Simulación y Dinámica de Sistemas
Maestría en Investigación Operativa y Estadística
Problema
Datos
| Name | Piped data |
| Number of rows | 599 |
| Number of columns | 22 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| DWD_ID | 0 | 1 | 5 | 5 | 0 | 599 | 0 |
| STATION_NAME | 0 | 1 | 3 | 36 | 0 | 599 | 0 |
| FEDERAL_STATE | 0 | 1 | 6 | 22 | 0 | 16 | 0 |
| PERIOD | 0 | 1 | 9 | 9 | 0 | 370 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ID | 0 | 1.00 | 489.20 | 278.50 | 0.00 | 259.50 | 479.00 | 731.50 | 1058.00 | ▇▇▇▇▅ |
| LAT | 0 | 1.00 | 50.75 | 1.90 | 47.40 | 49.27 | 50.64 | 51.96 | 55.01 | ▆▇▇▃▃ |
| LON | 0 | 1.00 | 10.12 | 2.05 | 6.09 | 8.48 | 9.97 | 11.70 | 14.95 | ▃▇▇▅▂ |
| ALTITUDE | 0 | 1.00 | 285.28 | 283.78 | 1.00 | 75.00 | 224.00 | 418.00 | 2964.00 | ▇▁▁▁▁ |
| RECORD_LENGTH | 0 | 1.00 | 80.07 | 36.82 | 30.00 | 54.00 | 70.00 | 103.00 | 297.00 | ▇▃▁▁▁ |
| MEAN_ANNUAL_AIR_TEMP | 1 | 1.00 | 8.40 | 1.23 | 2.50 | 8.00 | 8.50 | 9.10 | 11.00 | ▁▁▂▇▂ |
| MEAN_MONTHLY_MAX_TEMP | 2 | 1.00 | 12.66 | 1.49 | 3.30 | 12.10 | 12.90 | 13.50 | 15.60 | ▁▁▁▇▆ |
| MEAN_MONTHLY_MIN_TEMP | 4 | 0.99 | 4.49 | 1.18 | 0.30 | 3.80 | 4.60 | 5.30 | 7.30 | ▁▂▆▇▂ |
| MEAN_ANNUAL_WIND_SPEED | 11 | 0.98 | 2.12 | 0.70 | 1.00 | 2.00 | 2.00 | 2.00 | 6.00 | ▇▂▁▁▁ |
| MEAN_CLOUD_COVER | 11 | 0.98 | 66.80 | 3.06 | 56.00 | 65.00 | 67.00 | 69.00 | 79.00 | ▁▅▇▂▁ |
| MEAN_ANNUAL_SUNSHINE | 193 | 0.68 | 1516.92 | 203.79 | 0.00 | 1441.25 | 1543.00 | 1634.75 | 1846.00 | ▁▁▁▃▇ |
| MEAN_ANNUAL_RAINFALL | 13 | 0.98 | 787.25 | 233.76 | 446.00 | 640.25 | 737.50 | 857.00 | 1995.00 | ▇▅▁▁▁ |
| MAX_MONTHLY_WIND_SPEED | 11 | 0.98 | 2.72 | 0.79 | 1.00 | 2.00 | 3.00 | 3.00 | 7.00 | ▇▇▂▁▁ |
| MAX_AIR_TEMP | 2 | 1.00 | 31.84 | 2.01 | 13.90 | 31.10 | 32.20 | 33.10 | 35.40 | ▁▁▁▂▇ |
| MAX_WIND_SPEED | 380 | 0.37 | 27.56 | 5.74 | 3.80 | 25.45 | 27.50 | 29.50 | 54.30 | ▁▂▇▁▁ |
| MAX_RAINFALL | 14 | 0.98 | 38.55 | 7.36 | 25.00 | 34.00 | 36.00 | 41.00 | 76.00 | ▇▇▂▁▁ |
| MIN_AIR_TEMP | 2 | 1.00 | -14.93 | 2.57 | -25.40 | -16.70 | -14.90 | -13.30 | -5.30 | ▁▂▇▃▁ |
| MEAN_RANGE_AIR_TEMP | 0 | 1.00 | 8.17 | 1.17 | 0.00 | 7.60 | 8.40 | 8.90 | 11.10 | ▁▁▁▇▃ |
## # A tibble: 599 × 22
## ID DWD_ID STATION_NAME FEDERAL_STATE LAT LON ALTITUDE PERIOD
## <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 0 00001 Aach Baden-Württemb… 47.8 8.85 478 1931-…
## 2 1 00003 Aachen Nordrhein-West… 50.8 6.09 202 1851-…
## 3 2 00044 Großenkneten Niedersachsen 52.9 8.24 44 1971-…
## 4 6 00071 Albstadt-Badkap Baden-Württemb… 48.2 8.98 759 1986-…
## 5 8 00073 Aldersbach-Kriestorf Bayern 48.6 13.1 340 1952-…
## 6 9 00078 Alfhausen Niedersachsen 52.5 7.91 65 1961-…
## 7 10 00091 Alsfeld-Eifa Hessen 50.7 9.35 300 1978-…
## 8 12 00098 Altastenberg Nordrhein-West… 51.2 8.47 780 1887-…
## 9 14 00116 Altenburg Thüringen 51.0 12.4 213 1899-…
## 10 18 00132 Altglashütte Bayern 49.8 12.4 750 1954-…
## # ℹ 589 more rows
## # ℹ 14 more variables: RECORD_LENGTH <dbl>, MEAN_ANNUAL_AIR_TEMP <dbl>,
## # MEAN_MONTHLY_MAX_TEMP <dbl>, MEAN_MONTHLY_MIN_TEMP <dbl>,
## # MEAN_ANNUAL_WIND_SPEED <dbl>, MEAN_CLOUD_COVER <dbl>,
## # MEAN_ANNUAL_SUNSHINE <dbl>, MEAN_ANNUAL_RAINFALL <dbl>,
## # MAX_MONTHLY_WIND_SPEED <dbl>, MAX_AIR_TEMP <dbl>, MAX_WIND_SPEED <dbl>,
## # MAX_RAINFALL <dbl>, MIN_AIR_TEMP <dbl>, MEAN_RANGE_AIR_TEMP <dbl>Ubicación estaciones meteorológicas (Alemania)
#Map buoys location
station_coor <- data[, c('STATION_NAME', 'LON', 'LAT')]
station <- unique(station_coor$STATION_NAME)
dat_coor <- data.frame()
for (i in 1:length(station)) {
M_station <- station_coor[station_coor$STATION_NAME == station[i], ][1,]
dat_coor <- rbind(dat_coor, M_station)
}
# Create a leaflet map
map <- leaflet(dat_coor) %>%
addTiles() %>%
addMarkers(
~LON, ~LAT,
label = ~STATION_NAME,
popup = ~STATION_NAME,
) %>%
addProviderTiles("Esri.WorldStreetMap")
# Print the map
mapnew_num_col <- c('ALTITUDE','MEAN_ANNUAL_AIR_TEMP', 'MEAN_MONTHLY_MAX_TEMP', 'MEAN_MONTHLY_MIN_TEMP', 'MEAN_ANNUAL_WIND_SPEED', 'MEAN_CLOUD_COVER', 'MAX_MONTHLY_WIND_SPEED', 'MAX_AIR_TEMP', 'MIN_AIR_TEMP','MAX_RAINFALL', 'MEAN_ANNUAL_RAINFALL')
MEAN_ANNUAL_RAINFALL <- data$MEAN_ANNUAL_RAINFALL
data <- data[, new_num_col]
data <- data %>% mutate(MEAN_ANNUAL_RAINFALL = MEAN_ANNUAL_RAINFALL)
data <- data %>% drop_na()
colnames(data) <- c('Altitude','Mean_Annual_Air_Temp','Mean_Monthly_Max_Temp','Mean_Monthly_Min_Temp','Mean_Annual_Wind_Speed','Mean_Cloud_Clover','Max_Monthly_Wind_Speed','Max_Air_Temp','Min_Air_Temp','Max_Rainfall', 'Mean_Annual_Rainfall')
data## # A tibble: 575 × 11
## Altitude Mean_Annual_Air_Temp Mean_Monthly_Max_Temp Mean_Monthly_Min_Temp
## <dbl> <dbl> <dbl> <dbl>
## 1 478 8.2 13.1 3.5
## 2 202 9.8 13.6 6.3
## 3 44 9.2 13.2 5.4
## 4 759 7.4 12.2 3.3
## 5 340 8.4 13.4 3.9
## 6 65 9.3 13.4 5.2
## 7 300 8.2 12.7 4.1
## 8 750 5.7 9.2 2.7
## 9 510 8 13 3.7
## 10 215 9.5 13.8 5.3
## # ℹ 565 more rows
## # ℹ 7 more variables: Mean_Annual_Wind_Speed <dbl>, Mean_Cloud_Clover <dbl>,
## # Max_Monthly_Wind_Speed <dbl>, Max_Air_Temp <dbl>, Min_Air_Temp <dbl>,
## # Max_Rainfall <dbl>, Mean_Annual_Rainfall <dbl>#Variable correlation
pairs.panels(data,
method = "pearson", # correlation method
hist.col = "#00AFBB",
density = TRUE, # show density plots
ellipses = TRUE # show correlation ellipses
)
Funciones: Análisis de parámetros
#Espectro de modelos
Bin_matrix1 <- function(pred_var,dt) {
matrix_data <- matrix(0, nrow = (2^pred_var), ncol = pred_var) #Matriz inicial de 0s
combinations <- do.call(expand.grid, replicate(pred_var, 0:1, simplify = FALSE)) #Cálculo combinaciones
matrix_data <- as.matrix(combinations)
dat_fr <- as.data.frame(matrix_data)
dat_frr <- dat_fr[-1, ] #Eliminación de primer modelo (Ninguna variable predictora)
colnames(dat_frr) <- names(dt)[1:pred_var]
return(dat_frr)
}#Seleccionar modelos: Consistencia / Significancia
Con_Sig2 <- function(dt,F0,sng){
value_list <- list(
NA_p = vector("logical"),
Sg = vector("logical")
)
for (i in 1:nrow(F0)){
indices <- which(F0[i, ] == 1)
column_name <- names(F0)[indices]
formula <- as.formula(paste("Mean_Annual_Rainfall ~", paste(column_name, collapse = " + ")))
model <- lm(formula, data = dt)
p_values <- data.frame(summary(model)$coefficients[, "Pr(>|t|)"])
colnames(p_values) <- c("P_values")
pn_r <- nrow(p_values)-1
LC <- any(p_values$P_values > sng)
value_list$NA_p <- c(value_list$NA_p, length(column_name) != nrow(p_values)-1) #Consistencia
value_list$Sg <- c(value_list$Sg,LC) #Significancia
}
my_matrix <- do.call(rbind, value_list)
my_matrix<- t(my_matrix)
NA_pSg <- data.frame(my_matrix)
matching_rows <- which((NA_pSg$Sg == "FALSE") & (NA_pSg$NA_p == "FALSE"))
matching_rows <- data.frame(matching_rows)
Filter_1 <- F0[matching_rows$matching_rows, ]
return(Filter_1)
}#Seleccionar modelos: Prueba de Independencia
Ind <- function(F1,dt){
value_list <- list(
Indp = vector("logical")
)
for(i in 1:nrow(F1)){
indices <- which(F1[i, ] == 1)
column_name <- names(F1)[indices]
formula <- as.formula(paste("Mean_Annual_Rainfall ~", paste(column_name, collapse = " + ")))
model <- lm(formula, data = dt)
DW_t <- dwtest(model)$statistic #Independencia
DW_c <- data.frame(DW_t)
value_list$Indp <- c(value_list$Indp, any(DW_c$DW_t > 1.5 && DW_t < 2.5))
}
Id <- data.frame(value_list)
matching_rows <- which(Id$Indp == "TRUE")
matching_rows <- data.frame(matching_rows)
Filter_2 <- F1[matching_rows$matching_rows, ]
return(Filter_2)
}#Seleccionar modelos: Prueba de Colinealidad
Coli <- function(F2,dt){
value_list <- list(
Coln = vector("logical")
)
sum_r <- rowSums(F2[, 1:ncol(F2)])
sum_r <- data.frame(sum_r)
colnames(sum_r) <- c("sm")
matrix_of_zeros <- matrix(0, nrow = 1, ncol = 1)
for(i in 1:nrow(F2)){
indices <- which(F2[i, ] == 1)
column_name <- names(F2)[indices]
formula <- as.formula(paste("Mean_Annual_Rainfall ~", paste(column_name, collapse = " + ")))
model <- lm(formula, data = dt)
if (sum_r$sm[i] > 1) {
cold <- data.frame(vif(model)) #Colinealidad
} else {
cold <- data.frame(matrix_of_zeros)
}
colnames(cold) <- c("VIF")
value_list$Coln <- c(value_list$Coln, any(cold$VIF > 5.0)) #Colinealidad
}
Cl <- data.frame(value_list)
matching_rows <- which(Cl$Coln == "FALSE")
matching_rows <- data.frame(matching_rows)
Filter_3 <- F2[matching_rows$matching_rows, ]
return(Filter_3)
}#Construcción matriz de decisión inciial pra MCDM (Pruebas múltiples)
MCDM_DM <- function(F3,dt){
value_list <- list(
CI = vector("integer"),
R_2 = vector("numeric"),
LF = vector("numeric"),
BP = vector("numeric")
)
for(i in 1:nrow(F3)){
indices <- which(F3[i, ] == 1)
column_name <- names(F3)[indices]
formula <- as.formula(paste("Mean_Annual_Rainfall ~", paste(column_name, collapse = " + ")))
model <- lm(formula, data = dt)
R_2 <- summary(model)$adj.r.square #Ajuste del modelo
R_2 <- data.frame(R_2)
LF_t <- lillie.test(model$residuals)$p.value #Normalidad
LF_t <- data.frame(LF_t)
BP_t <- bptest(model)$p.value #Homocedasticidad
BP_c <- data.frame(BP_t)
value_list$R_2 <- c(value_list$R_2, R_2$R_2)
value_list$LF_t <- c(value_list$LF_t, LF_t$LF_t)
value_list$BP <- c(value_list$BP, BP_c$BP_t)
}
row_sums <- data.frame(rowSums(F3[, 1:ncol(F3)])) #Número de variables predictoras
colnames(row_sums) <- c("CI")
value_list$CI <- c(value_list$CI, row_sums$CI)
my_matrix <- do.call(rbind, value_list)
my_matrix <- t(my_matrix)
MD <- data.frame(my_matrix)
return(MD)
}Número de posibles modelos lineales
## [1] 10## Altitude Mean_Annual_Air_Temp Mean_Monthly_Max_Temp Mean_Monthly_Min_Temp
## 2 1 0 0 0
## 3 0 1 0 0
## 4 1 1 0 0
## 5 0 0 1 0
## 6 1 0 1 0
## 7 0 1 1 0
## 8 1 1 1 0
## 9 0 0 0 1
## 10 1 0 0 1
## 11 0 1 0 1
## 12 1 1 0 1
## 13 0 0 1 1
## 14 1 0 1 1
## 15 0 1 1 1
## 16 1 1 1 1
## 17 0 0 0 0
## 18 1 0 0 0
## 19 0 1 0 0
## 20 1 1 0 0
## 21 0 0 1 0
## 22 1 0 1 0
## 23 0 1 1 0
## 24 1 1 1 0
## 25 0 0 0 1
## 26 1 0 0 1
## 27 0 1 0 1
## 28 1 1 0 1
## 29 0 0 1 1
## 30 1 0 1 1
## 31 0 1 1 1
## 32 1 1 1 1
## 33 0 0 0 0
## 34 1 0 0 0
## 35 0 1 0 0
## 36 1 1 0 0
## 37 0 0 1 0
## 38 1 0 1 0
## 39 0 1 1 0
## 40 1 1 1 0
## 41 0 0 0 1
## 42 1 0 0 1
## 43 0 1 0 1
## 44 1 1 0 1
## 45 0 0 1 1
## 46 1 0 1 1
## 47 0 1 1 1
## 48 1 1 1 1
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## 57 0 0 0 1
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## 450 1 0 0 0
## 451 0 1 0 0
## 452 1 1 0 0
## 453 0 0 1 0
## 454 1 0 1 0
## 455 0 1 1 0
## 456 1 1 1 0
## 457 0 0 0 1
## 458 1 0 0 1
## 459 0 1 0 1
## 460 1 1 0 1
## 461 0 0 1 1
## 462 1 0 1 1
## 463 0 1 1 1
## 464 1 1 1 1
## 465 0 0 0 0
## 466 1 0 0 0
## 467 0 1 0 0
## 468 1 1 0 0
## 469 0 0 1 0
## 470 1 0 1 0
## 471 0 1 1 0
## 472 1 1 1 0
## 473 0 0 0 1
## 474 1 0 0 1
## 475 0 1 0 1
## 476 1 1 0 1
## 477 0 0 1 1
## 478 1 0 1 1
## 479 0 1 1 1
## 480 1 1 1 1
## 481 0 0 0 0
## 482 1 0 0 0
## 483 0 1 0 0
## 484 1 1 0 0
## 485 0 0 1 0
## 486 1 0 1 0
## 487 0 1 1 0
## 488 1 1 1 0
## 489 0 0 0 1
## 490 1 0 0 1
## 491 0 1 0 1
## 492 1 1 0 1
## 493 0 0 1 1
## 494 1 0 1 1
## 495 0 1 1 1
## 496 1 1 1 1
## 497 0 0 0 0
## 498 1 0 0 0
## 499 0 1 0 0
## 500 1 1 0 0
## 501 0 0 1 0
## 502 1 0 1 0
## 503 0 1 1 0
## 504 1 1 1 0
## 505 0 0 0 1
## 506 1 0 0 1
## 507 0 1 0 1
## 508 1 1 0 1
## 509 0 0 1 1
## 510 1 0 1 1
## 511 0 1 1 1
## 512 1 1 1 1
## 513 0 0 0 0
## 514 1 0 0 0
## 515 0 1 0 0
## 516 1 1 0 0
## 517 0 0 1 0
## 518 1 0 1 0
## 519 0 1 1 0
## 520 1 1 1 0
## 521 0 0 0 1
## 522 1 0 0 1
## 523 0 1 0 1
## 524 1 1 0 1
## 525 0 0 1 1
## 526 1 0 1 1
## 527 0 1 1 1
## 528 1 1 1 1
## 529 0 0 0 0
## 530 1 0 0 0
## 531 0 1 0 0
## 532 1 1 0 0
## 533 0 0 1 0
## 534 1 0 1 0
## 535 0 1 1 0
## 536 1 1 1 0
## 537 0 0 0 1
## 538 1 0 0 1
## 539 0 1 0 1
## 540 1 1 0 1
## 541 0 0 1 1
## 542 1 0 1 1
## 543 0 1 1 1
## 544 1 1 1 1
## 545 0 0 0 0
## 546 1 0 0 0
## 547 0 1 0 0
## 548 1 1 0 0
## 549 0 0 1 0
## 550 1 0 1 0
## 551 0 1 1 0
## 552 1 1 1 0
## 553 0 0 0 1
## 554 1 0 0 1
## 555 0 1 0 1
## 556 1 1 0 1
## 557 0 0 1 1
## 558 1 0 1 1
## 559 0 1 1 1
## 560 1 1 1 1
## 561 0 0 0 0
## 562 1 0 0 0
## 563 0 1 0 0
## 564 1 1 0 0
## 565 0 0 1 0
## 566 1 0 1 0
## 567 0 1 1 0
## 568 1 1 1 0
## 569 0 0 0 1
## 570 1 0 0 1
## 571 0 1 0 1
## 572 1 1 0 1
## 573 0 0 1 1
## 574 1 0 1 1
## 575 0 1 1 1
## 576 1 1 1 1
## 577 0 0 0 0
## 578 1 0 0 0
## 579 0 1 0 0
## 580 1 1 0 0
## 581 0 0 1 0
## 582 1 0 1 0
## 583 0 1 1 0
## 584 1 1 1 0
## 585 0 0 0 1
## 586 1 0 0 1
## 587 0 1 0 1
## 588 1 1 0 1
## 589 0 0 1 1
## 590 1 0 1 1
## 591 0 1 1 1
## 592 1 1 1 1
## 593 0 0 0 0
## 594 1 0 0 0
## 595 0 1 0 0
## 596 1 1 0 0
## 597 0 0 1 0
## 598 1 0 1 0
## 599 0 1 1 0
## 600 1 1 1 0
## 601 0 0 0 1
## 602 1 0 0 1
## 603 0 1 0 1
## 604 1 1 0 1
## 605 0 0 1 1
## 606 1 0 1 1
## 607 0 1 1 1
## 608 1 1 1 1
## 609 0 0 0 0
## 610 1 0 0 0
## 611 0 1 0 0
## 612 1 1 0 0
## 613 0 0 1 0
## 614 1 0 1 0
## 615 0 1 1 0
## 616 1 1 1 0
## 617 0 0 0 1
## 618 1 0 0 1
## 619 0 1 0 1
## 620 1 1 0 1
## 621 0 0 1 1
## 622 1 0 1 1
## 623 0 1 1 1
## 624 1 1 1 1
## 625 0 0 0 0
## 626 1 0 0 0
## 627 0 1 0 0
## 628 1 1 0 0
## 629 0 0 1 0
## 630 1 0 1 0
## 631 0 1 1 0
## 632 1 1 1 0
## 633 0 0 0 1
## 634 1 0 0 1
## 635 0 1 0 1
## 636 1 1 0 1
## 637 0 0 1 1
## 638 1 0 1 1
## 639 0 1 1 1
## 640 1 1 1 1
## 641 0 0 0 0
## 642 1 0 0 0
## 643 0 1 0 0
## 644 1 1 0 0
## 645 0 0 1 0
## 646 1 0 1 0
## 647 0 1 1 0
## 648 1 1 1 0
## 649 0 0 0 1
## 650 1 0 0 1
## 651 0 1 0 1
## 652 1 1 0 1
## 653 0 0 1 1
## 654 1 0 1 1
## 655 0 1 1 1
## 656 1 1 1 1
## 657 0 0 0 0
## 658 1 0 0 0
## 659 0 1 0 0
## 660 1 1 0 0
## 661 0 0 1 0
## 662 1 0 1 0
## 663 0 1 1 0
## 664 1 1 1 0
## 665 0 0 0 1
## 666 1 0 0 1
## 667 0 1 0 1
## 668 1 1 0 1
## 669 0 0 1 1
## 670 1 0 1 1
## 671 0 1 1 1
## 672 1 1 1 1
## 673 0 0 0 0
## 674 1 0 0 0
## 675 0 1 0 0
## 676 1 1 0 0
## 677 0 0 1 0
## 678 1 0 1 0
## 679 0 1 1 0
## 680 1 1 1 0
## 681 0 0 0 1
## 682 1 0 0 1
## 683 0 1 0 1
## 684 1 1 0 1
## 685 0 0 1 1
## 686 1 0 1 1
## 687 0 1 1 1
## 688 1 1 1 1
## 689 0 0 0 0
## 690 1 0 0 0
## 691 0 1 0 0
## 692 1 1 0 0
## 693 0 0 1 0
## 694 1 0 1 0
## 695 0 1 1 0
## 696 1 1 1 0
## 697 0 0 0 1
## 698 1 0 0 1
## 699 0 1 0 1
## 700 1 1 0 1
## 701 0 0 1 1
## 702 1 0 1 1
## 703 0 1 1 1
## 704 1 1 1 1
## 705 0 0 0 0
## 706 1 0 0 0
## 707 0 1 0 0
## 708 1 1 0 0
## 709 0 0 1 0
## 710 1 0 1 0
## 711 0 1 1 0
## 712 1 1 1 0
## 713 0 0 0 1
## 714 1 0 0 1
## 715 0 1 0 1
## 716 1 1 0 1
## 717 0 0 1 1
## 718 1 0 1 1
## 719 0 1 1 1
## 720 1 1 1 1
## 721 0 0 0 0
## 722 1 0 0 0
## 723 0 1 0 0
## 724 1 1 0 0
## 725 0 0 1 0
## 726 1 0 1 0
## 727 0 1 1 0
## 728 1 1 1 0
## 729 0 0 0 1
## 730 1 0 0 1
## 731 0 1 0 1
## 732 1 1 0 1
## 733 0 0 1 1
## 734 1 0 1 1
## 735 0 1 1 1
## 736 1 1 1 1
## 737 0 0 0 0
## 738 1 0 0 0
## 739 0 1 0 0
## 740 1 1 0 0
## 741 0 0 1 0
## 742 1 0 1 0
## 743 0 1 1 0
## 744 1 1 1 0
## 745 0 0 0 1
## 746 1 0 0 1
## 747 0 1 0 1
## 748 1 1 0 1
## 749 0 0 1 1
## 750 1 0 1 1
## 751 0 1 1 1
## 752 1 1 1 1
## 753 0 0 0 0
## 754 1 0 0 0
## 755 0 1 0 0
## 756 1 1 0 0
## 757 0 0 1 0
## 758 1 0 1 0
## 759 0 1 1 0
## 760 1 1 1 0
## 761 0 0 0 1
## 762 1 0 0 1
## 763 0 1 0 1
## 764 1 1 0 1
## 765 0 0 1 1
## 766 1 0 1 1
## 767 0 1 1 1
## 768 1 1 1 1
## 769 0 0 0 0
## 770 1 0 0 0
## 771 0 1 0 0
## 772 1 1 0 0
## 773 0 0 1 0
## 774 1 0 1 0
## 775 0 1 1 0
## 776 1 1 1 0
## 777 0 0 0 1
## 778 1 0 0 1
## 779 0 1 0 1
## 780 1 1 0 1
## 781 0 0 1 1
## 782 1 0 1 1
## 783 0 1 1 1
## 784 1 1 1 1
## 785 0 0 0 0
## 786 1 0 0 0
## 787 0 1 0 0
## 788 1 1 0 0
## 789 0 0 1 0
## 790 1 0 1 0
## 791 0 1 1 0
## 792 1 1 1 0
## 793 0 0 0 1
## 794 1 0 0 1
## 795 0 1 0 1
## 796 1 1 0 1
## 797 0 0 1 1
## 798 1 0 1 1
## 799 0 1 1 1
## 800 1 1 1 1
## 801 0 0 0 0
## 802 1 0 0 0
## 803 0 1 0 0
## 804 1 1 0 0
## 805 0 0 1 0
## 806 1 0 1 0
## 807 0 1 1 0
## 808 1 1 1 0
## 809 0 0 0 1
## 810 1 0 0 1
## 811 0 1 0 1
## 812 1 1 0 1
## 813 0 0 1 1
## 814 1 0 1 1
## 815 0 1 1 1
## 816 1 1 1 1
## 817 0 0 0 0
## 818 1 0 0 0
## 819 0 1 0 0
## 820 1 1 0 0
## 821 0 0 1 0
## 822 1 0 1 0
## 823 0 1 1 0
## 824 1 1 1 0
## 825 0 0 0 1
## 826 1 0 0 1
## 827 0 1 0 1
## 828 1 1 0 1
## 829 0 0 1 1
## 830 1 0 1 1
## 831 0 1 1 1
## 832 1 1 1 1
## 833 0 0 0 0
## 834 1 0 0 0
## 835 0 1 0 0
## 836 1 1 0 0
## 837 0 0 1 0
## 838 1 0 1 0
## 839 0 1 1 0
## 840 1 1 1 0
## 841 0 0 0 1
## 842 1 0 0 1
## 843 0 1 0 1
## 844 1 1 0 1
## 845 0 0 1 1
## 846 1 0 1 1
## 847 0 1 1 1
## 848 1 1 1 1
## 849 0 0 0 0
## 850 1 0 0 0
## 851 0 1 0 0
## 852 1 1 0 0
## 853 0 0 1 0
## 854 1 0 1 0
## 855 0 1 1 0
## 856 1 1 1 0
## 857 0 0 0 1
## 858 1 0 0 1
## 859 0 1 0 1
## 860 1 1 0 1
## 861 0 0 1 1
## 862 1 0 1 1
## 863 0 1 1 1
## 864 1 1 1 1
## 865 0 0 0 0
## 866 1 0 0 0
## 867 0 1 0 0
## 868 1 1 0 0
## 869 0 0 1 0
## 870 1 0 1 0
## 871 0 1 1 0
## 872 1 1 1 0
## 873 0 0 0 1
## 874 1 0 0 1
## 875 0 1 0 1
## 876 1 1 0 1
## 877 0 0 1 1
## 878 1 0 1 1
## 879 0 1 1 1
## 880 1 1 1 1
## 881 0 0 0 0
## 882 1 0 0 0
## 883 0 1 0 0
## 884 1 1 0 0
## 885 0 0 1 0
## 886 1 0 1 0
## 887 0 1 1 0
## 888 1 1 1 0
## 889 0 0 0 1
## 890 1 0 0 1
## 891 0 1 0 1
## 892 1 1 0 1
## 893 0 0 1 1
## 894 1 0 1 1
## 895 0 1 1 1
## 896 1 1 1 1
## 897 0 0 0 0
## 898 1 0 0 0
## 899 0 1 0 0
## 900 1 1 0 0
## 901 0 0 1 0
## 902 1 0 1 0
## 903 0 1 1 0
## 904 1 1 1 0
## 905 0 0 0 1
## 906 1 0 0 1
## 907 0 1 0 1
## 908 1 1 0 1
## 909 0 0 1 1
## 910 1 0 1 1
## 911 0 1 1 1
## 912 1 1 1 1
## 913 0 0 0 0
## 914 1 0 0 0
## 915 0 1 0 0
## 916 1 1 0 0
## 917 0 0 1 0
## 918 1 0 1 0
## 919 0 1 1 0
## 920 1 1 1 0
## 921 0 0 0 1
## 922 1 0 0 1
## 923 0 1 0 1
## 924 1 1 0 1
## 925 0 0 1 1
## 926 1 0 1 1
## 927 0 1 1 1
## 928 1 1 1 1
## 929 0 0 0 0
## 930 1 0 0 0
## 931 0 1 0 0
## 932 1 1 0 0
## 933 0 0 1 0
## 934 1 0 1 0
## 935 0 1 1 0
## 936 1 1 1 0
## 937 0 0 0 1
## 938 1 0 0 1
## 939 0 1 0 1
## 940 1 1 0 1
## 941 0 0 1 1
## 942 1 0 1 1
## 943 0 1 1 1
## 944 1 1 1 1
## 945 0 0 0 0
## 946 1 0 0 0
## 947 0 1 0 0
## 948 1 1 0 0
## 949 0 0 1 0
## 950 1 0 1 0
## 951 0 1 1 0
## 952 1 1 1 0
## 953 0 0 0 1
## 954 1 0 0 1
## 955 0 1 0 1
## 956 1 1 0 1
## 957 0 0 1 1
## 958 1 0 1 1
## 959 0 1 1 1
## 960 1 1 1 1
## 961 0 0 0 0
## 962 1 0 0 0
## 963 0 1 0 0
## 964 1 1 0 0
## 965 0 0 1 0
## 966 1 0 1 0
## 967 0 1 1 0
## 968 1 1 1 0
## 969 0 0 0 1
## 970 1 0 0 1
## 971 0 1 0 1
## 972 1 1 0 1
## 973 0 0 1 1
## 974 1 0 1 1
## 975 0 1 1 1
## 976 1 1 1 1
## 977 0 0 0 0
## 978 1 0 0 0
## 979 0 1 0 0
## 980 1 1 0 0
## 981 0 0 1 0
## 982 1 0 1 0
## 983 0 1 1 0
## 984 1 1 1 0
## 985 0 0 0 1
## 986 1 0 0 1
## 987 0 1 0 1
## 988 1 1 0 1
## 989 0 0 1 1
## 990 1 0 1 1
## 991 0 1 1 1
## 992 1 1 1 1
## 993 0 0 0 0
## 994 1 0 0 0
## 995 0 1 0 0
## 996 1 1 0 0
## 997 0 0 1 0
## 998 1 0 1 0
## 999 0 1 1 0
## 1000 1 1 1 0
## 1001 0 0 0 1
## 1002 1 0 0 1
## 1003 0 1 0 1
## 1004 1 1 0 1
## 1005 0 0 1 1
## 1006 1 0 1 1
## 1007 0 1 1 1
## 1008 1 1 1 1
## 1009 0 0 0 0
## 1010 1 0 0 0
## 1011 0 1 0 0
## 1012 1 1 0 0
## 1013 0 0 1 0
## 1014 1 0 1 0
## 1015 0 1 1 0
## 1016 1 1 1 0
## 1017 0 0 0 1
## 1018 1 0 0 1
## 1019 0 1 0 1
## 1020 1 1 0 1
## 1021 0 0 1 1
## 1022 1 0 1 1
## 1023 0 1 1 1
## 1024 1 1 1 1
## Mean_Annual_Wind_Speed Mean_Cloud_Clover Max_Monthly_Wind_Speed
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 1 0 0
## 18 1 0 0
## 19 1 0 0
## 20 1 0 0
## 21 1 0 0
## 22 1 0 0
## 23 1 0 0
## 24 1 0 0
## 25 1 0 0
## 26 1 0 0
## 27 1 0 0
## 28 1 0 0
## 29 1 0 0
## 30 1 0 0
## 31 1 0 0
## 32 1 0 0
## 33 0 1 0
## 34 0 1 0
## 35 0 1 0
## 36 0 1 0
## 37 0 1 0
## 38 0 1 0
## 39 0 1 0
## 40 0 1 0
## 41 0 1 0
## 42 0 1 0
## 43 0 1 0
## 44 0 1 0
## 45 0 1 0
## 46 0 1 0
## 47 0 1 0
## 48 0 1 0
## 49 1 1 0
## 50 1 1 0
## 51 1 1 0
## 52 1 1 0
## 53 1 1 0
## 54 1 1 0
## 55 1 1 0
## 56 1 1 0
## 57 1 1 0
## 58 1 1 0
## 59 1 1 0
## 60 1 1 0
## 61 1 1 0
## 62 1 1 0
## 63 1 1 0
## 64 1 1 0
## 65 0 0 1
## 66 0 0 1
## 67 0 0 1
## 68 0 0 1
## 69 0 0 1
## 70 0 0 1
## 71 0 0 1
## 72 0 0 1
## 73 0 0 1
## 74 0 0 1
## 75 0 0 1
## 76 0 0 1
## 77 0 0 1
## 78 0 0 1
## 79 0 0 1
## 80 0 0 1
## 81 1 0 1
## 82 1 0 1
## 83 1 0 1
## 84 1 0 1
## 85 1 0 1
## 86 1 0 1
## 87 1 0 1
## 88 1 0 1
## 89 1 0 1
## 90 1 0 1
## 91 1 0 1
## 92 1 0 1
## 93 1 0 1
## 94 1 0 1
## 95 1 0 1
## 96 1 0 1
## 97 0 1 1
## 98 0 1 1
## 99 0 1 1
## 100 0 1 1
## 101 0 1 1
## 102 0 1 1
## 103 0 1 1
## 104 0 1 1
## 105 0 1 1
## 106 0 1 1
## 107 0 1 1
## 108 0 1 1
## 109 0 1 1
## 110 0 1 1
## 111 0 1 1
## 112 0 1 1
## 113 1 1 1
## 114 1 1 1
## 115 1 1 1
## 116 1 1 1
## 117 1 1 1
## 118 1 1 1
## 119 1 1 1
## 120 1 1 1
## 121 1 1 1
## 122 1 1 1
## 123 1 1 1
## 124 1 1 1
## 125 1 1 1
## 126 1 1 1
## 127 1 1 1
## 128 1 1 1
## 129 0 0 0
## 130 0 0 0
## 131 0 0 0
## 132 0 0 0
## 133 0 0 0
## 134 0 0 0
## 135 0 0 0
## 136 0 0 0
## 137 0 0 0
## 138 0 0 0
## 139 0 0 0
## 140 0 0 0
## 141 0 0 0
## 142 0 0 0
## 143 0 0 0
## 144 0 0 0
## 145 1 0 0
## 146 1 0 0
## 147 1 0 0
## 148 1 0 0
## 149 1 0 0
## 150 1 0 0
## 151 1 0 0
## 152 1 0 0
## 153 1 0 0
## 154 1 0 0
## 155 1 0 0
## 156 1 0 0
## 157 1 0 0
## 158 1 0 0
## 159 1 0 0
## 160 1 0 0
## 161 0 1 0
## 162 0 1 0
## 163 0 1 0
## 164 0 1 0
## 165 0 1 0
## 166 0 1 0
## 167 0 1 0
## 168 0 1 0
## 169 0 1 0
## 170 0 1 0
## 171 0 1 0
## 172 0 1 0
## 173 0 1 0
## 174 0 1 0
## 175 0 1 0
## 176 0 1 0
## 177 1 1 0
## 178 1 1 0
## 179 1 1 0
## 180 1 1 0
## 181 1 1 0
## 182 1 1 0
## 183 1 1 0
## 184 1 1 0
## 185 1 1 0
## 186 1 1 0
## 187 1 1 0
## 188 1 1 0
## 189 1 1 0
## 190 1 1 0
## 191 1 1 0
## 192 1 1 0
## 193 0 0 1
## 194 0 0 1
## 195 0 0 1
## 196 0 0 1
## 197 0 0 1
## 198 0 0 1
## 199 0 0 1
## 200 0 0 1
## 201 0 0 1
## 202 0 0 1
## 203 0 0 1
## 204 0 0 1
## 205 0 0 1
## 206 0 0 1
## 207 0 0 1
## 208 0 0 1
## 209 1 0 1
## 210 1 0 1
## 211 1 0 1
## 212 1 0 1
## 213 1 0 1
## 214 1 0 1
## 215 1 0 1
## 216 1 0 1
## 217 1 0 1
## 218 1 0 1
## 219 1 0 1
## 220 1 0 1
## 221 1 0 1
## 222 1 0 1
## 223 1 0 1
## 224 1 0 1
## 225 0 1 1
## 226 0 1 1
## 227 0 1 1
## 228 0 1 1
## 229 0 1 1
## 230 0 1 1
## 231 0 1 1
## 232 0 1 1
## 233 0 1 1
## 234 0 1 1
## 235 0 1 1
## 236 0 1 1
## 237 0 1 1
## 238 0 1 1
## 239 0 1 1
## 240 0 1 1
## 241 1 1 1
## 242 1 1 1
## 243 1 1 1
## 244 1 1 1
## 245 1 1 1
## 246 1 1 1
## 247 1 1 1
## 248 1 1 1
## 249 1 1 1
## 250 1 1 1
## 251 1 1 1
## 252 1 1 1
## 253 1 1 1
## 254 1 1 1
## 255 1 1 1
## 256 1 1 1
## 257 0 0 0
## 258 0 0 0
## 259 0 0 0
## 260 0 0 0
## 261 0 0 0
## 262 0 0 0
## 263 0 0 0
## 264 0 0 0
## 265 0 0 0
## 266 0 0 0
## 267 0 0 0
## 268 0 0 0
## 269 0 0 0
## 270 0 0 0
## 271 0 0 0
## 272 0 0 0
## 273 1 0 0
## 274 1 0 0
## 275 1 0 0
## 276 1 0 0
## 277 1 0 0
## 278 1 0 0
## 279 1 0 0
## 280 1 0 0
## 281 1 0 0
## 282 1 0 0
## 283 1 0 0
## 284 1 0 0
## 285 1 0 0
## 286 1 0 0
## 287 1 0 0
## 288 1 0 0
## 289 0 1 0
## 290 0 1 0
## 291 0 1 0
## 292 0 1 0
## 293 0 1 0
## 294 0 1 0
## 295 0 1 0
## 296 0 1 0
## 297 0 1 0
## 298 0 1 0
## 299 0 1 0
## 300 0 1 0
## 301 0 1 0
## 302 0 1 0
## 303 0 1 0
## 304 0 1 0
## 305 1 1 0
## 306 1 1 0
## 307 1 1 0
## 308 1 1 0
## 309 1 1 0
## 310 1 1 0
## 311 1 1 0
## 312 1 1 0
## 313 1 1 0
## 314 1 1 0
## 315 1 1 0
## 316 1 1 0
## 317 1 1 0
## 318 1 1 0
## 319 1 1 0
## 320 1 1 0
## 321 0 0 1
## 322 0 0 1
## 323 0 0 1
## 324 0 0 1
## 325 0 0 1
## 326 0 0 1
## 327 0 0 1
## 328 0 0 1
## 329 0 0 1
## 330 0 0 1
## 331 0 0 1
## 332 0 0 1
## 333 0 0 1
## 334 0 0 1
## 335 0 0 1
## 336 0 0 1
## 337 1 0 1
## 338 1 0 1
## 339 1 0 1
## 340 1 0 1
## 341 1 0 1
## 342 1 0 1
## 343 1 0 1
## 344 1 0 1
## 345 1 0 1
## 346 1 0 1
## 347 1 0 1
## 348 1 0 1
## 349 1 0 1
## 350 1 0 1
## 351 1 0 1
## 352 1 0 1
## 353 0 1 1
## 354 0 1 1
## 355 0 1 1
## 356 0 1 1
## 357 0 1 1
## 358 0 1 1
## 359 0 1 1
## 360 0 1 1
## 361 0 1 1
## 362 0 1 1
## 363 0 1 1
## 364 0 1 1
## 365 0 1 1
## 366 0 1 1
## 367 0 1 1
## 368 0 1 1
## 369 1 1 1
## 370 1 1 1
## 371 1 1 1
## 372 1 1 1
## 373 1 1 1
## 374 1 1 1
## 375 1 1 1
## 376 1 1 1
## 377 1 1 1
## 378 1 1 1
## 379 1 1 1
## 380 1 1 1
## 381 1 1 1
## 382 1 1 1
## 383 1 1 1
## 384 1 1 1
## 385 0 0 0
## 386 0 0 0
## 387 0 0 0
## 388 0 0 0
## 389 0 0 0
## 390 0 0 0
## 391 0 0 0
## 392 0 0 0
## 393 0 0 0
## 394 0 0 0
## 395 0 0 0
## 396 0 0 0
## 397 0 0 0
## 398 0 0 0
## 399 0 0 0
## 400 0 0 0
## 401 1 0 0
## 402 1 0 0
## 403 1 0 0
## 404 1 0 0
## 405 1 0 0
## 406 1 0 0
## 407 1 0 0
## 408 1 0 0
## 409 1 0 0
## 410 1 0 0
## 411 1 0 0
## 412 1 0 0
## 413 1 0 0
## 414 1 0 0
## 415 1 0 0
## 416 1 0 0
## 417 0 1 0
## 418 0 1 0
## 419 0 1 0
## 420 0 1 0
## 421 0 1 0
## 422 0 1 0
## 423 0 1 0
## 424 0 1 0
## 425 0 1 0
## 426 0 1 0
## 427 0 1 0
## 428 0 1 0
## 429 0 1 0
## 430 0 1 0
## 431 0 1 0
## 432 0 1 0
## 433 1 1 0
## 434 1 1 0
## 435 1 1 0
## 436 1 1 0
## 437 1 1 0
## 438 1 1 0
## 439 1 1 0
## 440 1 1 0
## 441 1 1 0
## 442 1 1 0
## 443 1 1 0
## 444 1 1 0
## 445 1 1 0
## 446 1 1 0
## 447 1 1 0
## 448 1 1 0
## 449 0 0 1
## 450 0 0 1
## 451 0 0 1
## 452 0 0 1
## 453 0 0 1
## 454 0 0 1
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## 1024 1 1 1Evaluación de parámetros estadísticos
#Pruebas conjuntas
filtros <- function(num_col_dat,data){
F0 <- Bin_matrix1(num_col_dat,data) #Matriz de modelos
F1 <- Con_Sig2(data,F0,0.001) # Consistencia / Significancia
F2 <- Ind(F1,data) #Independencia
F3 <- Coli(F2,data) # Colinealidad
models <- rownames(F3)
models <- paste0("M", models) #Construcción alternativas = modelos
DM <- MCDM_DM(F3,data) #Matriz de decisión inicial
rownames(DM) <- models
DM <- DM[DM$LF_t > 0.05, ] #Normalidad
#DM <- DM[DM$BP > 0.05, ] #Homocedasticidad
DM <- subset(DM, select = c("CI", "R_2")) # Matriz de decisión preliminar
DM <- DM[DM$CI > 2, ] #Matriz de decisión final
return(DM)
}Matriz de decisión
## CI R_2
## M541 4 0.7623657
## M546 3 0.7518150
## M589 4 0.7619596
## M644 4 0.7827550
## M657 3 0.7848536
## M705 3 0.7848624
## M774 4 0.7792558
## M801 3 0.7541242
## M837 4 0.7853780
## M838 5 0.7897274
## M898 4 0.7894618
## M962 5 0.8020371Método de cálculo de pesos para MCDM
#Normalización: Método basado en la entropía
Norm <- function(A, n) {
num_A <- nrow(A)
num_C <- ncol(A)
A_norm <- matrix(0, nrow = num_A, ncol = num_C)
suma <- numeric(num_C)
for (j in 1:num_C) {
for (i in 1:num_A) {
suma[j] <- suma[j] + A[i, j]^n
}
}
for (j in 1:num_C) {
for (i in 1:num_A) {
A_norm[i, j] <- A[i, j] / suma[j]^(1/n)
}
}
return(A_norm)
}Entropía de Shannon
#Pesos de los criterios: Entropía de Shannon
Entropy <- function(A) {
num_A <- nrow(A)
num_C <- ncol(A)
A_norm <- Norm(A, 1)
E <- numeric(num_C)
D <- numeric(num_C)
for (j in 1:num_C) {
for (i in 1:num_A) {
E[j] <- E[j] + A_norm[i, j] * log(A_norm[i, j])/log(num_A)
}
D[j] <- 1 + E[j]
}
w <- numeric(num_C)
for (j in 1:num_C) {
w[j] <- D[j] / sum(D)
}
return(w)
}Multicriteria Decision Making Method (MCDM): TOPSIS
# Normalización (Matriz de decisión)
Norm_improved <- function(A, Atri) {
num_A <- nrow(A)
num_C <- ncol(A)
A_norm <- matrix(0, nrow = num_A, ncol = num_C)
L <- apply(A, ncol(A), min) # 2 indicates columns
U <- apply(A, ncol(A), max)
for (j in 1:num_C) {
for (i in 1:num_A) {
if (Atri[j] == 0) {
A_norm[i, j] <- (U[j] - A[i, j]) / (U[j] - L[j])
} else if (Atri[j] == 1) {
A_norm[i, j] <- (A[i, j] - L[j]) / (U[j] - L[j])
}
}
}
return(A_norm)
}#Ponderación (Matriz de decisión)
Pond <- function(A_norm, W) {
num_A <- nrow(A_norm)
num_C <- ncol(A_norm)
A_pond <- matrix(0, nrow = num_A, ncol = num_C)
for (i in 1:num_A) {
for (j in 1:num_C) {
A_pond[i, j] <- A_norm[i, j] * W[j]
}
}
return(A_pond)
}#Solución ideal y antideal (TOPSIS)
Ideal_solution_improved <- function(A, Atri, W) {
PIS <- W
NIS <- numeric(ncol(A))
return(list(PIS, NIS))
}#Índice de proximidad relativa (TOPSIS)
Similiraty_ratio <- function(A_pond, PIS, NIS) {
num_A <- nrow(A_pond)
num_C <- ncol(A_pond)
Sp <- numeric(num_A) #rep(0, num_A)
Sn <- numeric(num_A)
Sr <- numeric(num_A)
for (i in 1:num_A) {
for (j in 1:num_C) {
Sp[i] <- Sp[i] + (PIS[j] - A_pond[i, j])^2
Sn[i] <- Sn[i] + (NIS[j] - A_pond[i, j])^2
}
Sp[i] <- sqrt(Sp[i])
Sn[i] <- sqrt(Sn[i])
Sr[i] <- Sn[i] / (Sp[i] + Sn[i])
}
return(Sr)
}#Función auxiliar No.1
Ordenar <- function(Ranking, Alt) {
num_A <- length(Ranking)
Res <- cbind(data.frame(Modelos = Alt),data.frame(Ranking= seq(1,length(Ranking))), data.frame(Dominancia = Ranking))
Res <- Res[order(-Res$Dominancia, Res$Modelos), ]
Res$Ranking <- 1:num_A
print(Res)
return(Res)
}#Evaluación de pesos en el caso de tener múltiples escenarios
Pesos_Escenarios <- function(Escenario, A, dex) {
Escenarios <- unique(Escenario)
s <- length(Escenarios) + 2
num_C <- ncol(A)
W <- matrix(0, nrow = s, ncol = num_C)
W[1,] <- Entropy(A)
W[2,] <- rep(1 / num_C, num_C)
for (i in 1:(s - 2)) {
suma_pertenece <- 0
No_pertenece <- 0
for (j in 1:num_C) {
if (Escenarios[i] == Escenario[j]) {
suma_pertenece <- suma_pertenece + W[1, j]
} else {
No_pertenece <- No_pertenece + 1
}
}
for (j in 1:num_C) {
if (Escenarios[i] == Escenario[j]) {
W[i + 2, j] <- dex * (W[1, j] / suma_pertenece)
} else {
W[i + 2, j] <- (1 - dex) / No_pertenece
}
}
}
Escenarios <- c("Base", "Homogéneo", Escenarios)
return(list(Escenarios, W))
}#Evaluación de pesos en el caso de tener múltiples escenarios
pesos_casos_escenarios <- function(Criterios, Escenarios, Matriz_D, des_pesos, index) {
result <- Pesos_Escenarios(Escenarios, Matriz_D, des_pesos)
A <- result[[2]]
A <- A[match(index, result[[1]]), ]
return(A)
}# MCDM TOPSIS
TOPSIS_improved_No2 <- function(Alternativas, Criterios, A, Escenarios, Atri, des_pesos, index) {
W <- pesos_casos_escenarios(Criterios, Escenarios, A, des_pesos, index)
A_norm <- Norm_improved(A, Atri)
A_pond <- Pond(A_norm, W)
PIS_NIS <- Ideal_solution_improved(A, Atri, W)
PIS <- PIS_NIS[[1]]
NIS <- PIS_NIS[[2]]
Sr <- Similiraty_ratio(A_pond, PIS, NIS)
Ranking <- Ordenar(Sr, rownames(A))
Pesos_W <- data.frame(W)
colnames(Pesos_W) <- c("Pesos")
rownames(Pesos_W) <- Criterios
print(Pesos_W)
return(Ranking)
}Resultados: Selección del mejor modelo de regresión lineal múltiple
#Evaluación de la estrategia propuesta
Alternativas_E <- rownames(DM)
#-------------------------
Criterios_E <- c("CI", "R_2") #Nombre de los criterios
Atributo_E <- c(0, 1) #Asignación de atributo (0: Negativo, 1: Positivo)
Escenario_E <- c("Estadístico", "Estadístico") #Nota: Definir cuando se tienen criterios de diferente naturaleza
D_Matrix <- as.matrix(DM) #Matriz de decisión (Numérica)
Rankeo <- TOPSIS_improved_No2(Alternativas_E, Criterios_E, D_Matrix, Escenario_E, Atributo_E, 0.8, 'Base')## Modelos Ranking Dominancia
## 6 M705 1 0.995926259
## 5 M657 2 0.995924172
## 8 M801 3 0.988717243
## 2 M546 4 0.988179893
## 11 M898 5 0.500035707
## 9 M837 6 0.500024075
## 4 M644 7 0.500016603
## 7 M774 8 0.500006636
## 1 M541 9 0.499958527
## 3 M589 10 0.499957370
## 12 M962 11 0.011820107
## 10 M838 12 0.008948833
## Pesos
## CI 0.98817989
## R_2 0.01182011Modelo seleccionado por el método propuesto: “M705 / 3 variables predictoras”
## Altitude Mean_Annual_Air_Temp Mean_Monthly_Max_Temp Mean_Monthly_Min_Temp
## 705 0 0 0 0
## Mean_Annual_Wind_Speed Mean_Cloud_Clover Max_Monthly_Wind_Speed
## 705 0 0 1
## Max_Air_Temp Min_Air_Temp Max_Rainfall
## 705 1 0 1Estadísticos
column_name <- names(sf)[mol]
form <- as.formula(paste("Mean_Annual_Rainfall ~", paste(column_name, collapse = " + ")))
modl <- lm(form, data = data)
summary(modl)##
## Call:
## lm(formula = form, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -367.93 -66.39 2.86 71.58 313.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1123.0739 128.0119 8.773 < 0.0000000000000002 ***
## Max_Monthly_Wind_Speed -33.7169 6.8628 -4.913 0.00000117 ***
## Max_Air_Temp -36.0920 3.2055 -11.259 < 0.0000000000000002 ***
## Max_Rainfall 23.5142 0.6848 34.336 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 104.9 on 571 degrees of freedom
## Multiple R-squared: 0.786, Adjusted R-squared: 0.7849
## F-statistic: 699 on 3 and 571 DF, p-value: < 0.00000000000000022data_filter <- data %>% dplyr::select('Max_Monthly_Wind_Speed', 'Max_Air_Temp', 'Max_Rainfall', 'Mean_Annual_Rainfall')
# se selecionan las variables de interes
data_filter## # A tibble: 575 × 4
## Max_Monthly_Wind_Speed Max_Air_Temp Max_Rainfall Mean_Annual_Rainfall
## <dbl> <dbl> <dbl> <dbl>
## 1 2 32.5 39 755
## 2 3 32.3 36 820
## 3 3 32.4 32 759
## 4 2 30.2 43 919
## 5 2 33 43 790
## 6 2 32.2 33 794
## 7 3 31.6 37 657
## 8 3 29 40 915
## 9 3 32.5 41 862
## 10 2 33.5 31 526
## # ℹ 565 more rows##
## Call:
## lm(formula = Mean_Annual_Rainfall ~ ., data = data_filter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -367.93 -66.39 2.86 71.58 313.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1123.0739 128.0119 8.773 < 0.0000000000000002 ***
## Max_Monthly_Wind_Speed -33.7169 6.8628 -4.913 0.00000117 ***
## Max_Air_Temp -36.0920 3.2055 -11.259 < 0.0000000000000002 ***
## Max_Rainfall 23.5142 0.6848 34.336 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 104.9 on 571 degrees of freedom
## Multiple R-squared: 0.786, Adjusted R-squared: 0.7849
## F-statistic: 699 on 3 and 571 DF, p-value: < 0.00000000000000022error <- model_01$residuals
df_error <- data.frame(error = error)
plot_01 <- ggplot(data =df_error,aes(error)) + geom_histogram(binwidth = 50) + theme_light()
plot_01
##
## Asymptotic one-sample Kolmogorov-Smirnov test
##
## data: error
## D = 0.024644, p-value = 0.876
## alternative hypothesis: two-sided# pruebas de bondad de ajuste
library(fitdistrplus)
library(logspline)
descdist(error,discrete = F, boot = 1000)
## summary statistics
## ------
## min: -367.9258 max: 313.9372
## median: 2.856082
## mean: -0.0000000000000003898814
## estimated sd: 104.6452
## estimated skewness: -0.1655726
## estimated kurtosis: 3.555465parametros <- fitdist(error_t,"norm")
error_t_sim <- rnorm(nrow(df_error),parametros$estimate[1], parametros$estimate[2])
df_error_t <- data.frame(error = (error_t_sim))
# mean = -0.0000000000000003898814 , sd = 104.5541
ks.test(error, "pnorm", mean = -0.0000000000000003898814 , sd = 104.5541 )##
## Asymptotic one-sample Kolmogorov-Smirnov test
##
## data: error
## D = 0.024447, p-value = 0.882
## alternative hypothesis: two-sidedsimu_Mean_Annual_Rainfall <- function(Max_Monthly_Wind_Speed, Max_Air_Temp, Max_Rainfall){
b_interc <- 1123.0739
b_Max_Monthly_Wind_Speed <- -33.7169
b_Max_Air_Temp <- -36.0920
b_Max_Rainfall <- 23.5142
val <- b_interc + b_Max_Monthly_Wind_Speed + b_Max_Air_Temp + b_Max_Rainfall
aleato <- rnorm(1, -0.0000000000000003898814, 104.5541)
val <- val + aleato
return(val)
}## # A tibble: 1 × 4
## Max_Monthly_Wind_Speed Max_Air_Temp Max_Rainfall Mean_Annual_Rainfall
## <dbl> <dbl> <dbl> <dbl>
## 1 3 34.5 43 912n <- 1000
simul <- data.frame(temp = rep(NA,n))
for(k in 1:n){
simul$CE[k] <- simu_Mean_Annual_Rainfall (data_filter[150,1],data_filter[150,2])
}n <- 1000
df_salidas <- data.frame(id=1:n, sim=NA)
for(k in 1:n){
pos_Mean_Annual_Rainfall <- ceiling(
rnorm(1, -0.0000000000000003898814,104.5541))
Max_Monthly_Wind_Speed <- data_filter$Max_Monthly_Wind_Speed[pos_Mean_Annual_Rainfall]
Max_Air_Temp<-data_filter$Max_Air_Temp[pos_Mean_Annual_Rainfall]
Max_Rainfall<-data_filter$Max_Rainfall[pos_Mean_Annual_Rainfall]
salida_Mean_Annual_Rainfall <- simu_Mean_Annual_Rainfall(Monthly_Wind_Speed,Max_Air_Temp,Max_Rainfall)
df_salidas$sim[k] <- salida_Mean_Annual_Rainfall
}
plot_03 <- ggplot(data =df_salidas, aes(x = sim)) + geom_histogram(bins = 50) + theme_light()
plot_03
plot_03 <- ggplot(data =data_filter, aes(x = Mean_Annual_Rainfall)) + geom_histogram(bins = 60) + theme_light()
plot_03
data_sim <- data_filter
data_sim$simu_M_A_Rainfall<- NA
sim_Mean_Annual_Rainfall <- mapply(simu_Mean_Annual_Rainfall,data_sim$Max_Monthly_Wind_Speed,data_sim$Max_Air_Temp,data_sim$Max_Rainfall)
data_sim$simu_M_A_Rainfall<- sim_Mean_Annual_Rainfall
ty_data_sim <- data_sim %>% dplyr::select(c(Mean_Annual_Rainfall,simu_M_A_Rainfall)) %>% pivot_longer(cols = Mean_Annual_Rainfall:simu_M_A_Rainfall, names_to = "tipo", values_to = "Mean_Annual_Rainfall")
plot_03 <- ggplot(data =ty_data_sim,aes(Mean_Annual_Rainfall)) + geom_histogram(bins = 50) + facet_grid(.~tipo) + theme_light()
plot_03
plot_04 <- ggplot(data =data_sim) + geom_point(aes(x = Mean_Annual_Rainfall, y = simu_M_A_Rainfall )) + theme_light()
plot_04
breaks_vet <- sim_table$breaks
# Asegurarse de que los intervalos sean más amplios para cubrir todo el rango de valores
real_data <- hist(data_sim$Mean_Annual_Rainfall, breaks = seq(min(data_sim$Mean_Annual_Rainfall), max(data_sim$Mean_Annual_Rainfall), length.out = length(breaks_vet)))
##
## Pearson's Chi-squared test
##
## data: sim_table$counts and real_data$counts
## X-squared = 500.67, df = 572, p-value = 0.9855##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: sim_table$counts and real_data$counts
## D = 0.20968, p-value = 0.08577
## alternative hypothesis: two-sidedConclusión
De acuerdo con la prueba de Chi cuadrado las distribuciones de los datos simulados son similares de acuerdo con los datos reales. En este caso, el valor p es 0.9725, lo que indica que no hay suficiente evidencia para rechazar la hipótesis nula.