#EB landa ML gama ML s(t) h(t) در مدل اول

install.packages("actuar")
library(actuar)
my_func <- function(){
  
  #set.seed(123) # برای تولید نتایج تصادفی قطعی، همیشه باید از seed استفاده کنیم.
  shape1 <- 2
  shape2 <- 1
  n = 50
  m=30
  Rm=20
  
  y <- sort( rburr(n, shape1 , shape2))
  y
  x=y[1:m]
  x
  
  
  T1=0.5
  T2=1
  ds=0
  if(x[m]>T1 & x[m]<T2) {
    ds=m
    Rs=array(0,ds)
    Rs[m]=Rm
    Rsd1=0
    Rsd2=0
  }
  
  if(x[m]>T2 ) {
    ds=which.max(x[x<T2]) 
    x=x[1:ds]
    Rs=array(0,ds+1)
    Rsd1=0
    Rsd2=n-ds
    Rs[ds+1]=n-ds
    
  }
  
  
  
  if(x[m]<T1 ) {
    ds=which.max(y[y<T1]) 
    x=y[1:ds]
    Rs=array(0,ds+1)
    Rs[ds+1]=n-ds
    Rsd1=n-ds
    Rsd2=0
  }
  
  m=length(x)
  lamda=array(100)
  gama=array(100)
  gama[1]=1
  for (j in 1:99) {
    
    lamda[j]=ds/(sum(sapply(1:ds,function(i) (Rs[i]+1)*log(1+x[i]^gama[j])))+Rsd1*log(1+T1^gama[j])+Rsd2*log(1+T2^gama[j]))
    
    
    denom = (sum(sapply(1:ds,function(i) (lamda[j]*(Rs[i]+1)+1)*x[i]^gama[j]*log(x[i])/(1+x[i]^gama[j])))
             +lamda[j]*(Rsd1*T1^gama[j]*log(T1^gama[j])/(1+T1^gama[j])
                        +Rsd2*T2^gama[j]*log(T2^gama[j])/(1+T2^gama[j]))-sum(log(x[1:ds])))
    
    if(is.nan(denom)){
      denom = rnorm(1)
    }
    
    gama[j+1] = ds/denom
    
    if(abs(gama[j+1]-gama[j])<0.01) 
      break
    
  }
  
  gamahat=gama[length(gama)]
  
  
  lamdahat=ds/(sum(sapply(1:ds,function(i) (Rs[i]+1)*log(1+x[i]^gamahat)))+Rsd1*log(1+T1^gamahat)+Rsd2*log(1+T2^gamahat))
  shat=(1+0.5^gamahat)^(-lamdahat)
  hhat=lamdahat*gamahat*0.5^(gamahat-1)*(1+0.5^gamahat)^(-1)
  
  return(c(gamahat, lamdahat,shat,hhat))
  # return(gamahat)
  
}
my_func()
b=replicate(10000, my_func())
b
gam <- b[1,]
gam
lam <- b[2,]
lam
shaat=b[3,]
hhaat=b[4,]
# فیلتر کردن مقادیر lam که از صفر کمتر و از پنج بیشتر هستند
lam_filtered <- lam[lam >= 0 & lam <= 5]

# فیلتر کردن مقادیر gam که از صفر کمتر و از پنج بیشتر هستند
gam_filtered <- gam[gam >= 0 & gam <= 5]

# چاپ مقادیر فیلتر شده
print(lam_filtered)
print(gam_filtered)

# شمارش تعداد عناصر در gam_filtered
count_gam_filtered <- length(gam_filtered)

# شمارش تعداد عناصر در lam_filtered
count_lam_filtered <- length(lam_filtered)

# چاپ تعداد عناصر
print(paste("تعداد عناصر gam_filtered:", count_gam_filtered))
print(paste("تعداد عناصر lam_filtered:", count_lam_filtered))



a<- gam_filtered[-c(1:2000)]
a
c<- lam_filtered[-c(1:2000)]
c

actual_gamahat <- 1
actual_lamdahat <- 2

# محاسبه EB برای هر یک از پارامترها
EB_shaat <- mean(shaat) - actual_lamdahat
EB_hhaat <- mean(hhaat) - actual_gamahat
EB_a <- mean(a) - actual_gamahat
EB_c <- mean(c) - actual_lamdahat

# چاپ نتایج
print(paste("Expected Bias for shaat (EB_shaat):", EB_shaat))
print(paste("Expected Bias for hhaat (EB_hhaat):", EB_hhaat))
print(paste("Expected Bias for a (EB_a):", EB_a))
print(paste("Expected Bias for c (EB_c):", EB_c))
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Stack trace:
#0 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php(339): yii\web\Response-&gt;sendHeaders()
#1 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/ErrorHandler.php(136): yii\web\Response-&gt;send()
#2 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/ErrorHandler.php(135): yii\web\ErrorHandler-&gt;renderException()
#3 [internal function]: yii\base\ErrorHandler-&gt;handleException()
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yii\web\HeadersAlreadySentException: Headers already sent in  on line 0. in /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php:366
Stack trace:
#0 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php(339): yii\web\Response-&gt;sendHeaders()
#1 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Application.php(656): yii\web\Response-&gt;send()
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#9 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Application.php(386): yii\web\Application-&gt;handleRequest()
#10 /var/www/html/prof-homepages/frontend/web/index.php(18): yii\base\Application-&gt;run()
#11 {main}</pre>