WinBUGS'da belirli bir değişken için tahmin nasıl alınır?

Yeni bir WinBUGS kullanıcısıyım ve yardımınız için bir sorum var. Aşağıdaki kodu çalıştırdıktan sonra, (istatistik, yoğunluk) beta0üzerinden parametreleri var beta4, ama kod modellemek için hayarladığım son değerinin tahmin almak için nasıl bilmiyorum NA.

Birisi bana bir ipucu verebilir mi? Herhangi bir tavsiye büyük mutluluk duyacağız.

model {
for(i in 1: N) {
CF01[i] ~ dnorm(0, 20)
CF02[i]  ~ dnorm(0, 1)
h[i] ~ dpois (lambda [i])
log(lambda [i]) <- beta0 + beta1*CF03[i] + beta2*CF02[i] + beta3*CF01[i] + beta4*IND[i]
}
beta0 ~ dnorm(0.0, 1.0E-6)
beta1 ~ dnorm(0.0, 1.0E-6)
beta2 ~ dnorm(0.0, 1.0E-6)
beta3 ~ dnorm(0.0, 1.0E-6)
beta4  <- log(p)
p ~ dunif(lower, upper)
}

INITS
list(beta0 = 0, beta1 = 0, beta2 = 0, beta3 = 0, p = 0.9)

DATA(LIST)
list(N = 154, lower = 0.80, upper = 0.95,

h = c(1, 4, 1, 2, 1, 2, 1, 1, 1, 3, 3, 0, 0, 0, 2, 0, 1, 0, 4, 2,
3, 0, 2, 1, 1, 2, 2, 2, 3, 4, 2, 3, 1, 0, 1, 3, 3, 3, 1, 0, 1,
0, 5, 2, 1, 2, 1, 3, 3, 1, 1, 0, 2, 2, 0, 3, 0, 0, 3, 2, 2, 2,
1, 0, 3, 3, 1, 1, 1, 2, 1, 0, 1, 2, 1, 2, 0, 2, 1, 0, 0, 2, 5,
0, 2, 1, 0, 2, 1, 2, 2, 2, 0, 3, 2, 1, 3, 3, 3, 3, 0, 1, 3, 3,
3, 1, 0, 0, 1, 2, 1, 0, 1, 4, 1, 1, 1, 1, 2, 1, 3, 0, 0, 1, 1,
1, 1, 0, 2, 1, 0, 0, 1, 1, 5, 1, 1, 1, 3, 0, 1, 1, 1, 0, 2, 1,
0, 3, 3, 0, 0, 1, 2, 6, NA),

CF03 = c(-1.575, 0.170, -1.040, -0.010, -0.750,
0.665, -0.250, 0.145, -0.345, -1.915, -1.515,
0.215, -1.040, -0.035, 0.805, -0.860, -1.775,
1.725, -1.345, 1.055, -1.935, -0.160, -0.075,
-1.305, 1.175, 0.130, -1.025, -0.630, 0.065,
-0.665, 0.415, -0.660, -1.145, 0.165, 0.955,
-0.920, 0.250, -0.365, 0.750, 0.045, -2.760,
-0.520, -0.095, 0.700, 0.155, -0.580, -0.970,
-0.685, -0.640, -0.900, -0.250, -1.355, -1.330,
0.440, -1.505, -1.715, -0.330, 1.375, -1.135,
-1.285, 0.605, 0.360, 0.705, 1.380, -2.385, -1.875,
-0.390, 0.770, 1.605, -0.430, -1.120, 1.575, 0.440,
-1.320, -0.540, -1.490, -1.815, -2.395, 0.305,
0.735, -0.790, -1.070, -1.085, -0.540, -0.935,
-0.790, 1.400, 0.310, -1.150, -0.725, -0.150,
-0.640, 2.040, -1.180, -0.235, -0.070, -0.500,
-0.750, -1.450, -0.235, -1.635, -0.460, -1.855,
-0.925, 0.075, 2.900, -0.820, -0.170, -0.355,
-0.170, 0.595, 0.655, 0.070, 0.330, 0.395, 1.165,
0.750, -0.275, -0.700, 0.880, -0.970, 1.155, 0.600,
-0.075, -1.120, 1.480, -1.255, 0.255, 0.725,
-1.230, -0.760, -0.380, -0.015, -1.005, -1.605,
0.435, -0.695, -1.995, 0.315, -0.385, -0.175,
-0.470, -1.215, 0.780, -1.860, -0.035, -2.700,
-1.055, 1.210, 0.600, -0.710, 0.425, 0.155, -0.525,
-0.565),

CF02 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0.38, 0.06, -0.94,
-0.02, -0.28, -0.78, -0.95, 2.33, 1.43, 1.24, 1.26,
-0.75, -1.5, -2.09, 1.01, -0.05, 2.48, 2.48, 0.46,
0.46, -0.2, -1.11, 0.52, -0.37, 0.58, 0.86, 0.59,
-0.12, -1.33, 1.4, -1.84, -1.4, -0.76, -0.23,
-1.78, -1.43, 1.2, 0.32, 1.87, 0.43, -1.71, -0.54,
-1.25, -1.01, -1.98, 0.52, -1.07, -0.44, -0.24,
-1.31, -2.14, -0.43, 2.47, -0.09, -1.32, -0.3,
-0.99, 1.1, 0.41, 1.01, -0.19, 0.45, -0.07, -1.41,
0.87, 0.68, 1.61, 0.36, -1.06, -0.44, -0.16, 0.72,
-0.69, -0.94, 0.11, 1.25, 0.33, -0.05, 0.87, -0.37,
-0.2, -2.22, 0.26, -0.53, -1.59, 0.04, 0.16, -2.66,
-0.21, -0.92, 0.25, -1.36, -1.62, 0.61, -0.2, 0,
1.14, 0.27, -0.64, 2.29, -0.56, -0.59, 0.44, -0.05,
0.56, 0.71, 0.32, -0.38, 0.01, -1.62, 1.74, 0.27, 0.97,
1.22, -0.21, -0.05, 1.15, 1.49, -0.15, 0.05, -0.87,
-0.3, -0.08, 0.5, 0.84, -1.67, 0.69, 0.47, 0.44,
-1.35, -0.24, -1.5, -1.32, -0.08, 0.76, -0.57,
-0.84, -1.11, 1.94, -0.68),

CF01 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, -0.117, -0.211, -0.333, -0.229, -0.272,
-0.243, -0.148, 0.191, -0.263, -0.239, -0.168,
-0.381, -0.512, -0.338, -0.296, 0.067, 0.104,
-0.254, -0.167, -0.526, -0.096, -0.43, 0.013,
-0.438, -0.297, -0.131, -0.098, -0.046, -0.063,
-0.194, -0.155, -0.645, -0.603, -0.374, -0.214,
-0.165, -0.509, -0.171, -0.442, -0.468, -0.289,
-0.427, -0.519, -0.454, 0.046, -0.275, -0.401,
-0.542, -0.488, -0.52, -0.018, -0.551, -0.444,
-0.254, -0.286, 0.048, -0.03, -0.015, -0.219,
-0.029, 0.059, 0.007, 0.157, 0.141, -0.035, 0.136,
0.526, 0.113, 0.22, -0.022, -0.173, 0.021, -0.027,
0.261, 0.082, -0.266, -0.284, -0.097, 0.097, -0.06,
0.397, 0.315, 0.302, -0.026, 0.268, -0.111, 0.084,
0.14, -0.073, 0.287, 0.061, 0.035, -0.022, -0.091,
-0.22, -0.021, -0.17, -0.184, 0.121, -0.192,
-0.24, -0.283, -0.003, -0.45, -0.138, -0.143,
0.017, -0.245, 0.003, 0.108, 0.015, -0.219, 0.09,
-0.22, -0.004, -0.178, 0.396, 0.204, 0.342, 0.079,
-0.034, -0.122, -0.24, -0.125, 0.382, 0.072, 0.294,
0.577, 0.4, 0.213, 0.359, 0.074, 0.388, 0.253, 0.167),

IND = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0))

Değişkeni hizlenecek parametreler listesine eklemeniz yeterlidir. R2WinBUGS gibi bir paket kullanıyorsanız , işleve bağımsız değişkene hiletilen listeye değişken ekleyin . Sonra (NA olanı) son değerine bakın - orada bir posterior dağılım elde edeceksiniz.parameters.to.savebugsh

Bayes çıkarımında tahminlerde bulunmanın olağan bir yoludur ( ayrıca bu soruya bakın ). Güzel ve basit! Artık parametre değerlendirme ve tahmin ayrımı yok. Her şey bir anda yapılır. Parametrelerin posterior dağılımı gerçek verilerle verilir ve NA değerlerine yayılır ("tahminler" olarak).