mixglikilhood

H_2015 <- read.csv("2015.csv")
H_2016 <- read.csv("2016.csv")
H_2017 <- read.csv("2017.csv")
H_2018 <- read.csv("2018.csv")
H_2019 <- read.csv("2019.csv")

colnames(H_2018)[2] <- "Country"
colnames(H_2019)[2] <- "Country"

years <- c("H_2015", "H_2016", "H_2017", "H_2018", "H_2019")
for(i in 1:5){
  cyr <- get(years[i])
  df <- order(cyr$Country)
  cyr <- cyr[df, ]
  assign(years[i], cyr)
  
  
}

missing_list <- paste("missing_", 2015:2019, sep = "")

for(i in 1:5){
  temp <- 0
  for(j in 1:4){
    df <- years[i] == years
    yr <- years[!df]
    cyr <- get(years[i])
    cyr2 <- get(yr[j])
    missing <- is.element(cyr$Country,cyr2$Country)
    if(sum(!missing != 0)){
       misel <- cyr$Country[!missing]
       if(temp[1] == 0){
         temp <- misel
       }
       else{
         temp <- c(temp, misel)
       }
    }}
  assign(missing_list[i], unique(temp))}

for(i in 1:5){
  cyr <- get(years[i])
  missing <- is.element(cyr$Country, get(missing_list[i]))
  cyr <- cyr[!missing,]
  assign(years[i], cyr)
}

for(i in 1:3){
  cyr <- get(years[i])
  colnames(cyr)[4] <- "Score"
  assign(years[i], cyr)
}
H_years <- data.frame(H_2015$Country)
a <- 2
for(i in 1:5){
  cyr <- get(years[i])
  H_years[,a] <- cyr$Score
  a <- a+1
}
colnames(H_years) <- c("Country", 2015, 2016, 2017, 2018, 2019)

data <- H_2015[,6:11]

MixGHD

library(MixGHD)

## Warning: package 'MixGHD' was built under R version 4.0.5

## Loading required package: MASS

MGHD

mixg <- MGHD(data, G = 1:20, scale = FALSE)

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## Error in cov.wt(x, wt = abc[, 2] * weights, center = mu.new, method = "ML") : 
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## Error in cov.wt(x, wt = abc[, 2] * weights, center = mu.new, method = "ML") : 
##   weights must be non-negative and not all zero

## Warning in log(det(sigma)): NaNs produced

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## The best model (AIC) for the range of  components used is  G = 17.
## The AIC for this model is 1861.681.

mixg@loglik

##   [1]  462.6127 6181.2094  779.8619 5896.2777 1663.5374  875.4017  837.3008
##   [8]  872.1562 1181.1984  949.7673  981.2022 1012.6216 1254.0727 1004.8694
##  [15] 1016.2987 1026.6569 1037.6999 1046.1331 1054.4972 1064.2571 1073.2313
##  [22] 1080.5429 1087.6702 1094.6375 1101.4601 1108.1566 1114.7689 1121.5249
##  [29] 1130.3384 1247.0673 1115.4328 1125.3133 1133.0523 1140.3254 1146.8838
##  [36] 1153.1562 1159.3877 1165.7706 1172.4687 1179.0523 1184.8724 1190.4123
##  [43] 1195.9027 1201.4502 1206.9767 1212.2361 1217.5161 1222.9381 1227.9995
##  [50] 1232.8758 1237.6948 1242.4698 1247.2158 1251.9798 1256.9525 1262.2991
##  [57] 1267.0515 1271.5837 1276.0659 1280.5062 1284.9077 1289.2724 1293.6028
##  [64] 1297.9022 1302.1779 1306.4528 1310.8259 1315.6124 1320.2345 1324.3548
##  [71] 1328.4375 1332.4890 1336.5103 1340.5022 1344.4655 1348.4010 1352.3094
##  [78] 1356.1913 1360.0474 1363.8782 1367.6843 1371.4662 1375.2245 1378.9597
##  [85] 1382.6722 1386.3627 1390.0315 1393.6793 1397.3065 1400.9139 1404.5025
##  [92] 1408.0736 1411.6300 1415.1783 1418.7402 1422.4172 1426.7896 1432.4051
##  [99] 1436.2256 1439.8404