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x n y | ||
2 1443 1346 | ||
3 694 577 | ||
4 455 337 | ||
5 353 208 | ||
6 272 149 | ||
7 256 136 | ||
8 240 111 | ||
9 217 69 | ||
10 200 67 | ||
11 237 75 | ||
12 202 52 | ||
13 192 46 | ||
14 174 54 | ||
15 167 28 | ||
16 201 27 | ||
17 195 31 | ||
18 191 33 | ||
19 147 20 | ||
20 152 24 |
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dis make count exitos | ||
0.28 100.0% 45198 45183 | ||
0.97 99.9% 183020 182899 | ||
1.93 99.5% 169503 168594 | ||
2.92 96.3% 113094 108953 | ||
3.93 87.7% 73855 64740 | ||
4.94 76.6% 53659 41106 | ||
5.94 65.6% 42991 28205 | ||
6.95 57.6% 37050 21334 | ||
7.95 49.9% 33275 16615 | ||
8.95 43.8% 30836 13503 | ||
9.95 38.6% 28637 11060 | ||
10.95 34.4% 26239 9032 | ||
11.95 31.2% 24636 7687 | ||
12.95 28.1% 22876 6432 | ||
14.43 23.8% 41267 9813 | ||
16.43 20.2% 35712 7196 | ||
18.44 16.8% 31573 5290 | ||
20.44 14.4% 28280 4086 | ||
21.95 12.4% 13238 1642 | ||
24.39 10.2% 46570 4767 | ||
28.40 7.8% 38422 2980 | ||
32.39 6.3% 31641 1996 | ||
36.39 5.2% 25604 1327 | ||
40.37 4.1% 20366 834 | ||
44.38 3.5% 15977 559 | ||
48.37 2.6% 11770 311 | ||
52.36 2.7% 8708 231 | ||
57.25 2.3% 8878 204 | ||
63.23 1.9% 5492 103 | ||
69.18 1.1% 3087 35 | ||
75.19 1.4% 1742 24 | ||
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data { | ||
int<lower=0> N; | ||
vector[N] h; | ||
vector[N] w; | ||
} | ||
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parameters { | ||
real alpha; | ||
real <lower=0> beta; | ||
real <lower=0> sigma; | ||
} | ||
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transformed parameters { | ||
vector[N] w_media; | ||
// determinístico dado parámetros | ||
w_media = alpha + beta * (h - 160); | ||
} | ||
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model { | ||
// partes no determinísticas | ||
w ~ normal(w_media, sigma); | ||
alpha ~ normal(60, 10); | ||
beta ~ normal(0, 1); | ||
sigma ~ normal(0, 20); | ||
} |
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data { | ||
int<lower=0> N; | ||
vector[N] w; | ||
array[N] int s; | ||
} | ||
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parameters { | ||
array[2] real alpha; | ||
real <lower=0> sigma; | ||
} | ||
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transformed parameters { | ||
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} | ||
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model { | ||
// modelo para peso | ||
w ~ normal(alpha[s], sigma); | ||
// también se puede escribir como | ||
// for (i in 1:N) { | ||
// w[i] ~ normal(alpha[s[i]], sigma); | ||
// } | ||
// iniciales | ||
alpha ~ normal(60, 10); | ||
sigma ~ normal(0, 20); | ||
} | ||
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data { | ||
int<lower=0> N; | ||
vector[N] w; | ||
vector[N] h; | ||
array[N] int s; | ||
} | ||
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transformed data { | ||
real h_media; | ||
h_media = mean(h); | ||
} | ||
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parameters { | ||
array[2] real alpha; | ||
array[2] real<lower=0> beta; | ||
real <lower=0> sigma; | ||
} | ||
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transformed parameters { | ||
array[N] real mu; | ||
for (i in 1:N) { | ||
mu[i] = alpha[s[i]] + beta[s[i]] * (h[i] - h_media); | ||
} | ||
} | ||
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model { | ||
// modelo para peso | ||
w ~ normal(mu, sigma); | ||
// también se puede escribir: | ||
//for (i in 1:N) { | ||
// w[i] ~ normal(mu[i], sigma); | ||
//} | ||
alpha ~ normal(60, 10); | ||
beta ~ normal(0, 1); | ||
sigma ~ normal(0, 20); | ||
} | ||
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generated quantities { | ||
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} |