0.008147051 0.06254596
## AHL_11239959 0.019141441 0.0336587861 6.760815e-02 0.001904680 0.02728088
## AJGD_11119689 0.265119950 0.2075143863 2.285859e-02 0.073221775 0.05640236
## AMP_11228639 0.037213706 0.0247929825 2.625292e-05 0.002655831 0.03184893
## X316 X317 X318 X319 X320
## ACR_11231843 0.004299736 0.0002992080 0.005857324 0.007158136 0.001309858
## ADAO_11159808 0.002051022 0.0004341849 0.010746467 0.009371412 0.015246671
## AGG_11236448 0.007165748 0.0085344047 0.010118452 0.060762956 0.039074871
## AHL_11239959 0.023700272 0.0015598610 0.017390038 0.014537620 0.008198007
## AJGD_11119689 0.058302003 0.0629066762 0.061858529 0.010085570 0.004437876
## AMP_11228639 0.008451500 0.0091357923 0.026687995 0.003048407 0.004590435
## X321 X322 X323 X324 X325
## ACR_11231843 0.006356601 0.0004187628 0.004337170 0.004832361 0.005267023
## ADAO_11159808 0.022354364 0.0053927175 0.008236551 0.005957846 0.035160008
## AGG_11236448 0.028316978 0.0021685459 0.049872617 0.001946517 0.017398491
## AHL_11239959 0.035935927 0.0903719734 0.009380955 0.030338355 0.085156576
## AJGD_11119689 0.070188406 0.1149524177 0.068734097 0.129745412 0.009653725
## AMP_11228639 0.004500632 0.0075628477 0.003249839 0.022403960 0.031478504
## X326 X327 X328 X329 X330
## ACR_11231843 0.003410390 0.001809137 0.001069952 0.0017492336 0.003928266
## ADAO_11159808 0.039673212 0.043270208 0.002897255 0.0165603937 0.003792764
## AGG_11236448 0.056016522 0.002202799 0.027957899 0.0367266751 0.008695934
## AHL_11239959 0.047952959 0.033491404 0.012702894 0.0001972801 0.009670017
## AJGD_11119689 0.037672868 0.006023682 0.103721458 0.0780525664 0.041903822
## AMP_11228639 0.002703966 0.005146231 0.002314646 0.0186744152 0.019734296
## X331 X332 X333 X334 X335
## ACR_11231843 0.001694852 0.003187604 0.002454322 0.000364638 0.003783277
## ADAO_11159808 0.006888415 0.055685426 0.003472036 0.032121020 0.016832547
## AGG_11236448 0.013255778 0.005192139 0.006952358 0.017348862 0.038894720
## AHL_11239959 0.026499130 0.022496787 0.055854269 0.046954475 0.005543831
## AJGD_11119689 0.081385361 0.078940873 0.062760068 0.162238969 0.040715666
## AMP_11228639 0.031642931 0.032744706 0.002342143 0.007432832 0.006110115
## X336 X337 X338 X339 X340
## ACR_11231843 0.001335465 0.0046786558 0.003503886 0.0002060436 0.005161912
## ADAO_11159808 0.002876496 0.0003010287 0.010583644 0.0045792152 0.014628293
## AGG_11236448 0.030438591 0.0559000240 0.008505523 0.0170654900 0.040314073
## AHL_11239959 0.071481852 0.0184163701 0.081021481 0.0148453348 0.010648406
## AJGD_11119689 0.026152737 0.0862358458 0.005516163 0.0767369377 0.002698358
## AMP_11228639 0.012782289 0.0329449212 0.021224332 0.0076970259 0.027708890
## X341 X342 X343 X344 X345
## ACR_11231843 6.037923e-05 0.006914379 0.002666583 0.003519849 0.002355671
## ADAO_11159808 1.110574e-02 0.004439753 0.005664906 0.013966253 0.005963971
## AGG_11236448 1.096872e-01 0.036445572 0.006798097 0.025350519 0.015576249
## AHL_11239959 3.693500e-03 0.032989021 0.004987038 0.019007929 0.007781697
## AJGD_11119689 7.510475e-02 0.152652934 0.094053150 0.038574164 0.111431622
## AMP_11228639 1.458704e-02 0.013307193 0.005716929 0.009836704 0.015710329
## X346 X347 X348 X349 X350
## ACR_11231843 0.0020356429 0.002463969 0.0074877481 0.001019650 0.001286978
## ADAO_11159808 0.0206215366 0.029187967 0.0008514953 0.009641363 0.058081273
## AGG_11236448 0.0180310895 0.015730988 0.0222714636 0.043649470 0.012432768
## AHL_11239959 0.0002570743 0.008511673 0.0254773171 0.003715998 0.001116029
## AJGD_11119689 0.0091147194 0.006570532 0.1253431873 0.075085080 0.069665739
## AMP_11228639 0.0098276367 0.002578411 0.0026826513 0.022808013 0.016483776
## X351 X352 X353 X354 X355
## ACR_11231843 0.003885371 0.001969166 0.003852631 0.004010098 0.0002635215
## ADAO_11159808 0.001823264 0.001524924 0.004364322 0.001985458 0.0117415031
## AGG_11236448 0.023957009 0.002032810 0.012127867 0.012549329 0.0108082001
## AHL_11239959 0.003339720 0.017975773 0.069130370 0.110162806 0.0216373850
## AJGD_11119689 0.035666992 0.011340165 0.149919871 0.004078126 0.1530357733
## AMP_11228639 0.002194981 0.000840148 0.059254492 0.018531260 0.0088048719
## X356 X357 X358 X359 X360
## ACR_11231843 0.004890609 0.0009482344 0.001947810 0.004098602 0.0001479797
## ADAO_11159808 0.005464265 0.0049375102 0.006194246 0.012254741 0.0001599416
## AGG_11236448 0.015290372 0.0138877144 0.002644078 0.021793649 0.0079279084
## AHL_11239959 0.010396478 0.0006460461 0.015821643 0.049768298 0.0346859892
## AJGD_11119689 0.090702020 0.1045917508 0.029625094 0.048202785 0.0617486598
## AMP_11228639 0.016255561 0.0392805684 0.000114624 0.001445594 0.0102398801
## X361 X362 X363 X364 X365
## ACR_11231843 0.0060255859 0.006734871 0.0004745433 0.004380674 0.0012151951
## ADAO_11159808 0.0048772927 0.004911499 0.0150876618 0.016673799 0.0381429395
## AGG_11236448 0.0059132436 0.002409300 0.0008640730 0.016299762 0.0003490109
## AHL_11239959 0.0198951611 0.040296386 0.0075726409 0.035271866 0.0155654821
## AJGD_11119689 0.0425943548 0.012782149 0.0068919722 0.021642983 0.0472674649
## AMP_11228639 0.0009313942 0.007904873 0.0383047482 0.009660896 0.0058499205
## X366 X367 X368 X369 X370
## ACR_11231843 0.0009604692 0.005727827 0.0028767376 0.002220999 0.001709068
## ADAO_11159808 0.0195669205 0.003361383 0.0093018276 0.003318064 0.012014837
## AGG_11236448 0.0015930879 0.003392550 0.0003939956 0.023939065 0.009925056
## AHL_11239959 0.0254271587 0.078917825 0.0400517596 0.016762284 0.013707726
## AJGD_11119689 0.0276650887 0.014867056 0.0890894879 0.032339263 0.001728490
## AMP_11228639 0.0071384168 0.006751390 0.0048473217 0.009463716 0.012304021
## X371 X372 X373 X374 X375
## ACR_11231843 0.001598033 0.004384187 0.0046078676 0.0006473851 0.004004066
## ADAO_11159808 0.007913402 0.020084813 0.0096674215 0.0740894223 0.020697270
## AGG_11236448 0.021746969 0.002871925 0.0002796431 0.0125013608 0.006924342
## AHL_11239959 0.007586180 0.031688417 0.1510475912 0.0770788733 0.012740620
## AJGD_11119689 0.056448938 0.044503098 0.0202317116 0.0238556639 0.029180560
## AMP_11228639 0.007035311 0.012695566 0.0025718235 0.0247545880 0.050474368
## X376 X377 X378 X379 X380
## ACR_11231843 0.001460118 0.001985934 0.0049359951 0.000445727 0.002246056
## ADAO_11159808 0.027198499 0.018825547 0.0017854781 0.003780256 0.008889118
## AGG_11236448 0.007221970 0.026483477 0.0003334471 0.045886596 0.006148049
## AHL_11239959 0.001026920 0.085446263 0.0079458866 0.040514084 0.002561656
## AJGD_11119689 0.042220673 0.066663444 0.0947274340 0.045115969 0.001201257
## AMP_11228639 0.005438497 0.015352743 0.0354741025 0.029045288 0.001831793
## X381 X382 X383 X384 X385
## ACR_11231843 0.001284664 0.0018780730 0.003457497 0.0017815058 0.002766978
## ADAO_11159808 0.004843851 0.0001653445 0.012158313 0.0256914015 0.001542689
## AGG_11236448 0.014116530 0.0647134694 0.051030812 0.0005156368 0.006581315
## AHL_11239959 0.017668493 0.0265028033 0.002592314 0.0228965760 0.020417454
## AJGD_11119689 0.014650768 0.2145563346 0.065345577 0.0751601260 0.028721331
## AMP_11228639 0.012222389 0.0018829781 0.012819968 0.0041239310 0.012761213
## X386 X387 X388 X389 X390
## ACR_11231843 0.002354210 0.001561437 0.002757258 0.002159404 0.001457741
## ADAO_11159808 0.008312850 0.002723231 0.008342768 0.012810271 0.008019787
## AGG_11236448 0.006938600 0.044095657 0.029325799 0.067988476 0.015318743
## AHL_11239959 0.004469129 0.032634469 0.041537663 0.029264165 0.106784599
## AJGD_11119689 0.044765470 0.039006865 0.043600113 0.006072279 0.099345888
## AMP_11228639 0.012020039 0.044838760 0.016297887 0.002426662 0.002276423
## X391 X392 X393 X394 X395
## ACR_11231843 0.003142110 0.005358183 0.0008943519 0.0053795081 0.00132100
## ADAO_11159808 0.022208258 0.001122608 0.0198403034 0.0173150262 0.02440688
## AGG_11236448 0.009916432 0.028126644 0.0102557463 0.0001950864 0.01474133
## AHL_11239959 0.010160859 0.009960969 0.0227869428 0.0155654984 0.02449055
## AJGD_11119689 0.079767795 0.017398940 0.0199185381 0.0087850731 0.13000078
## AMP_11228639 0.004943980 0.010441542 0.0122252081 0.0255304122 0.02743810
## X396 X397 X398 X399 X400
## ACR_11231843 0.004196003 0.005052518 0.001001973 0.004389698 0.0038334117
## ADAO_11159808 0.001599489 0.008940554 0.007895663 0.004946808 0.0001332835
## AGG_11236448 0.021202344 0.010448636 0.022530581 0.001741918 0.0015605163
## AHL_11239959 0.021315728 0.008032114 0.040886533 0.007960652 0.0066909538
## AJGD_11119689 0.098115645 0.119126299 0.188776310 0.004917380 0.1176489568
## AMP_11228639 0.003708940 0.015304572 0.014752058 0.014406330 0.0027626485
## X401 X402 X403 X404 X405
## ACR_11231843 0.0002952432 0.004768847 0.001224221 0.0068927917 0.005624194
## ADAO_11159808 0.0165693463 0.013569218 0.008800430 0.0197832450 0.018056950
## AGG_11236448 0.0103056438 0.018677238 0.010850168 0.0179976895 0.019068281
## AHL_11239959 0.0026379310 0.014454532 0.024134852 0.0290149515 0.009289577
## AJGD_11119689 0.0380581150 0.116335169 0.082150051 0.0096393933 0.009606193
## AMP_11228639 0.0166000399 0.008329422 0.010294196 0.0006590615 0.027891199
## X406 X407 X408 X409 X410
## ACR_11231843 0.001974349 0.003623647 0.003107213 0.0014393661 0.0036844111
## ADAO_11159808 0.027491019 0.007595157 0.015175806 0.0038598000 0.0002076333
## AGG_11236448 0.004422378 0.008873990 0.010825079 0.0005208885 0.0004039750
## AHL_11239959 0.001148526 0.011042458 0.017779335 0.0474332653 0.0068163912
## AJGD_11119689 0.062201542 0.049070762 0.068686369 0.1799354475 0.0225691651
## AMP_11228639 0.011516010 0.025445083 0.022189273 0.0074401981 0.0246708080
## X411 X412 X413 X414 X415
## ACR_11231843 0.0009230126 0.003932500 0.004989508 0.002792348 0.002125151
## ADAO_11159808 0.0036080682 0.005305688 0.008175068 0.016008449 0.033323443
## AGG_11236448 0.0015998478 0.004634592 0.017889973 0.001792301 0.008552444
## AHL_11239959 0.0358267535 0.011143965 0.007233111 0.002212653 0.021987645
## AJGD_11119689 0.0497977292 0.059043601 0.026011510 0.142753897 0.044714370
## AMP_11228639 0.0324164918 0.007812833 0.005009228 0.058143613 0.045523433
## X416 X417 X418 X419 X420
## ACR_11231843 0.0014134924 0.002606901 0.0061348171 0.002534046 0.001527951
## ADAO_11159808 0.0001358812 0.015095114 0.0062381736 0.001875013 0.003922788
## AGG_11236448 0.0019213181 0.013914328 0.0008647023 0.003699604 0.010210024
## AHL_11239959 0.0034717910 0.013281290 0.0127965607 0.017040692 0.005776028
## AJGD_11119689 0.0857140836 0.082088177 0.0424761268 0.012070854 0.068183526
## AMP_11228639 0.0033134470 0.001166984 0.0070391417 0.006086372 0.005196420
## X421 X422 X423 X424 X425
## ACR_11231843 0.001956135 0.0001824963 0.006114042 0.001628895 0.002299060
## ADAO_11159808 0.017725434 0.0332986725 0.019359601 0.016026893 0.010209181
## AGG_11236448 0.021633657 0.0134838636 0.010732386 0.002635290 0.009419816
## AHL_11239959 0.009538895 0.0137761840 0.037468662 0.011361573 0.001937010
## AJGD_11119689 0.025580423 0.0341452621 0.028978066 0.107596794 0.099988858
## AMP_11228639 0.004539961 0.0040345774 0.013231915 0.015428982 0.005955688
## X426 X427 X428 X429 X430
## ACR_11231843 0.001959534 0.002895671 0.002034898 0.0034631728 0.002512409
## ADAO_11159808 0.006444412 0.002147021 0.015923457 0.0253095375 0.010124052
## AGG_11236448 0.033007899 0.014262566 0.002752943 0.0063445744 0.005400672
## AHL_11239959 0.014878974 0.031473715 0.015774219 0.0038368385 0.021070051
## AJGD_11119689 0.011298382 0.084669380 0.036206415 0.0001013254 0.041378800
## AMP_11228639 0.008107092 0.004164672 0.014822942 0.0059739986 0.002861308
## X431 X432 X433 X434 X435
## ACR_11231843 0.002139917 0.0026408516 0.001634892 0.001624848 0.005096672
## ADAO_11159808 0.012346128 0.0020638602 0.004602150 0.009813869 0.005905533
## AGG_11236448 0.012070029 0.0076454850 0.031188190 0.001264647 0.004462875
## AHL_11239959 0.006314724 0.0004838708 0.010997741 0.022556810 0.026950815
## AJGD_11119689 0.095775141 0.0098114106 0.119020878 0.044389731 0.309550487
## AMP_11228639 0.033386155 0.0140436595 0.012662284 0.014224790 0.014325051
## X436 X437 X438 X439 X440
## ACR_11231843 0.001600055 0.004648909 0.0006950449 0.001815009 0.0015097296
## ADAO_11159808 0.002033916 0.014805867 0.0024736123 0.001887653 0.0007154884
## AGG_11236448 0.028966759 0.013782612 0.0033169749 0.002698040 0.0072043440
## AHL_11239959 0.092662759 0.013159547 0.0239483768 0.003870477 0.0025498846
## AJGD_11119689 0.018637848 0.033257713 0.0454344500 0.019045720 0.0101626511
## AMP_11228639 0.001600465 0.019138606 0.0225169957 0.005897850 0.0029549110
## X441 X442 X443 X444 X445
## ACR_11231843 0.002915231 0.004496119 0.0006821579 0.001638866 0.0013166010
## ADAO_11159808 0.006758415 0.010558720 0.0197781308 0.020866383 0.0017031500
## AGG_11236448 0.006553657 0.037836293 0.0019381164 0.002492667 0.0019611384
## AHL_11239959 0.009493808 0.014659568 0.0245564670 0.008468273 0.0068285979
## AJGD_11119689 0.162925836 0.015494936 0.0745746727 0.033645347 0.0315858486
## AMP_11228639 0.002623044 0.001395195 0.0199687908 0.010958935 0.0000414788
## X446 X447 X448 X449 X450
## ACR_11231843 0.001705833 0.002155267 0.002938489 0.001563434 0.0016913359
## ADAO_11159808 0.014284512 0.004356601 0.001580822 0.013794678 0.0122416229
## AGG_11236448 0.005165989 0.001330134 0.029078111 0.017015390 0.0005169822
## AHL_11239959 0.039554307 0.036051450 0.001447832 0.041540078 0.0507737323
## AJGD_11119689 0.174121843 0.006420175 0.011512953 0.262637010 0.0495231170
## AMP_11228639 0.010555567 0.009126864 0.027717068 0.001513354 0.0117047959
## X451 X452 X453 X454 X455
## ACR_11231843 0.0011454122 0.002531867 0.001001203 0.001199534 0.001012105
## ADAO_11159808 0.0044044822 0.004567701 0.014283622 0.002825495 0.017195116
## AGG_11236448 0.0003730042 0.002036017 0.010083894 0.009524501 0.007992632
## AHL_11239959 0.0134972760 0.042595994 0.016019659 0.041293063 0.008845300
## AJGD_11119689 0.0230599369 0.022160424 0.055753435 0.036653655 0.077744872
## AMP_11228639 0.0265067845 0.007537867 0.000134721 0.007991157 0.006805094
## X456 X457 X458 X459 X460
## ACR_11231843 0.002901409 0.0012790861 0.002126382 0.0004990376 0.001286343
## ADAO_11159808 0.007301579 0.0006129838 0.013621005 0.0021834399 0.012427842
## AGG_11236448 0.008663268 0.0179627806 0.012625752 0.0239270580 0.004247566
## AHL_11239959 0.012840700 0.0043720914 0.038017552 0.0409157313 0.010603025
## AJGD_11119689 0.001628251 0.0083552841 0.004008203 0.0473291121 0.014775849
## AMP_11228639 0.002647968 0.0033259286 0.002671043 0.0222067956 0.004585452
## X461 X462 X463 X464 X465
## ACR_11231843 0.001905377 0.001653424 0.001518872 0.001558562 0.0004076182
## ADAO_11159808 0.008359206 0.041643049 0.006474549 0.001995487 0.0001375381
## AGG_11236448 0.000249191 0.001951449 0.017781944 0.011314477 0.0117390360
## AHL_11239959 0.005063536 0.003908493 0.015979987 0.003436973 0.0097358458
## AJGD_11119689 0.008044921 0.021760620 0.133924727 0.042301321 0.0261266792
## AMP_11228639 0.001855153 0.019776755 0.004444629 0.007786362 0.0096147812
## X466 X467 X468 X469 X470
## ACR_11231843 0.001079410 0.002042499 0.002444948 0.0023746838 0.002927610
## ADAO_11159808 0.010970877 0.025893293 0.003932126 0.0096733529 0.007607704
## AGG_11236448 0.015504724 0.008840224 0.012087977 0.0020367127 0.037346086
## AHL_11239959 0.017839258 0.031484905 0.006704746 0.0355160008 0.023771098
## AJGD_11119689 0.196134356 0.075386856 0.133282431 0.0533518708 0.056635072
## AMP_11228639 0.003124835 0.004864384 0.030126939 0.0002340561 0.012994589
## X471 X472 X473 X474 X475
## ACR_11231843 0.0011711000 0.001497628 0.002045806 0.0007346903 0.002849697
## ADAO_11159808 0.0115935515 0.004392633 0.008641028 0.0239998890 0.002308332
## AGG_11236448 0.0274383832 0.054922023 0.001047180 0.0159150738 0.012660231
## AHL_11239959 0.0032921410 0.003425980 0.006887291 0.0427070898 0.029683989
## AJGD_11119689 0.0008970865 0.164458097 0.065914014 0.0796018979 0.191574730
## AMP_11228639 0.0100510939 0.005124847 0.023849634 0.0005273842 0.004260781
## X476 X477 X478 X479 X480
## ACR_11231843 0.0009914444 0.001012983 0.001159732 0.003685346 3.353014e-03
## ADAO_11159808 0.0049430199 0.010582599 0.026189445 0.029035926 3.178474e-04
## AGG_11236448 0.0421503513 0.001919151 0.015872615 0.002443488 2.592733e-03
## AHL_11239959 0.0057535798 0.010793166 0.023248996 0.013417448 8.079576e-02
## AJGD_11119689 0.1305254752 0.007122585 0.001766798 0.077342542 6.035286e-05
## AMP_11228639 0.0129695143 0.012579008 0.004176968 0.014971229 3.595856e-03
## DDclust_PER_cuantiles
## 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_cuantiles), mean)
row.names(rp_tbl_PER) <- paste0("Group",rp_tbl_PER$DDclust_PER_cuantiles)
rp_tbl_PER <- rp_tbl_PER %>%
select(starts_with('X'))
rp_tbl_PER <- data.frame(t(rp_tbl_PER))
head(rp_tbl_PER)## Group1 Group2
## X1 1.0499550 4.9362862
## X2 0.8721602 3.6044422
## X3 0.5343588 1.0745436
## X4 0.5591749 1.0627817
## X5 0.3217129 0.5952355
## X6 0.2390891 0.6153139# Create plotting data-frame
PER_values_by_group <- data.frame("value_PER" = c(rp_tbl_PER$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_cuantiles = data.frame(matrix(ncol = 3 , nrow = 3))
colnames(rand_index_table_cuantiles) <- c("DDclust_ACF_cuantiles", "DDclust_EUCL_cuantiles", "DDclust_PER_cuantiles")
rownames(rand_index_table_cuantiles) <- c("DDclust_ACF_cuantiles", "DDclust_EUCL_cuantiles", "DDclust_PER_cuantiles")
cluster_study_cuantiles <- list(DDclust_ACF_cuantiles, DDclust_EUCL_cuantiles, DDclust_PER_cuantiles)for (i in c(1:length(cluster_study_cuantiles))) {
for (j in c(1:length(cluster_study_cuantiles))){
rand_index_table_cuantiles[i,j] <- adjustedRandIndex(cluster_study_cuantiles[[i]], cluster_study_cuantiles[[j]])
}}head(rand_index_table_cuantiles)## DDclust_ACF_cuantiles DDclust_EUCL_cuantiles
## DDclust_ACF_cuantiles 1.000000000 0.006549118
## DDclust_EUCL_cuantiles 0.006549118 1.000000000
## DDclust_PER_cuantiles 0.194712764 0.109127771
## DDclust_PER_cuantiles
## DDclust_ACF_cuantiles 0.1947128
## DDclust_EUCL_cuantiles 0.1091278
## DDclust_PER_cuantiles 1.0000000write.csv(cluster_study_cuantiles, "../../data/clusters/cluster_study_cuantiles.csv")