text: # 負二項分布 (Netative binomial distribution)
  
  (1)
  \begin{align} f(x) = {x-1 \choose r-1} (1-p)^{x-r} p^r \end{align}
  • 範圍：r=1,2,3,…. ; x= r, r+1, r+2, ….
  • 意義：要得到第 r 次成功所需要的試驗次數 x;
  • R 函數： nbinom(size, prob) ; r:size:成功數, p:prob:成功機率
  
  # 特性
  
  (2)
  \begin{eqnarray} 1. && E(X) = r/p \\ 2. && Var(X) = r (1-p)/p^2 = r q/p^2 \end{eqnarray}
  
  # 動差生成函數
  
  (3)
  \begin{align} m_x(t) = \frac{(pe^t)^r}{(1-(1-p) e^t)^r} = \frac{(p e^t)^r}{(1-q e^t)^r} \end{align}
  
  # R 程式範例
  
  > n=20; p=0.4; k=seq(0,50)
  > plot(k, dnbinom(k,n,p), type='h', main='dnbinom(k,n=20,p=0.4)', xlab='k')
  >
  
  > x = rnbinom(100000, 100, 0.8)
  > hist(x, nclass=max(x)-min(x)+1)
  >
  
  
  # R 程式範例 (進階)
  
  require(graphics)
  x <- 0:11
  dnbinom(x, size = 1, prob = 1/2) * 2^(1 + x) # == 1
  126 /  dnbinom(0:8, size  = 2, prob  = 1/2) #- theoretically integer
  
  ## Cumulative ('p') = Sum of discrete prob.s ('d');  Relative error :
  summary(1 - cumsum(dnbinom(x, size = 2, prob = 1/2)) /
  pnbinom(x, size  = 2, prob = 1/2))
  
  x <- 0:15
  size <- (1:20)/4
  persp(x,size, dnb <- outer(x, size, function(x,s) dnbinom(x,s, prob= 0.4)),
  xlab = "x", ylab = "s", zlab="density", theta = 150)
  title(tit <- "negative binomial density(x,s, pr = 0.4)  vs.  x & s")
  
  image  (x,size, log10(dnb), main= paste("log [",tit,"]"))
  
  ## Alternative parametrization
  x1 <- rnbinom(500, mu = 4, size = 1)
  x2 <- rnbinom(500, mu = 4, size = 10)
  x3 <- rnbinom(500, mu = 4, size = 100)
  h1 <- hist(x1, breaks = 20, plot = FALSE)
  h2 <- hist(x2, breaks = h1$breaks, plot = FALSE) h3 <- hist(x3, breaks = h1$breaks, plot = FALSE)
  barplot(rbind(h1$counts, h2$counts, h3$counts), beside = TRUE, col = c("red","blue","cyan"), names.arg = round(h1$breaks[-length(h1$breaks)]))  執行結果： > require(graphics) > x <- 0:11 > dnbinom(x, size = 1, prob = 1/2) * 2^(1 + x) # == 1 [1] 1 1 1 1 1 1 1 1 1 1 1 1 > 126 / dnbinom(0:8, size = 2, prob = 1/2) #- theoretically integer [1] 504.0 504.0 672.0 1008.0 1612.8 2688.0 4608.0 8064.0 14336.0 > > ## Cumulative ('p') = Sum of discrete prob.s ('d'); Relative error : > summary(1 - cumsum(dnbinom(x, size = 2, prob = 1/2)) / + pnbinom(x, size = 2, prob = 1/2)) Min. 1st Qu. Median Mean 3rd Qu. Max. -2.22e-16 -2.22e-16 -2.22e-16 -1.48e-16 0.00e+00 0.00e+00 > > x <- 0:15 > size <- (1:20)/4 > persp(x,size, dnb <- outer(x, size, function(x,s) dnbinom(x,s, prob= 0.4)), + xlab = "x", ylab = "s", zlab="density", theta = 150) > title(tit <- "negative binomial density(x,s, pr = 0.4) vs. x & s") > > image (x,size, log10(dnb), main= paste("log [",tit,"]")) > contour(x,size, log10(dnb),add=TRUE) > > ## Alternative parametrization > x1 <- rnbinom(500, mu = 4, size = 1) > x2 <- rnbinom(500, mu = 4, size = 10) > x3 <- rnbinom(500, mu = 4, size = 100) > h1 <- hist(x1, breaks = 20, plot = FALSE) > h2 <- hist(x2, breaks = h1$breaks, plot = FALSE)
  > h3 <- hist(x3, breaks = h1$breaks, plot = FALSE) > barplot(rbind(h1$counts, h2$counts, h3$counts),
  +         beside = TRUE, col = c("red","blue","cyan"),
  +         names.arg = round(h1$breaks[-length(h1$breaks)]))
explanation: The document provides a detailed explanation of the negative binomial distribution, including its formula, properties, and R code examples. It does not contain a specific question or a question-and-answer pair. Instead, it is a comprehensive guide or reference material on the topic.
distilabel_metadata.content: Evaluate the user given text extract and determine if it contains a math question and optionally and answer to this question.
  
  Output rules:
  
  1. Briefly describe the document and mention if it contains a question or a question and answer.
  2. Finalize the output with: Label: no-question,question,question-and-answer,multiple-question,multiple-question-and-answer
distilabel_metadata.role: system
distilabel_metadata.content: # 負二項分布 (Netative binomial distribution)
  
  (1)
  \begin{align} f(x) = {x-1 \choose r-1} (1-p)^{x-r} p^r \end{align}
  • 範圍：r=1,2,3,…. ; x= r, r+1, r+2, ….
  • 意義：要得到第 r 次成功所需要的試驗次數 x;
  • R 函數： nbinom(size, prob) ; r:size:成功數, p:prob:成功機率
  
  # 特性
  
  (2)
  \begin{eqnarray} 1. && E(X) = r/p \\ 2. && Var(X) = r (1-p)/p^2 = r q/p^2 \end{eqnarray}
  
  # 動差生成函數
  
  (3)
  \begin{align} m_x(t) = \frac{(pe^t)^r}{(1-(1-p) e^t)^r} = \frac{(p e^t)^r}{(1-q e^t)^r} \end{align}
  
  # R 程式範例
  
  > n=20; p=0.4; k=seq(0,50)
  > plot(k, dnbinom(k,n,p), type='h', main='dnbinom(k,n=20,p=0.4)', xlab='k')
  >
  
  > x = rnbinom(100000, 100, 0.8)
  > hist(x, nclass=max(x)-min(x)+1)
  >
  
  
  # R 程式範例 (進階)
  
  require(graphics)
  x <- 0:11
  dnbinom(x, size = 1, prob = 1/2) * 2^(1 + x) # == 1
  126 /  dnbinom(0:8, size  = 2, prob  = 1/2) #- theoretically integer
  
  ## Cumulative ('p') = Sum of discrete prob.s ('d');  Relative error :
  summary(1 - cumsum(dnbinom(x, size = 2, prob = 1/2)) /
  pnbinom(x, size  = 2, prob = 1/2))
  
  x <- 0:15
  size <- (1:20)/4
  persp(x,size, dnb <- outer(x, size, function(x,s) dnbinom(x,s, prob= 0.4)),
  xlab = "x", ylab = "s", zlab="density", theta = 150)
  title(tit <- "negative binomial density(x,s, pr = 0.4)  vs.  x & s")
  
  image  (x,size, log10(dnb), main= paste("log [",tit,"]"))
  
  ## Alternative parametrization
  x1 <- rnbinom(500, mu = 4, size = 1)
  x2 <- rnbinom(500, mu = 4, size = 10)
  x3 <- rnbinom(500, mu = 4, size = 100)
  h1 <- hist(x1, breaks = 20, plot = FALSE)
  h2 <- hist(x2, breaks = h1$breaks, plot = FALSE) h3 <- hist(x3, breaks = h1$breaks, plot = FALSE)
  barplot(rbind(h1$counts, h2$counts, h3$counts), beside = TRUE, col = c("red","blue","cyan"), names.arg = round(h1$breaks[-length(h1$breaks)]))  執行結果： > require(graphics) > x <- 0:11 > dnbinom(x, size = 1, prob = 1/2) * 2^(1 + x) # == 1 [1] 1 1 1 1 1 1 1 1 1 1 1 1 > 126 / dnbinom(0:8, size = 2, prob = 1/2) #- theoretically integer [1] 504.0 504.0 672.0 1008.0 1612.8 2688.0 4608.0 8064.0 14336.0 > > ## Cumulative ('p') = Sum of discrete prob.s ('d'); Relative error : > summary(1 - cumsum(dnbinom(x, size = 2, prob = 1/2)) / + pnbinom(x, size = 2, prob = 1/2)) Min. 1st Qu. Median Mean 3rd Qu. Max. -2.22e-16 -2.22e-16 -2.22e-16 -1.48e-16 0.00e+00 0.00e+00 > > x <- 0:15 > size <- (1:20)/4 > persp(x,size, dnb <- outer(x, size, function(x,s) dnbinom(x,s, prob= 0.4)), + xlab = "x", ylab = "s", zlab="density", theta = 150) > title(tit <- "negative binomial density(x,s, pr = 0.4) vs. x & s") > > image (x,size, log10(dnb), main= paste("log [",tit,"]")) > contour(x,size, log10(dnb),add=TRUE) > > ## Alternative parametrization > x1 <- rnbinom(500, mu = 4, size = 1) > x2 <- rnbinom(500, mu = 4, size = 10) > x3 <- rnbinom(500, mu = 4, size = 100) > h1 <- hist(x1, breaks = 20, plot = FALSE) > h2 <- hist(x2, breaks = h1$breaks, plot = FALSE)
  > h3 <- hist(x3, breaks = h1$breaks, plot = FALSE) > barplot(rbind(h1$counts, h2$counts, h3$counts),
  +         beside = TRUE, col = c("red","blue","cyan"),
  +         names.arg = round(h1$breaks[-length(h1$breaks)]))
distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The document provides a detailed explanation of the negative binomial distribution, including its formula, properties, and R code examples. It does not contain a specific question or a question-and-answer pair. Instead, it is a comprehensive guide or reference material on the topic.
  
  Label: no-question