Nowcasting
nowcast.RdFunction that uses the [rstan::sampling()] engine to generate nowcasts.
Usage
nowcast(
.disease_data,
onset_date,
report_date,
strata = NULL,
dist = c("NegativeBinomial", "Poisson"),
now = NULL,
units = NULL,
max_delay = Inf,
prior_only = FALSE,
proportion_reported = 1,
refresh = 250 * interactive(),
control = control_default(),
method = c("sampling", "variational"),
mu_degree = 1,
nu_degree = 1,
mu_is_constant = FALSE,
nu_is_constant = TRUE,
mu_sd_prior = "standard_normal",
nu_sd_prior = "standard_normal",
mu_sd_param_1 = 0,
mu_sd_param_2 = 0.1,
nu_sd_param_1 = 0,
nu_sd_param_2 = 0.1,
mu_0_mean_param_1 = "auto",
mu_0_mean_param_2 = 0.01,
mu_0_sd_param_1 = "auto",
mu_0_sd_param_2 = 0.01,
nu_0_mean_param_1 = 0,
nu_0_mean_param_2 = 0.01,
nu_0_sd_param_1 = 0,
nu_0_sd_param_2 = 0.01,
mu_0_mean_hyperprior = "standard_normal",
nu_0_mean_hyperprior = "standard_normal",
mu_0_sd_hyperprior = "standard_normal",
nu_0_sd_hyperprior = "standard_normal",
r_prior = "standard_normal",
r_param_1 = 0,
r_param_2 = 1,
...
)Arguments
- .disease_data
A time series of reporting data in aggregated line list format such that each row has a column for onset date, report date, and
- onset_date
In quotations, the name of the column of datatype
Datedesignating the date of case onset. e.g. "onset_week"- report_date
In quotations, the name of the column of datatype
Datedesignating the date of case report. e.g. "report_week"- strata
Character vector of names of the strata included in the data.
- dist
Distribution. Either "NegativeBinomial" or "Poisson"
- now
An object of datatype
Dateindicating the date at which to perform the nowcast.- units
Time scale of reporting. Options: "1 day", "1 week".
- max_delay
Maximum possible delay observed or considered for estimation of the delay distribution (numeric). Default: `Inf`
- prior_only
Boolean variable indicating whether to compute only the prior distribution
- proportion_reported
A decimal greater than 0 and less than or equal to 1 representing the proportion of all cases expected to be reported. Default: 1, e.g. 100 percent of all cases will eventually be reported. For asymptomatic diseases where not all cases will ever be reported, or for outbreaks in which severe under-reporting is expected, change this to less than 1.
- refresh
Refresh parameter for [rstan::sampling()]
- control
Control parameter for [rstan::sampling()]
- method
Fitting method either `sampling` (recommended for inference) or `variational` (recommended for testing). The `sampling` method calls [rstan::sampling()] while the `variational` calls [rstan::vb()]
- mu_degree
Integer. Degree of the epidemic trend. Default is 1.
- nu_degree
Integer. Degree of the delay trend. Default is 1.
- mu_is_constant
Logical. Indicates whether the epidemic trend is constant. Default is FALSE.
- nu_is_constant
Logical. Indicates whether the delay trend is constant. Default is TRUE.
- mu_sd_prior
Character. Prior for the epidemic trend error. Default is "normal".
- nu_sd_prior
Character. Prior for the delay trend error. Default is "normal".
- mu_sd_param_1
Numeric. First parameter for the epidemic trend error. Default is 0.0.
- mu_sd_param_2
Numeric. Second parameter for the epidemic trend error. Default is 1.0.
- nu_sd_param_1
Numeric. First parameter for the delay trend error. Default is 0.0.
- nu_sd_param_2
Numeric. Second parameter for the delay trend error. Default is 1.0.
- mu_0_mean_param_1
Character. Prior for the initial epidemic trend mean's first parameter.
- mu_0_mean_param_2
Character. Prior for the initial epidemic trend mean's second parameter.
- mu_0_sd_param_1
Character. Prior for the initial epidemic trend standard deviation's first parameter.
- mu_0_sd_param_2
Character. Prior for the initial epidemic trend standard deviation's second parameter.
- nu_0_mean_param_1
Character. Prior for the initial delay trend mean's first parameter.
- nu_0_mean_param_2
Character. Prior for the initial delay trend mean's second parameter.
- nu_0_sd_param_1
Character. Prior for the initial delay trend standard deviation's first parameter.
- nu_0_sd_param_2
Character. Prior for the initial delay trend standard deviation's second parameter.
- mu_0_mean_hyperprior
Prior distribution for the mean of the epidemic trend. Default is normal.
- nu_0_mean_hyperprior
Prior distribution for the mean of the delay trend. Default is normal.
- mu_0_sd_hyperprior
Prior distribution for the standard deviation of the epidemic trend. Default is normal (truncated).
- nu_0_sd_hyperprior
Prior distribution for the standard deviation of the delay trend. Default is normal (truncated).
- r_prior
Character. Prior for the negative binomial precision parameter. Default is "normal".
- r_param_1
Numeric. First parameter for the dispersion prior if negative binomial. Default is 0.0.
- r_param_2
Numeric. Second parameter for the dispersion prior if negative binomial. Default is 1.0.
- ...
Additional arguments to pass to [rstan::sampling()]
Examples
# Load the data
data(denguedat)
# Create a fake disease process
sims <- simulate_process_for_testing()
# Run a nowcast with very few iterations
# change to 4 chains and 2000 iter when doing inference
nowcast(sims, "onset_date", "report_date", iter = 100, chains = 1, seed = 2524)
#> ℹ Computing a nowcast for 2024-09-17 per "days"
#> ℹ Assuming data is count-data where counts are in column `n`. To change this set `data_type = "linelist"`
#> Warning: The largest R-hat is 1.27, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> $data
#> $data$stan_data
#> $data$stan_data$num_steps
#> [1] 10
#>
#> $data$stan_data$num_delays
#> [1] 8
#>
#> $data$stan_data$num_strata
#> [1] 1
#>
#> $data$stan_data$n_rows
#> [1] 52
#>
#> $data$stan_data$N_cases
#> n .tval .delay .strata
#> [1,] 913 1 1 1
#> [2,] 753 1 2 1
#> [3,] 61 1 3 1
#> [4,] 7 1 4 1
#> [5,] 158 1 5 1
#> [6,] 135 1 6 1
#> [7,] 205 1 7 1
#> [8,] 283 1 8 1
#> [9,] 218 2 1 1
#> [10,] 45 2 2 1
#> [11,] 194 2 3 1
#> [12,] 271 2 4 1
#> [13,] 397 2 5 1
#> [14,] 88 2 6 1
#> [15,] 177 2 7 1
#> [16,] 275 2 8 1
#> [17,] 356 3 1 1
#> [18,] 199 3 2 1
#> [19,] 700 3 3 1
#> [20,] 703 3 4 1
#> [21,] 55 3 5 1
#> [22,] 233 3 6 1
#> [23,] 0 3 7 1
#> [24,] 90 3 8 1
#> [25,] 359 4 1 1
#> [26,] 13 4 2 1
#> [27,] 68 4 3 1
#> [28,] 173 4 4 1
#> [29,] 56 4 5 1
#> [30,] 1 4 6 1
#> [31,] 133 4 7 1
#> [32,] 17 5 1 1
#> [33,] 30 5 2 1
#> [34,] 52 5 3 1
#> [35,] 6 5 4 1
#> [36,] 82 5 5 1
#> [37,] 4 5 6 1
#> [38,] 71 6 1 1
#> [39,] 969 6 2 1
#> [40,] 148 6 3 1
#> [41,] 252 6 4 1
#> [42,] 1308 6 5 1
#> [43,] 106 7 1 1
#> [44,] 28 7 2 1
#> [45,] 342 7 3 1
#> [46,] 130 7 4 1
#> [47,] 497 8 1 1
#> [48,] 77 8 2 1
#> [49,] 201 8 3 1
#> [50,] 103 9 1 1
#> [51,] 172 9 2 1
#> [52,] 1198 10 1 1
#>
#> $data$stan_data$prior_only
#> [1] 0
#>
#> $data$stan_data$mu_degree
#> [1] 1
#>
#> $data$stan_data$nu_degree
#> [1] 1
#>
#> $data$stan_data$mu_is_constant
#> [1] FALSE
#>
#> $data$stan_data$nu_is_constant
#> [1] TRUE
#>
#> $data$stan_data$is_negative_binomial
#> [1] 1
#>
#> $data$stan_data$mu_sd_prior
#> [1] 1
#>
#> $data$stan_data$nu_sd_prior
#> [1] 1
#>
#> $data$stan_data$r_prior
#> [1] 1
#>
#> $data$stan_data$r_param_1
#> [1] 0
#>
#> $data$stan_data$r_param_2
#> [1] 1
#>
#> $data$stan_data$mu_sd_param_1
#> [1] 0
#>
#> $data$stan_data$mu_sd_param_2
#> [1] 0.1
#>
#> $data$stan_data$nu_sd_param_1
#> [1] 0
#>
#> $data$stan_data$nu_sd_param_2
#> [1] 0.1
#>
#> $data$stan_data$mu_0_mean_param_1
#> [1] 4.739691
#>
#> $data$stan_data$mu_0_mean_param_2
#> [1] 0.01
#>
#> $data$stan_data$mu_0_sd_param_1
#> [1] 1.550358
#>
#> $data$stan_data$mu_0_sd_param_2
#> [1] 0.01
#>
#> $data$stan_data$nu_0_mean_param_1
#> [1] 0
#>
#> $data$stan_data$nu_0_mean_param_2
#> [1] 0.01
#>
#> $data$stan_data$nu_0_sd_param_1
#> [1] 0
#>
#> $data$stan_data$nu_0_sd_param_2
#> [1] 0.01
#>
#> $data$stan_data$mu_0_mean_hyperprior
#> [1] 1
#>
#> $data$stan_data$nu_0_mean_hyperprior
#> [1] 1
#>
#> $data$stan_data$mu_0_sd_hyperprior
#> [1] 1
#>
#> $data$stan_data$nu_0_sd_hyperprior
#> [1] 1
#>
#>
#> $data$preprocessed_data
#> # A tibble: 52 × 5
#> onset_date .delay report_date n .tval
#> <date> <dbl> <date> <dbl> <dbl>
#> 1 2024-09-08 0 2024-09-08 913 1
#> 2 2024-09-08 1 2024-09-09 753 1
#> 3 2024-09-08 2 2024-09-10 61 1
#> 4 2024-09-08 3 2024-09-11 7 1
#> 5 2024-09-08 4 2024-09-12 158 1
#> 6 2024-09-08 5 2024-09-13 135 1
#> 7 2024-09-08 6 2024-09-14 205 1
#> 8 2024-09-08 7 2024-09-15 283 1
#> 9 2024-09-09 0 2024-09-09 218 2
#> 10 2024-09-09 1 2024-09-10 45 2
#> # ℹ 42 more rows
#>
#> $data$call_parameters
#> $data$call_parameters$onset_date
#> [1] "onset_date"
#>
#> $data$call_parameters$report_date
#> [1] "report_date"
#>
#> $data$call_parameters$strata
#> NULL
#>
#> $data$call_parameters$now
#> [1] "2024-09-17"
#>
#> $data$call_parameters$units
#> [1] "days"
#>
#> $data$call_parameters$max_delay
#> [1] Inf
#>
#>
#>
#> $dict
#> $dict$strata_dict
#> # A tibble: 1 × 2
#> .strata_unified .strata
#> <chr> <dbl>
#> 1 No strata 1
#>
#>
#> $generated_quantities
#> Inference for Stan model: generated_quantities.
#> 1 chains, each with iter=50; warmup=0; thin=1;
#> post-warmup draws per chain=50, total post-warmup draws=50.
#>
#> mean se_mean sd 2.5% 25% 50% 75%
#> N_mat_predict[1,1] 913.00 NaN 0.00 913.00 913.00 913.0 913.00
#> N_mat_predict[1,2] 753.00 NaN 0.00 753.00 753.00 753.0 753.00
#> N_mat_predict[1,3] 61.00 NaN 0.00 61.00 61.00 61.0 61.00
#> N_mat_predict[1,4] 7.00 NaN 0.00 7.00 7.00 7.0 7.00
#> N_mat_predict[1,5] 158.00 NaN 0.00 158.00 158.00 158.0 158.00
#> N_mat_predict[1,6] 135.00 NaN 0.00 135.00 135.00 135.0 135.00
#> N_mat_predict[1,7] 205.00 NaN 0.00 205.00 205.00 205.0 205.00
#> N_mat_predict[1,8] 283.00 NaN 0.00 283.00 283.00 283.0 283.00
#> N_mat_predict[2,1] 218.00 NaN 0.00 218.00 218.00 218.0 218.00
#> N_mat_predict[2,2] 45.00 NaN 0.00 45.00 45.00 45.0 45.00
#> N_mat_predict[2,3] 194.00 NaN 0.00 194.00 194.00 194.0 194.00
#> N_mat_predict[2,4] 271.00 NaN 0.00 271.00 271.00 271.0 271.00
#> N_mat_predict[2,5] 397.00 NaN 0.00 397.00 397.00 397.0 397.00
#> N_mat_predict[2,6] 88.00 NaN 0.00 88.00 88.00 88.0 88.00
#> N_mat_predict[2,7] 177.00 NaN 0.00 177.00 177.00 177.0 177.00
#> N_mat_predict[2,8] 275.00 NaN 0.00 275.00 275.00 275.0 275.00
#> N_mat_predict[3,1] 356.00 NaN 0.00 356.00 356.00 356.0 356.00
#> N_mat_predict[3,2] 199.00 NaN 0.00 199.00 199.00 199.0 199.00
#> N_mat_predict[3,3] 700.00 NaN 0.00 700.00 700.00 700.0 700.00
#> N_mat_predict[3,4] 703.00 NaN 0.00 703.00 703.00 703.0 703.00
#> N_mat_predict[3,5] 55.00 NaN 0.00 55.00 55.00 55.0 55.00
#> N_mat_predict[3,6] 233.00 NaN 0.00 233.00 233.00 233.0 233.00
#> N_mat_predict[3,7] 0.00 NaN 0.00 0.00 0.00 0.0 0.00
#> N_mat_predict[3,8] 90.00 NaN 0.00 90.00 90.00 90.0 90.00
#> N_mat_predict[4,1] 359.00 NaN 0.00 359.00 359.00 359.0 359.00
#> N_mat_predict[4,2] 13.00 NaN 0.00 13.00 13.00 13.0 13.00
#> N_mat_predict[4,3] 68.00 NaN 0.00 68.00 68.00 68.0 68.00
#> N_mat_predict[4,4] 173.00 NaN 0.00 173.00 173.00 173.0 173.00
#> N_mat_predict[4,5] 56.00 NaN 0.00 56.00 56.00 56.0 56.00
#> N_mat_predict[4,6] 1.00 NaN 0.00 1.00 1.00 1.0 1.00
#> N_mat_predict[4,7] 133.00 NaN 0.00 133.00 133.00 133.0 133.00
#> N_mat_predict[4,8] 220.94 36.04 245.34 3.13 55.50 129.5 251.50
#> N_mat_predict[5,1] 17.00 NaN 0.00 17.00 17.00 17.0 17.00
#> N_mat_predict[5,2] 30.00 NaN 0.00 30.00 30.00 30.0 30.00
#> N_mat_predict[5,3] 52.00 NaN 0.00 52.00 52.00 52.0 52.00
#> N_mat_predict[5,4] 6.00 NaN 0.00 6.00 6.00 6.0 6.00
#> N_mat_predict[5,5] 82.00 NaN 0.00 82.00 82.00 82.0 82.00
#> N_mat_predict[5,6] 4.00 NaN 0.00 4.00 4.00 4.0 4.00
#> N_mat_predict[5,7] 127.52 24.84 174.80 4.45 34.75 88.0 139.00
#> N_mat_predict[5,8] 245.06 54.80 437.64 15.68 63.75 110.0 203.00
#> N_mat_predict[6,1] 71.00 NaN 0.00 71.00 71.00 71.0 71.00
#> N_mat_predict[6,2] 969.00 NaN 0.00 969.00 969.00 969.0 969.00
#> N_mat_predict[6,3] 148.00 NaN 0.00 148.00 148.00 148.0 148.00
#> N_mat_predict[6,4] 252.00 NaN 0.00 252.00 252.00 252.0 252.00
#> N_mat_predict[6,5] 1308.00 NaN 0.00 1308.00 1308.00 1308.0 1308.00
#> N_mat_predict[6,6] 163.42 37.72 292.28 2.22 30.00 75.0 158.50
#> N_mat_predict[6,7] 267.48 59.63 384.60 5.45 69.25 141.0 244.25
#> N_mat_predict[6,8] 144.14 20.40 163.55 3.58 32.25 97.5 187.50
#> N_mat_predict[7,1] 106.00 NaN 0.00 106.00 106.00 106.0 106.00
#> N_mat_predict[7,2] 28.00 NaN 0.00 28.00 28.00 28.0 28.00
#> N_mat_predict[7,3] 342.00 NaN 0.00 342.00 342.00 342.0 342.00
#> N_mat_predict[7,4] 130.00 NaN 0.00 130.00 130.00 130.0 130.00
#> N_mat_predict[7,5] 483.58 78.98 613.16 28.22 95.75 194.5 603.00
#> N_mat_predict[7,6] 159.94 23.81 204.02 3.00 23.00 71.0 191.25
#> N_mat_predict[7,7] 249.90 68.14 501.54 11.58 58.25 123.5 191.25
#> N_mat_predict[7,8] 266.82 50.88 369.15 3.68 53.75 145.0 335.50
#> N_mat_predict[8,1] 497.00 NaN 0.00 497.00 497.00 497.0 497.00
#> N_mat_predict[8,2] 77.00 NaN 0.00 77.00 77.00 77.0 77.00
#> N_mat_predict[8,3] 201.00 NaN 0.00 201.00 201.00 201.0 201.00
#> N_mat_predict[8,4] 446.18 187.18 1314.64 8.45 54.50 167.5 311.25
#> N_mat_predict[8,5] 407.42 56.30 489.71 14.35 97.50 201.5 513.50
#> N_mat_predict[8,6] 110.60 19.15 106.84 4.45 34.50 68.0 185.50
#> N_mat_predict[8,7] 280.92 48.74 352.73 8.48 83.25 137.0 344.00
#> N_mat_predict[8,8] 335.22 73.60 597.48 13.00 52.50 103.5 373.25
#> N_mat_predict[9,1] 103.00 NaN 0.00 103.00 103.00 103.0 103.00
#> N_mat_predict[9,2] 172.00 NaN 0.00 172.00 172.00 172.0 172.00
#> N_mat_predict[9,3] 317.90 49.05 392.84 12.25 65.50 127.0 463.50
#> N_mat_predict[9,4] 452.00 138.14 997.95 19.00 70.75 185.5 388.50
#> N_mat_predict[9,5] 415.84 55.63 371.83 23.35 117.50 360.0 558.75
#> N_mat_predict[9,6] 153.42 30.00 197.14 3.45 30.00 67.0 238.25
#> N_mat_predict[9,7] 234.74 37.11 279.52 10.68 51.50 171.0 296.00
#> N_mat_predict[9,8] 237.34 44.98 272.47 5.00 58.00 135.0 312.00
#> N_mat_predict[10,1] 1198.00 NaN 0.00 1198.00 1198.00 1198.0 1198.00
#> N_mat_predict[10,2] 265.92 59.24 380.11 8.22 57.25 148.0 290.75
#> N_mat_predict[10,3] 405.04 79.90 557.62 7.68 81.50 239.5 494.75
#> N_mat_predict[10,4] 351.02 92.08 740.14 3.15 48.50 126.0 268.00
#> N_mat_predict[10,5] 438.14 85.25 611.08 4.68 74.25 281.5 577.50
#> N_mat_predict[10,6] 158.76 41.45 272.77 2.45 19.25 58.0 191.00
#> N_mat_predict[10,7] 204.10 44.05 268.90 1.45 31.50 91.0 247.00
#> N_mat_predict[10,8] 222.24 37.29 281.67 7.00 67.75 157.0 267.25
#> N_predict[1,1] 2515.00 NaN 0.00 2515.00 2515.00 2515.0 2515.00
#> N_predict[2,1] 1665.00 NaN 0.00 1665.00 1665.00 1665.0 1665.00
#> N_predict[3,1] 2336.00 NaN 0.00 2336.00 2336.00 2336.0 2336.00
#> N_predict[4,1] 1023.94 36.04 245.34 806.12 858.50 932.5 1054.50
#> N_predict[5,1] 563.58 56.85 459.06 223.73 318.25 417.5 601.50
#> N_predict[6,1] 3323.04 68.33 547.48 2845.45 2961.00 3098.5 3519.00
#> N_predict[7,1] 1766.24 111.91 993.62 808.92 1156.75 1542.5 1927.50
#> N_predict[8,1] 2355.34 276.95 1679.43 1254.43 1540.75 1875.5 2365.00
#> N_predict[9,1] 2086.24 137.59 1144.80 758.28 1336.25 1915.5 2466.00
#> N_predict[10,1] 3243.22 225.80 1714.68 1718.90 2362.00 2999.0 3603.50
#> 97.5% n_eff Rhat
#> N_mat_predict[1,1] 913.00 NaN NaN
#> N_mat_predict[1,2] 753.00 NaN NaN
#> N_mat_predict[1,3] 61.00 NaN NaN
#> N_mat_predict[1,4] 7.00 NaN NaN
#> N_mat_predict[1,5] 158.00 NaN NaN
#> N_mat_predict[1,6] 135.00 NaN NaN
#> N_mat_predict[1,7] 205.00 NaN NaN
#> N_mat_predict[1,8] 283.00 NaN NaN
#> N_mat_predict[2,1] 218.00 NaN NaN
#> N_mat_predict[2,2] 45.00 NaN NaN
#> N_mat_predict[2,3] 194.00 NaN NaN
#> N_mat_predict[2,4] 271.00 NaN NaN
#> N_mat_predict[2,5] 397.00 NaN NaN
#> N_mat_predict[2,6] 88.00 NaN NaN
#> N_mat_predict[2,7] 177.00 NaN NaN
#> N_mat_predict[2,8] 275.00 NaN NaN
#> N_mat_predict[3,1] 356.00 NaN NaN
#> N_mat_predict[3,2] 199.00 NaN NaN
#> N_mat_predict[3,3] 700.00 NaN NaN
#> N_mat_predict[3,4] 703.00 NaN NaN
#> N_mat_predict[3,5] 55.00 NaN NaN
#> N_mat_predict[3,6] 233.00 NaN NaN
#> N_mat_predict[3,7] 0.00 NaN NaN
#> N_mat_predict[3,8] 90.00 NaN NaN
#> N_mat_predict[4,1] 359.00 NaN NaN
#> N_mat_predict[4,2] 13.00 NaN NaN
#> N_mat_predict[4,3] 68.00 NaN NaN
#> N_mat_predict[4,4] 173.00 NaN NaN
#> N_mat_predict[4,5] 56.00 NaN NaN
#> N_mat_predict[4,6] 1.00 NaN NaN
#> N_mat_predict[4,7] 133.00 NaN NaN
#> N_mat_predict[4,8] 842.35 46 0.98
#> N_mat_predict[5,1] 17.00 NaN NaN
#> N_mat_predict[5,2] 30.00 NaN NaN
#> N_mat_predict[5,3] 52.00 NaN NaN
#> N_mat_predict[5,4] 6.00 NaN NaN
#> N_mat_predict[5,5] 82.00 NaN NaN
#> N_mat_predict[5,6] 4.00 NaN NaN
#> N_mat_predict[5,7] 630.60 50 0.98
#> N_mat_predict[5,8] 1515.90 64 1.04
#> N_mat_predict[6,1] 71.00 NaN NaN
#> N_mat_predict[6,2] 969.00 NaN NaN
#> N_mat_predict[6,3] 148.00 NaN NaN
#> N_mat_predict[6,4] 252.00 NaN NaN
#> N_mat_predict[6,5] 1308.00 NaN NaN
#> N_mat_predict[6,6] 860.67 60 0.99
#> N_mat_predict[6,7] 1530.50 42 0.99
#> N_mat_predict[6,8] 557.45 64 0.98
#> N_mat_predict[7,1] 106.00 NaN NaN
#> N_mat_predict[7,2] 28.00 NaN NaN
#> N_mat_predict[7,3] 342.00 NaN NaN
#> N_mat_predict[7,4] 130.00 NaN NaN
#> N_mat_predict[7,5] 1990.27 60 0.98
#> N_mat_predict[7,6] 768.42 73 1.01
#> N_mat_predict[7,7] 1023.32 54 1.01
#> N_mat_predict[7,8] 841.85 53 0.98
#> N_mat_predict[8,1] 497.00 NaN NaN
#> N_mat_predict[8,2] 77.00 NaN NaN
#> N_mat_predict[8,3] 201.00 NaN NaN
#> N_mat_predict[8,4] 1972.87 49 1.00
#> N_mat_predict[8,5] 1889.35 76 0.98
#> N_mat_predict[8,6] 336.70 31 0.98
#> N_mat_predict[8,7] 1119.05 52 1.03
#> N_mat_predict[8,8] 1452.20 66 1.02
#> N_mat_predict[9,1] 103.00 NaN NaN
#> N_mat_predict[9,2] 172.00 NaN NaN
#> N_mat_predict[9,3] 1350.22 64 1.04
#> N_mat_predict[9,4] 2089.15 52 1.05
#> N_mat_predict[9,5] 1415.15 45 1.02
#> N_mat_predict[9,6] 582.68 43 1.00
#> N_mat_predict[9,7] 819.55 57 0.98
#> N_mat_predict[9,8] 1014.60 37 1.00
#> N_mat_predict[10,1] 1198.00 NaN NaN
#> N_mat_predict[10,2] 1478.87 41 1.01
#> N_mat_predict[10,3] 1447.38 49 0.99
#> N_mat_predict[10,4] 2946.47 65 1.04
#> N_mat_predict[10,5] 1398.65 51 0.98
#> N_mat_predict[10,6] 777.22 43 1.00
#> N_mat_predict[10,7] 974.18 37 0.98
#> N_mat_predict[10,8] 1014.17 57 0.98
#> N_predict[1,1] 2515.00 NaN NaN
#> N_predict[2,1] 1665.00 NaN NaN
#> N_predict[3,1] 2336.00 NaN NaN
#> N_predict[4,1] 1645.35 46 0.98
#> N_predict[5,1] 1773.47 65 1.03
#> N_predict[6,1] 4864.50 64 0.98
#> N_predict[7,1] 3903.22 79 0.98
#> N_predict[8,1] 7184.50 37 1.03
#> N_predict[9,1] 4363.40 69 1.04
#> N_predict[10,1] 6730.38 58 0.99
#>
#> Samples were drawn using at Wed Sep 18 20:20:27 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
#>
#> $model
#> Inference for Stan model: nowcast.
#> 1 chains, each with iter=100; warmup=50; thin=1;
#> post-warmup draws per chain=50, total post-warmup draws=50.
#>
#> mean se_mean sd 2.5% 25% 50% 75%
#> mu_0_centered[1,1] 0.42 0.14 1.07 -1.33 -0.47 0.66 1.21
#> mu_0_centered[2,1] 0.06 0.11 1.05 -1.79 -0.57 0.06 0.78
#> mu_0_centered[3,1] 0.05 0.17 1.11 -2.41 -0.70 0.11 0.86
#> mu_0_centered[4,1] 0.12 0.10 0.96 -1.69 -0.52 0.05 0.73
#> mu_0_centered[5,1] 0.17 0.11 1.00 -1.40 -0.52 0.10 0.96
#> mu_0_centered[6,1] -0.18 0.11 0.99 -2.09 -0.70 -0.30 0.16
#> mu_0_centered[7,1] 0.01 0.09 0.72 -1.05 -0.49 0.02 0.42
#> mu_0_centered[8,1] 0.30 0.12 1.05 -1.77 -0.43 0.32 1.06
#> nu_0_centered[1,1] 0.63 0.10 0.87 -0.73 0.03 0.61 1.26
#> nu_0_centered[2,1] 0.20 0.12 1.04 -1.98 -0.64 0.41 0.91
#> nu_0_centered[3,1] 0.13 0.09 0.81 -1.66 -0.38 0.10 0.60
#> nu_0_centered[4,1] 0.04 0.09 0.85 -1.78 -0.53 0.04 0.47
#> nu_0_centered[5,1] 0.43 0.10 0.89 -1.33 -0.10 0.59 1.09
#> nu_0_centered[6,1] -0.32 0.10 0.96 -2.35 -1.10 -0.23 0.22
#> nu_0_centered[7,1] -0.12 0.10 0.91 -1.69 -0.64 0.00 0.43
#> nu_0_centered[8,1] 0.07 0.08 0.75 -1.21 -0.65 0.11 0.65
#> mu_0_mean 2.59 0.08 0.74 1.43 2.08 2.53 3.05
#> nu_0_mean 2.61 0.08 0.76 1.09 2.06 2.81 3.04
#> mu_0_sd 0.25 0.04 0.25 0.00 0.09 0.19 0.33
#> nu_0_sd 0.41 0.07 0.38 0.00 0.15 0.29 0.65
#> mu_sd 0.25 0.03 0.19 0.02 0.10 0.21 0.34
#> nu_sd 0.97 0.09 0.82 0.03 0.43 0.71 1.41
#> xi_mu_centered[1,1,1] 0.10 0.11 0.99 -1.69 -0.61 0.08 0.76
#> xi_mu_centered[1,2,1] -0.04 0.10 0.89 -1.75 -0.78 0.02 0.64
#> xi_mu_centered[1,3,1] 0.20 0.10 0.89 -1.22 -0.41 0.11 0.88
#> xi_mu_centered[1,4,1] 0.22 0.12 1.08 -1.79 -0.57 0.32 0.99
#> xi_mu_centered[1,5,1] 0.19 0.11 0.98 -1.38 -0.43 0.15 0.82
#> xi_mu_centered[1,6,1] -0.20 0.14 1.07 -2.50 -0.70 -0.16 0.60
#> xi_mu_centered[1,7,1] -0.07 0.11 1.05 -2.16 -0.73 -0.02 0.55
#> xi_mu_centered[1,8,1] 0.05 0.09 0.84 -1.64 -0.39 0.12 0.49
#> xi_mu_centered[2,1,1] 0.26 0.10 0.87 -1.29 -0.22 0.30 0.91
#> xi_mu_centered[2,2,1] -0.03 0.09 0.79 -1.29 -0.61 -0.16 0.57
#> xi_mu_centered[2,3,1] 0.22 0.09 0.85 -1.11 -0.38 0.24 0.75
#> xi_mu_centered[2,4,1] 0.12 0.10 0.94 -1.38 -0.71 0.15 0.84
#> xi_mu_centered[2,5,1] 0.15 0.08 0.70 -1.12 -0.29 0.15 0.67
#> xi_mu_centered[2,6,1] -0.19 0.11 0.99 -2.08 -0.92 -0.15 0.52
#> xi_mu_centered[2,7,1] -0.08 0.10 0.90 -1.59 -0.73 -0.17 0.60
#> xi_mu_centered[2,8,1] -0.16 0.10 0.95 -1.88 -0.78 -0.12 0.38
#> xi_mu_centered[3,1,1] 0.10 0.10 0.89 -1.30 -0.58 0.02 0.80
#> xi_mu_centered[3,2,1] 0.00 0.09 0.74 -1.23 -0.50 -0.02 0.44
#> xi_mu_centered[3,3,1] -0.31 0.10 0.92 -1.98 -0.93 -0.19 0.35
#> xi_mu_centered[3,4,1] -0.24 0.10 0.90 -2.11 -0.82 -0.11 0.34
#> xi_mu_centered[3,5,1] 0.48 0.11 0.91 -0.95 -0.12 0.36 1.05
#> xi_mu_centered[3,6,1] -0.27 0.10 0.93 -1.81 -0.86 -0.39 0.48
#> xi_mu_centered[3,7,1] 0.08 0.08 0.76 -1.26 -0.54 0.13 0.73
#> xi_mu_centered[3,8,1] 0.07 0.09 0.79 -1.14 -0.66 0.24 0.67
#> xi_mu_centered[4,1,1] -0.11 0.12 1.14 -1.99 -0.78 -0.09 0.65
#> xi_mu_centered[4,2,1] 0.07 0.10 0.90 -1.33 -0.53 -0.04 0.78
#> xi_mu_centered[4,3,1] -0.06 0.14 1.29 -2.05 -0.93 -0.05 0.94
#> xi_mu_centered[4,4,1] -0.28 0.13 1.21 -2.32 -1.23 -0.05 0.56
#> xi_mu_centered[4,5,1] 0.21 0.08 0.77 -1.31 -0.26 0.22 0.72
#> xi_mu_centered[4,6,1] -0.17 0.08 0.77 -1.72 -0.78 -0.16 0.31
#> xi_mu_centered[4,7,1] 0.14 0.11 1.01 -1.72 -0.54 0.06 0.91
#> xi_mu_centered[4,8,1] 0.05 0.11 1.05 -1.46 -0.83 0.13 0.78
#> xi_mu_centered[5,1,1] 0.07 0.10 0.95 -1.78 -0.55 0.18 0.84
#> xi_mu_centered[5,2,1] 0.18 0.09 0.85 -1.34 -0.35 0.12 0.72
#> xi_mu_centered[5,3,1] 0.08 0.15 1.04 -1.74 -0.54 0.03 0.78
#> xi_mu_centered[5,4,1] 0.13 0.12 1.06 -2.20 -0.45 0.13 0.86
#> xi_mu_centered[5,5,1] 0.46 0.11 0.97 -1.90 -0.10 0.54 1.03
#> xi_mu_centered[5,6,1] -0.03 0.08 0.69 -1.27 -0.49 0.07 0.56
#> xi_mu_centered[5,7,1] -0.01 0.10 0.92 -1.79 -0.59 -0.05 0.52
#> xi_mu_centered[5,8,1] 0.01 0.11 0.99 -1.81 -0.76 -0.21 0.72
#> xi_mu_centered[6,1,1] 0.09 0.12 1.06 -1.87 -0.55 0.07 0.79
#> xi_mu_centered[6,2,1] -0.33 0.16 1.21 -2.69 -1.28 -0.29 0.49
#> xi_mu_centered[6,3,1] 0.31 0.10 0.93 -1.21 -0.30 0.26 0.77
#> xi_mu_centered[6,4,1] -0.07 0.14 1.25 -2.87 -0.69 -0.09 0.53
#> xi_mu_centered[6,5,1] -0.07 0.11 0.99 -1.87 -0.60 -0.02 0.60
#> xi_mu_centered[6,6,1] 0.02 0.12 1.07 -1.72 -0.80 0.21 0.93
#> xi_mu_centered[6,7,1] 0.01 0.13 0.93 -1.85 -0.46 0.04 0.64
#> xi_mu_centered[6,8,1] -0.05 0.15 1.16 -2.20 -0.83 0.19 0.75
#> xi_mu_centered[7,1,1] 0.41 0.13 1.17 -1.68 -0.53 0.54 1.08
#> xi_mu_centered[7,2,1] -0.05 0.12 0.90 -1.72 -0.91 0.23 0.60
#> xi_mu_centered[7,3,1] -0.15 0.12 1.09 -2.21 -1.03 -0.15 0.63
#> xi_mu_centered[7,4,1] -0.04 0.12 1.06 -1.97 -0.69 -0.14 0.70
#> xi_mu_centered[7,5,1] 0.02 0.12 1.06 -1.80 -0.65 -0.03 0.65
#> xi_mu_centered[7,6,1] -0.07 0.11 0.97 -1.53 -0.75 -0.14 0.62
#> xi_mu_centered[7,7,1] 0.05 0.09 0.82 -1.27 -0.49 0.01 0.55
#> xi_mu_centered[7,8,1] 0.08 0.13 1.16 -2.24 -0.61 0.08 0.93
#> xi_mu_centered[8,1,1] 0.15 0.11 0.98 -1.78 -0.52 0.22 0.72
#> xi_mu_centered[8,2,1] -0.10 0.11 1.02 -1.91 -0.91 -0.24 0.75
#> xi_mu_centered[8,3,1] 0.02 0.10 0.94 -1.87 -0.51 -0.01 0.50
#> xi_mu_centered[8,4,1] 0.06 0.12 1.07 -1.66 -0.79 0.12 1.02
#> xi_mu_centered[8,5,1] 0.03 0.08 0.74 -1.45 -0.39 0.05 0.51
#> xi_mu_centered[8,6,1] -0.11 0.12 1.10 -2.30 -0.69 -0.16 0.65
#> xi_mu_centered[8,7,1] -0.12 0.13 1.18 -2.13 -0.85 -0.41 0.73
#> xi_mu_centered[8,8,1] 0.07 0.09 0.84 -1.20 -0.50 -0.03 0.65
#> xi_mu_centered[9,1,1] 0.31 0.08 0.73 -1.03 -0.14 0.26 0.64
#> xi_mu_centered[9,2,1] -0.17 0.14 1.07 -2.28 -0.88 -0.25 0.70
#> xi_mu_centered[9,3,1] -0.15 0.10 0.92 -1.92 -0.63 -0.22 0.37
#> xi_mu_centered[9,4,1] -0.01 0.11 1.01 -1.76 -0.80 -0.01 0.60
#> xi_mu_centered[9,5,1] 0.07 0.10 0.90 -1.48 -0.49 0.17 0.66
#> xi_mu_centered[9,6,1] -0.06 0.09 0.86 -1.90 -0.48 -0.12 0.42
#> xi_mu_centered[9,7,1] 0.16 0.08 0.76 -1.22 -0.32 0.19 0.65
#> xi_mu_centered[9,8,1] -0.02 0.13 1.22 -2.46 -0.80 -0.17 0.76
#> xi_nu_centered[1,1,1] 0.01 0.10 0.95 -1.54 -0.75 0.01 0.73
#> xi_nu_centered[1,2,1] 0.01 0.11 0.98 -1.78 -0.67 0.03 0.65
#> xi_nu_centered[1,3,1] 0.12 0.11 1.01 -1.63 -0.66 -0.02 0.90
#> xi_nu_centered[1,4,1] 0.07 0.10 0.96 -1.37 -0.81 0.22 0.85
#> xi_nu_centered[1,5,1] 0.09 0.08 0.70 -1.07 -0.46 0.11 0.62
#> xi_nu_centered[1,6,1] -0.03 0.11 1.03 -1.51 -0.74 -0.17 0.66
#> xi_nu_centered[1,7,1] -0.05 0.09 0.81 -1.64 -0.59 0.03 0.43
#> xi_nu_centered[1,8,1] 0.03 0.11 1.02 -1.91 -0.68 0.10 0.58
#> xi_nu_centered[2,1,1] 0.03 0.11 1.01 -2.11 -0.65 0.00 0.86
#> xi_nu_centered[2,2,1] -0.09 0.10 0.92 -1.59 -0.81 0.10 0.56
#> xi_nu_centered[2,3,1] -0.03 0.10 0.90 -1.47 -0.65 -0.01 0.53
#> xi_nu_centered[2,4,1] -0.03 0.11 0.97 -1.74 -0.67 -0.09 0.63
#> xi_nu_centered[2,5,1] 0.00 0.08 0.78 -1.46 -0.38 0.01 0.37
#> xi_nu_centered[2,6,1] 0.00 0.12 1.11 -1.65 -0.95 -0.10 0.96
#> xi_nu_centered[2,7,1] 0.00 0.07 0.67 -1.10 -0.47 0.05 0.38
#> xi_nu_centered[2,8,1] 0.02 0.13 1.18 -2.07 -1.01 0.10 0.90
#> xi_nu_centered[3,1,1] -0.06 0.12 1.15 -2.38 -0.63 -0.09 0.66
#> xi_nu_centered[3,2,1] 0.13 0.10 0.84 -1.34 -0.28 0.09 0.62
#> xi_nu_centered[3,3,1] -0.10 0.12 1.00 -2.21 -0.88 0.02 0.46
#> xi_nu_centered[3,4,1] -0.14 0.12 1.10 -2.24 -0.72 -0.09 0.39
#> xi_nu_centered[3,5,1] -0.29 0.13 1.19 -2.30 -1.11 -0.44 0.64
#> xi_nu_centered[3,6,1] 0.03 0.10 0.97 -1.71 -0.63 0.03 0.80
#> xi_nu_centered[3,7,1] -0.06 0.08 0.75 -1.60 -0.50 0.10 0.43
#> xi_nu_centered[3,8,1] -0.06 0.12 0.86 -1.74 -0.56 0.03 0.52
#> xi_nu_centered[4,1,1] -0.11 0.10 0.89 -1.75 -0.71 -0.22 0.35
#> xi_nu_centered[4,2,1] 0.00 0.11 1.02 -1.72 -0.65 0.05 0.68
#> xi_nu_centered[4,3,1] -0.16 0.13 1.01 -1.87 -1.04 -0.04 0.60
#> xi_nu_centered[4,4,1] 0.14 0.11 1.04 -1.77 -0.58 0.13 0.96
#> xi_nu_centered[4,5,1] 0.09 0.11 1.06 -1.49 -0.81 0.04 0.84
#> xi_nu_centered[4,6,1] -0.02 0.12 0.99 -2.21 -0.42 0.04 0.52
#> xi_nu_centered[4,7,1] 0.14 0.11 1.00 -1.89 -0.42 0.24 0.77
#> xi_nu_centered[4,8,1] -0.01 0.12 1.09 -1.68 -0.92 0.03 0.69
#> xi_nu_centered[5,1,1] -0.07 0.12 1.07 -1.70 -0.85 -0.34 0.84
#> xi_nu_centered[5,2,1] 0.04 0.11 1.03 -1.35 -0.81 -0.25 0.97
#> xi_nu_centered[5,3,1] -0.03 0.10 0.95 -1.41 -0.81 0.11 0.77
#> xi_nu_centered[5,4,1] 0.14 0.14 1.26 -2.17 -0.73 0.11 0.82
#> xi_nu_centered[5,5,1] -0.02 0.13 1.22 -2.30 -0.81 -0.03 0.63
#> xi_nu_centered[5,6,1] -0.18 0.09 0.79 -1.60 -0.57 -0.28 0.36
#> xi_nu_centered[5,7,1] 0.11 0.10 0.89 -1.22 -0.54 0.10 0.72
#> xi_nu_centered[5,8,1] -0.05 0.15 1.39 -2.62 -1.01 -0.15 0.84
#> xi_nu_centered[6,1,1] 0.00 0.10 0.96 -1.47 -0.78 0.10 0.68
#> xi_nu_centered[6,2,1] 0.14 0.08 0.72 -0.99 -0.44 0.13 0.71
#> xi_nu_centered[6,3,1] -0.09 0.10 0.95 -1.79 -0.91 -0.17 0.62
#> xi_nu_centered[6,4,1] 0.05 0.12 1.10 -1.86 -0.77 0.02 0.79
#> xi_nu_centered[6,5,1] 0.01 0.11 0.90 -1.72 -0.68 0.00 0.81
#> xi_nu_centered[6,6,1] 0.00 0.13 1.22 -2.30 -0.89 0.00 1.06
#> xi_nu_centered[6,7,1] -0.22 0.12 0.87 -1.55 -0.83 -0.38 0.45
#> xi_nu_centered[6,8,1] 0.03 0.09 0.81 -1.18 -0.48 0.06 0.61
#> xi_nu_centered[7,1,1] -0.05 0.07 0.63 -1.22 -0.53 -0.12 0.31
#> xi_nu_centered[7,2,1] 0.03 0.10 0.93 -1.92 -0.51 0.12 0.66
#> xi_nu_centered[7,3,1] 0.06 0.16 1.44 -2.56 -1.03 0.28 1.08
#> xi_nu_centered[7,4,1] 0.13 0.14 1.06 -1.63 -0.68 0.34 0.77
#> xi_nu_centered[7,5,1] 0.09 0.12 1.10 -2.20 -0.52 0.09 0.76
#> xi_nu_centered[7,6,1] 0.06 0.13 1.22 -2.34 -0.38 0.13 0.57
#> xi_nu_centered[7,7,1] 0.05 0.10 0.91 -1.63 -0.55 -0.01 0.54
#> xi_nu_centered[7,8,1] 0.02 0.12 1.15 -1.69 -0.93 0.10 0.94
#> xi_nu_centered[8,1,1] -0.11 0.10 0.93 -1.69 -0.77 -0.23 0.64
#> xi_nu_centered[8,2,1] 0.05 0.09 0.85 -1.55 -0.38 0.06 0.64
#> xi_nu_centered[8,3,1] -0.05 0.13 1.17 -1.80 -0.93 -0.17 0.80
#> xi_nu_centered[8,4,1] -0.02 0.11 0.98 -1.90 -0.58 -0.06 0.63
#> xi_nu_centered[8,5,1] -0.10 0.08 0.70 -1.48 -0.51 -0.12 0.33
#> xi_nu_centered[8,6,1] -0.17 0.11 0.98 -2.04 -0.63 -0.20 0.41
#> xi_nu_centered[8,7,1] 0.12 0.12 1.06 -1.89 -0.60 0.13 0.88
#> xi_nu_centered[8,8,1] -0.01 0.13 1.17 -2.43 -0.68 0.15 0.63
#> xi_nu_centered[9,1,1] -0.06 0.13 1.01 -2.31 -0.60 -0.03 0.61
#> xi_nu_centered[9,2,1] -0.04 0.11 1.02 -1.78 -0.90 0.03 0.67
#> xi_nu_centered[9,3,1] -0.09 0.11 1.00 -1.89 -0.76 -0.10 0.73
#> xi_nu_centered[9,4,1] 0.08 0.12 1.15 -2.54 -0.39 0.08 0.83
#> xi_nu_centered[9,5,1] -0.04 0.10 0.96 -1.83 -0.74 0.00 0.42
#> xi_nu_centered[9,6,1] -0.09 0.10 0.96 -2.03 -0.62 -0.04 0.55
#> xi_nu_centered[9,7,1] -0.02 0.10 0.94 -2.05 -0.66 0.04 0.50
#> xi_nu_centered[9,8,1] -0.02 0.08 0.69 -1.25 -0.46 0.02 0.49
#> r[1] 0.75 0.02 0.14 0.53 0.63 0.74 0.82
#> mu_0[1,1] 2.72 0.10 0.85 1.26 2.19 2.59 3.22
#> mu_0[2,1] 2.68 0.09 0.80 1.42 2.10 2.61 3.13
#> mu_0[3,1] 2.63 0.08 0.74 1.40 2.21 2.50 3.05
#> mu_0[4,1] 2.66 0.10 0.89 1.30 1.97 2.59 3.25
#> mu_0[5,1] 2.67 0.08 0.78 1.24 2.19 2.60 3.14
#> mu_0[6,1] 2.50 0.09 0.83 1.17 1.94 2.41 3.01
#> mu_0[7,1] 2.58 0.08 0.77 1.24 2.15 2.45 3.02
#> mu_0[8,1] 2.66 0.10 0.92 1.21 2.12 2.46 3.15
#> nu_0[1,1] 2.91 0.11 0.89 1.24 2.29 2.96 3.54
#> nu_0[2,1] 2.84 0.09 0.85 1.09 2.33 2.96 3.42
#> nu_0[3,1] 2.69 0.08 0.71 1.18 2.41 2.74 3.06
#> nu_0[4,1] 2.67 0.09 0.83 1.11 1.96 2.86 3.37
#> nu_0[5,1] 2.80 0.09 0.84 1.09 2.32 2.90 3.26
#> nu_0[6,1] 2.50 0.09 0.87 0.57 1.92 2.58 3.00
#> nu_0[7,1] 2.63 0.09 0.82 1.18 2.05 2.71 3.29
#> nu_0[8,1] 2.68 0.08 0.78 1.13 2.28 2.71 3.25
#> xi_mu[1,1,1] -0.01 0.03 0.29 -0.63 -0.14 0.01 0.12
#> xi_mu[1,2,1] -0.06 0.03 0.28 -0.73 -0.16 0.00 0.05
#> xi_mu[1,3,1] 0.10 0.03 0.31 -0.25 -0.08 0.02 0.20
#> xi_mu[1,4,1] 0.09 0.04 0.35 -0.56 -0.08 0.02 0.18
#> xi_mu[1,5,1] 0.05 0.03 0.29 -0.52 -0.07 0.02 0.20
#> xi_mu[1,6,1] -0.04 0.04 0.32 -0.59 -0.17 -0.02 0.07
#> xi_mu[1,7,1] -0.03 0.03 0.32 -0.82 -0.15 0.00 0.09
#> xi_mu[1,8,1] 0.02 0.04 0.23 -0.33 -0.06 0.01 0.10
#> xi_mu[2,1,1] 0.06 0.04 0.27 -0.49 -0.02 0.02 0.15
#> xi_mu[2,2,1] -0.05 0.03 0.25 -0.37 -0.13 -0.02 0.05
#> xi_mu[2,3,1] 0.06 0.03 0.25 -0.31 -0.07 0.03 0.15
#> xi_mu[2,4,1] 0.06 0.04 0.28 -0.41 -0.08 0.02 0.19
#> xi_mu[2,5,1] 0.02 0.03 0.26 -0.33 -0.05 0.02 0.11
#> xi_mu[2,6,1] -0.12 0.04 0.30 -0.76 -0.36 -0.01 0.05
#> xi_mu[2,7,1] -0.05 0.03 0.25 -0.54 -0.13 -0.04 0.07
#> xi_mu[2,8,1] -0.10 0.03 0.24 -0.53 -0.18 -0.02 0.04
#> xi_mu[3,1,1] 0.00 0.04 0.27 -0.48 -0.11 0.00 0.10
#> xi_mu[3,2,1] 0.01 0.03 0.24 -0.37 -0.08 0.00 0.08
#> xi_mu[3,3,1] -0.11 0.03 0.25 -0.63 -0.20 -0.03 0.03
#> xi_mu[3,4,1] -0.09 0.04 0.31 -0.85 -0.11 -0.01 0.05
#> xi_mu[3,5,1] 0.12 0.05 0.26 -0.29 -0.01 0.07 0.23
#> xi_mu[3,6,1] -0.05 0.05 0.36 -0.66 -0.19 -0.04 0.03
#> xi_mu[3,7,1] 0.01 0.04 0.23 -0.46 -0.09 0.01 0.11
#> xi_mu[3,8,1] 0.04 0.02 0.19 -0.28 -0.09 0.02 0.17
#> xi_mu[4,1,1] -0.06 0.04 0.28 -0.69 -0.20 -0.03 0.06
#> xi_mu[4,2,1] 0.03 0.03 0.27 -0.44 -0.09 0.00 0.10
#> xi_mu[4,3,1] -0.03 0.06 0.42 -0.80 -0.14 0.00 0.17
#> xi_mu[4,4,1] -0.05 0.05 0.37 -0.96 -0.13 -0.01 0.14
#> xi_mu[4,5,1] 0.06 0.03 0.22 -0.31 -0.04 0.02 0.13
#> xi_mu[4,6,1] -0.09 0.04 0.29 -0.73 -0.19 -0.02 0.03
#> xi_mu[4,7,1] 0.07 0.05 0.35 -0.52 -0.11 0.00 0.18
#> xi_mu[4,8,1] 0.01 0.04 0.31 -0.48 -0.09 0.01 0.17
#> xi_mu[5,1,1] 0.02 0.03 0.27 -0.47 -0.17 0.01 0.17
#> xi_mu[5,2,1] 0.09 0.03 0.27 -0.29 -0.04 0.01 0.18
#> xi_mu[5,3,1] 0.01 0.05 0.31 -0.59 -0.11 0.01 0.08
#> xi_mu[5,4,1] 0.05 0.04 0.35 -0.67 -0.06 0.02 0.18
#> xi_mu[5,5,1] 0.11 0.04 0.24 -0.23 -0.02 0.07 0.23
#> xi_mu[5,6,1] 0.01 0.03 0.20 -0.32 -0.12 0.01 0.09
#> xi_mu[5,7,1] 0.06 0.04 0.40 -0.22 -0.08 -0.01 0.14
#> xi_mu[5,8,1] 0.01 0.03 0.29 -0.56 -0.11 -0.03 0.09
#> xi_mu[6,1,1] 0.04 0.05 0.31 -0.56 -0.06 0.01 0.15
#> xi_mu[6,2,1] -0.17 0.05 0.34 -0.80 -0.35 -0.05 0.05
#> xi_mu[6,3,1] 0.11 0.04 0.32 -0.44 -0.03 0.02 0.24
#> xi_mu[6,4,1] -0.02 0.04 0.34 -0.77 -0.16 -0.01 0.16
#> xi_mu[6,5,1] -0.04 0.03 0.27 -0.55 -0.12 -0.01 0.06
#> xi_mu[6,6,1] -0.04 0.03 0.29 -0.67 -0.14 0.02 0.11
#> xi_mu[6,7,1] -0.03 0.04 0.26 -0.66 -0.13 0.00 0.07
#> xi_mu[6,8,1] -0.01 0.05 0.37 -0.77 -0.16 0.01 0.11
#> xi_mu[7,1,1] 0.10 0.03 0.32 -0.38 -0.06 0.08 0.25
#> xi_mu[7,2,1] 0.03 0.02 0.22 -0.37 -0.16 0.03 0.12
#> xi_mu[7,3,1] -0.07 0.05 0.33 -0.73 -0.21 -0.03 0.07
#> xi_mu[7,4,1] 0.09 0.04 0.38 -0.45 -0.12 -0.01 0.19
#> xi_mu[7,5,1] 0.04 0.03 0.26 -0.51 -0.07 -0.01 0.18
#> xi_mu[7,6,1] -0.05 0.04 0.25 -0.57 -0.20 -0.01 0.11
#> xi_mu[7,7,1] 0.03 0.02 0.22 -0.46 -0.05 0.00 0.16
#> xi_mu[7,8,1] 0.09 0.05 0.42 -0.44 -0.10 0.00 0.16
#> xi_mu[8,1,1] 0.09 0.03 0.29 -0.39 -0.04 0.06 0.15
#> xi_mu[8,2,1] -0.04 0.03 0.24 -0.52 -0.15 -0.03 0.13
#> xi_mu[8,3,1] 0.05 0.05 0.38 -0.34 -0.08 0.00 0.07
#> xi_mu[8,4,1] 0.06 0.05 0.35 -0.54 -0.10 0.02 0.19
#> xi_mu[8,5,1] -0.02 0.03 0.22 -0.58 -0.08 0.01 0.09
#> xi_mu[8,6,1] -0.01 0.04 0.33 -0.84 -0.11 -0.01 0.14
#> xi_mu[8,7,1] -0.07 0.04 0.38 -1.03 -0.25 -0.03 0.10
#> xi_mu[8,8,1] -0.04 0.03 0.26 -0.42 -0.08 0.00 0.10
#> xi_mu[9,1,1] 0.11 0.03 0.24 -0.29 -0.01 0.04 0.18
#> xi_mu[9,2,1] -0.03 0.04 0.30 -0.65 -0.18 -0.01 0.11
#> xi_mu[9,3,1] -0.03 0.03 0.32 -0.82 -0.10 -0.01 0.06
#> xi_mu[9,4,1] -0.04 0.03 0.32 -0.83 -0.14 0.00 0.08
#> xi_mu[9,5,1] 0.04 0.04 0.37 -0.52 -0.08 0.01 0.15
#> xi_mu[9,6,1] -0.01 0.03 0.26 -0.57 -0.09 -0.01 0.08
#> xi_mu[9,7,1] 0.01 0.03 0.24 -0.61 -0.03 0.02 0.12
#> xi_mu[9,8,1] 0.00 0.04 0.31 -0.61 -0.14 -0.01 0.15
#> xi_nu[1,1,1] -0.14 0.17 1.34 -4.22 -0.38 0.01 0.31
#> xi_nu[1,2,1] 0.16 0.14 1.15 -1.23 -0.42 -0.01 0.37
#> xi_nu[1,3,1] 0.38 0.26 1.39 -1.61 -0.19 -0.01 0.74
#> xi_nu[1,4,1] 0.05 0.14 1.13 -2.30 -0.33 0.08 0.46
#> xi_nu[1,5,1] 0.18 0.13 0.92 -1.59 -0.22 0.09 0.51
#> xi_nu[1,6,1] -0.02 0.20 1.39 -2.44 -0.52 -0.05 0.43
#> xi_nu[1,7,1] 0.10 0.16 1.14 -2.12 -0.23 0.02 0.26
#> xi_nu[1,8,1] 0.22 0.16 1.25 -1.51 -0.33 0.03 0.47
#> xi_nu[2,1,1] -0.03 0.16 1.19 -2.84 -0.39 -0.01 0.46
#> xi_nu[2,2,1] 0.05 0.17 1.23 -2.28 -0.48 0.02 0.32
#> xi_nu[2,3,1] -0.07 0.13 0.96 -2.05 -0.41 -0.02 0.50
#> xi_nu[2,4,1] -0.38 0.13 1.18 -3.95 -0.47 -0.07 0.17
#> xi_nu[2,5,1] 0.07 0.18 1.25 -2.97 -0.17 0.01 0.30
#> xi_nu[2,6,1] -0.22 0.22 1.45 -3.78 -0.81 0.03 0.50
#> xi_nu[2,7,1] 0.13 0.10 0.83 -1.25 -0.24 0.02 0.34
#> xi_nu[2,8,1] 0.01 0.23 1.56 -4.20 -0.61 0.01 0.70
#> xi_nu[3,1,1] -0.28 0.16 1.37 -3.94 -0.48 -0.03 0.27
#> xi_nu[3,2,1] 0.04 0.11 0.82 -1.85 -0.20 0.04 0.45
#> xi_nu[3,3,1] -0.10 0.19 1.23 -3.12 -0.50 0.00 0.47
#> xi_nu[3,4,1] -0.04 0.32 1.75 -2.46 -0.37 -0.03 0.24
#> xi_nu[3,5,1] -0.57 0.23 1.72 -4.19 -0.98 -0.23 0.15
#> xi_nu[3,6,1] 0.23 0.17 1.13 -1.46 -0.35 0.02 0.60
#> xi_nu[3,7,1] -0.23 0.17 1.06 -3.03 -0.45 0.02 0.24
#> xi_nu[3,8,1] -0.14 0.12 0.86 -2.36 -0.34 -0.01 0.34
#> xi_nu[4,1,1] -0.02 0.13 1.01 -1.73 -0.48 -0.05 0.36
#> xi_nu[4,2,1] 0.28 0.14 1.19 -1.34 -0.39 0.01 0.56
#> xi_nu[4,3,1] -0.36 0.19 1.24 -3.25 -0.75 -0.02 0.32
#> xi_nu[4,4,1] 0.01 0.14 1.24 -2.71 -0.47 0.03 0.62
#> xi_nu[4,5,1] -0.17 0.23 1.56 -3.79 -0.43 0.02 0.37
#> xi_nu[4,6,1] -0.06 0.18 1.21 -3.20 -0.44 0.01 0.24
#> xi_nu[4,7,1] 0.14 0.15 1.17 -2.25 -0.31 0.03 0.54
#> xi_nu[4,8,1] -0.12 0.19 1.32 -4.08 -0.57 0.01 0.41
#> xi_nu[5,1,1] -0.23 0.13 1.24 -2.30 -0.85 -0.08 0.41
#> xi_nu[5,2,1] 0.30 0.20 1.45 -2.11 -0.28 -0.02 0.68
#> xi_nu[5,3,1] -0.17 0.13 1.15 -2.54 -0.65 0.01 0.27
#> xi_nu[5,4,1] 0.39 0.17 1.50 -1.47 -0.26 0.05 0.76
#> xi_nu[5,5,1] 0.35 0.18 1.46 -1.64 -0.34 0.00 0.50
#> xi_nu[5,6,1] -0.33 0.15 1.03 -3.00 -0.45 -0.10 0.21
#> xi_nu[5,7,1] -0.20 0.13 0.97 -2.72 -0.30 0.05 0.39
#> xi_nu[5,8,1] 0.26 0.26 1.79 -2.61 -0.59 -0.02 0.69
#> xi_nu[6,1,1] 0.21 0.16 1.21 -1.45 -0.44 0.05 0.42
#> xi_nu[6,2,1] 0.29 0.12 0.94 -0.97 -0.17 0.12 0.49
#> xi_nu[6,3,1] -0.29 0.24 1.32 -3.71 -0.60 -0.02 0.30
#> xi_nu[6,4,1] -0.12 0.14 1.28 -3.41 -0.59 0.00 0.55
#> xi_nu[6,5,1] -0.12 0.17 1.07 -2.96 -0.61 0.00 0.23
#> xi_nu[6,6,1] -0.26 0.28 1.71 -4.37 -0.60 0.00 0.38
#> xi_nu[6,7,1] -0.02 0.23 1.35 -2.83 -0.50 -0.02 0.35
#> xi_nu[6,8,1] 0.19 0.14 0.98 -1.34 -0.21 0.07 0.44
#> xi_nu[7,1,1] -0.11 0.15 0.72 -1.73 -0.34 -0.07 0.16
#> xi_nu[7,2,1] 0.12 0.13 1.05 -2.27 -0.22 0.15 0.55
#> xi_nu[7,3,1] 0.18 0.16 1.47 -2.61 -0.47 0.08 0.88
#> xi_nu[7,4,1] 0.17 0.28 1.46 -2.59 -0.40 0.09 0.56
#> xi_nu[7,5,1] 0.00 0.13 1.22 -3.25 -0.30 0.02 0.42
#> xi_nu[7,6,1] 0.09 0.30 1.91 -3.16 -0.13 0.06 0.49
#> xi_nu[7,7,1] 0.07 0.20 1.18 -1.81 -0.27 0.00 0.28
#> xi_nu[7,8,1] -0.02 0.22 1.59 -4.38 -0.72 0.02 0.52
#> xi_nu[8,1,1] -0.07 0.19 1.13 -2.49 -0.59 -0.07 0.42
#> xi_nu[8,2,1] 0.06 0.17 1.11 -1.94 -0.21 0.03 0.30
#> xi_nu[8,3,1] 0.11 0.27 1.48 -2.69 -0.65 -0.03 0.53
#> xi_nu[8,4,1] 0.14 0.11 1.01 -1.68 -0.25 -0.04 0.46
#> xi_nu[8,5,1] -0.18 0.12 0.91 -2.07 -0.51 -0.01 0.13
#> xi_nu[8,6,1] -0.06 0.20 1.27 -2.73 -0.31 -0.02 0.26
#> xi_nu[8,7,1] 0.26 0.26 1.49 -2.07 -0.39 0.02 0.58
#> xi_nu[8,8,1] -0.26 0.30 1.79 -5.24 -0.60 0.01 0.21
#> xi_nu[9,1,1] 0.00 0.21 1.26 -1.99 -0.34 0.01 0.38
#> xi_nu[9,2,1] -0.04 0.14 1.27 -2.89 -0.55 0.00 0.63
#> xi_nu[9,3,1] 0.04 0.19 1.25 -2.16 -0.51 -0.01 0.35
#> xi_nu[9,4,1] -0.23 0.27 1.42 -3.88 -0.36 0.02 0.41
#> xi_nu[9,5,1] -0.11 0.12 1.05 -2.46 -0.60 0.00 0.40
#> xi_nu[9,6,1] -0.12 0.15 1.17 -2.84 -0.36 -0.02 0.44
#> xi_nu[9,7,1] 0.18 0.16 1.16 -1.61 -0.30 0.01 0.37
#> xi_nu[9,8,1] 0.01 0.13 0.98 -1.82 -0.20 0.00 0.30
#> lambda[1,1] 5.63 0.06 0.38 4.99 5.34 5.61 5.87
#> lambda[1,2] 5.62 0.05 0.38 5.06 5.37 5.56 5.79
#> lambda[1,3] 5.68 0.05 0.39 5.04 5.44 5.65 5.88
#> lambda[1,4] 5.68 0.04 0.35 5.07 5.43 5.65 5.87
#> lambda[1,5] 5.62 0.05 0.42 4.83 5.33 5.62 5.87
#> lambda[1,6] 5.65 0.05 0.43 5.04 5.38 5.59 5.80
#> lambda[1,7] 5.68 0.05 0.47 4.96 5.39 5.59 5.80
#> lambda[1,8] 5.79 0.06 0.47 5.04 5.49 5.78 6.04
#> lambda[1,9] 5.88 0.06 0.54 5.10 5.43 5.82 6.15
#> lambda[1,10] 5.99 0.07 0.60 5.13 5.55 5.90 6.25
#> lambda[2,1] 5.53 0.04 0.40 4.78 5.32 5.56 5.68
#> lambda[2,2] 5.47 0.05 0.42 4.65 5.35 5.44 5.76
#> lambda[2,3] 5.42 0.05 0.41 4.68 5.20 5.39 5.69
#> lambda[2,4] 5.44 0.06 0.41 4.70 5.20 5.42 5.67
#> lambda[2,5] 5.46 0.06 0.42 4.74 5.26 5.42 5.71
#> lambda[2,6] 5.56 0.05 0.43 4.97 5.28 5.45 5.75
#> lambda[2,7] 5.39 0.07 0.55 4.40 5.12 5.36 5.70
#> lambda[2,8] 5.41 0.07 0.57 4.24 5.14 5.45 5.71
#> lambda[2,9] 5.37 0.08 0.55 4.22 5.09 5.36 5.63
#> lambda[2,10] 5.34 0.10 0.67 3.80 5.07 5.32 5.63
#> lambda[3,1] 5.32 0.04 0.34 4.74 5.12 5.33 5.54
#> lambda[3,2] 5.42 0.04 0.41 4.76 5.09 5.39 5.66
#> lambda[3,3] 5.48 0.06 0.50 4.67 5.16 5.40 5.75
#> lambda[3,4] 5.37 0.06 0.46 4.64 5.04 5.39 5.67
#> lambda[3,5] 5.35 0.07 0.51 4.38 4.96 5.41 5.66
#> lambda[3,6] 5.36 0.08 0.47 4.59 4.96 5.39 5.66
#> lambda[3,7] 5.46 0.06 0.44 4.63 5.13 5.49 5.70
#> lambda[3,8] 5.39 0.09 0.56 4.34 5.02 5.40 5.69
#> lambda[3,9] 5.44 0.08 0.68 4.43 4.97 5.46 5.74
#> lambda[3,10] 5.42 0.09 0.77 4.12 4.93 5.37 5.67
#> lambda[4,1] 5.32 0.04 0.30 4.83 5.08 5.31 5.55
#> lambda[4,2] 5.41 0.06 0.43 4.80 5.10 5.30 5.67
#> lambda[4,3] 5.47 0.05 0.41 4.77 5.22 5.43 5.69
#> lambda[4,4] 5.37 0.06 0.51 4.49 5.12 5.34 5.63
#> lambda[4,5] 5.33 0.06 0.47 4.59 5.01 5.24 5.65
#> lambda[4,6] 5.37 0.05 0.43 4.71 5.05 5.34 5.61
#> lambda[4,7] 5.35 0.06 0.51 4.49 5.03 5.42 5.65
#> lambda[4,8] 5.44 0.08 0.70 4.34 5.02 5.42 5.58
#> lambda[4,9] 5.50 0.10 0.88 4.15 5.04 5.35 5.71
#> lambda[4,10] 5.46 0.10 0.88 3.86 5.01 5.37 5.77
#> lambda[5,1] 5.47 0.05 0.41 4.65 5.27 5.52 5.62
#> lambda[5,2] 5.52 0.06 0.42 4.73 5.29 5.53 5.70
#> lambda[5,3] 5.54 0.05 0.41 4.91 5.32 5.52 5.68
#> lambda[5,4] 5.66 0.05 0.42 4.63 5.47 5.59 5.89
#> lambda[5,5] 5.73 0.05 0.36 5.22 5.52 5.65 5.94
#> lambda[5,6] 5.84 0.07 0.48 4.92 5.51 5.79 6.11
#> lambda[5,7] 5.80 0.07 0.53 4.88 5.46 5.81 6.10
#> lambda[5,8] 5.84 0.06 0.54 4.87 5.45 5.80 6.20
#> lambda[5,9] 5.83 0.07 0.59 4.66 5.46 5.67 6.20
#> lambda[5,10] 5.87 0.08 0.65 4.60 5.48 5.79 6.19
#> lambda[6,1] 5.00 0.07 0.50 4.07 4.63 5.06 5.34
#> lambda[6,2] 4.95 0.09 0.58 4.02 4.54 4.93 5.29
#> lambda[6,3] 4.83 0.08 0.58 3.79 4.44 4.84 5.17
#> lambda[6,4] 4.78 0.08 0.59 3.68 4.52 4.75 5.10
#> lambda[6,5] 4.70 0.09 0.65 3.38 4.43 4.59 5.12
#> lambda[6,6] 4.71 0.09 0.72 3.32 4.32 4.72 5.12
#> lambda[6,7] 4.66 0.08 0.77 3.06 4.33 4.78 5.05
#> lambda[6,8] 4.62 0.08 0.78 2.98 4.29 4.58 5.15
#> lambda[6,9] 4.61 0.09 0.86 2.77 4.15 4.75 5.08
#> lambda[6,10] 4.60 0.11 1.00 2.68 4.27 4.70 5.20
#> lambda[7,1] 5.21 0.05 0.48 4.33 4.93 5.23 5.46
#> lambda[7,2] 5.18 0.06 0.55 4.25 4.91 5.12 5.48
#> lambda[7,3] 5.13 0.06 0.54 4.28 4.77 5.16 5.45
#> lambda[7,4] 5.14 0.06 0.60 4.18 4.72 5.18 5.49
#> lambda[7,5] 5.21 0.07 0.62 4.22 4.76 5.25 5.56
#> lambda[7,6] 5.27 0.08 0.72 4.22 4.71 5.27 5.60
#> lambda[7,7] 5.24 0.09 0.78 4.17 4.60 5.23 5.63
#> lambda[7,8] 5.27 0.10 0.81 4.27 4.57 5.28 5.71
#> lambda[7,9] 5.21 0.09 0.71 4.03 4.80 5.19 5.66
#> lambda[7,10] 5.21 0.09 0.67 4.16 4.67 5.17 5.59
#> lambda[8,1] 5.34 0.07 0.58 4.37 4.93 5.34 5.59
#> lambda[8,2] 5.36 0.07 0.56 4.57 4.94 5.31 5.66
#> lambda[8,3] 5.26 0.08 0.59 4.20 4.85 5.17 5.65
#> lambda[8,4] 5.30 0.07 0.60 4.53 4.87 5.23 5.59
#> lambda[8,5] 5.31 0.07 0.61 4.25 4.88 5.32 5.68
#> lambda[8,6] 5.32 0.06 0.60 4.33 4.88 5.34 5.71
#> lambda[8,7] 5.31 0.08 0.69 4.01 5.02 5.34 5.80
#> lambda[8,8] 5.40 0.08 0.71 4.12 4.95 5.34 5.80
#> lambda[8,9] 5.36 0.07 0.66 4.20 4.97 5.29 5.73
#> lambda[8,10] 5.36 0.08 0.72 4.07 4.95 5.27 5.83
#> lambda_mean[1] 5.63 0.06 0.38 4.99 5.34 5.61 5.87
#> lambda_mean[2] 5.53 0.04 0.40 4.78 5.32 5.56 5.68
#> lambda_mean[3] 5.32 0.04 0.34 4.74 5.12 5.33 5.54
#> lambda_mean[4] 5.32 0.04 0.30 4.83 5.08 5.31 5.55
#> lambda_mean[5] 5.47 0.05 0.41 4.65 5.27 5.52 5.62
#> lambda_mean[6] 5.00 0.07 0.50 4.07 4.63 5.06 5.34
#> lambda_mean[7] 5.21 0.05 0.48 4.33 4.93 5.23 5.46
#> lambda_mean[8] 5.34 0.07 0.58 4.37 4.93 5.34 5.59
#> lambda_mean[9] 5.62 0.05 0.38 5.06 5.37 5.56 5.79
#> lambda_mean[10] 5.47 0.05 0.42 4.65 5.35 5.44 5.76
#> lambda_mean[11] 5.42 0.04 0.41 4.76 5.09 5.39 5.66
#> lambda_mean[12] 5.41 0.06 0.43 4.80 5.10 5.30 5.67
#> lambda_mean[13] 5.52 0.06 0.42 4.73 5.29 5.53 5.70
#> lambda_mean[14] 4.95 0.09 0.58 4.02 4.54 4.93 5.29
#> lambda_mean[15] 5.18 0.06 0.55 4.25 4.91 5.12 5.48
#> lambda_mean[16] 5.36 0.07 0.56 4.57 4.94 5.31 5.66
#> lambda_mean[17] 5.68 0.05 0.39 5.04 5.44 5.65 5.88
#> lambda_mean[18] 5.42 0.05 0.41 4.68 5.20 5.39 5.69
#> lambda_mean[19] 5.48 0.06 0.50 4.67 5.16 5.40 5.75
#> lambda_mean[20] 5.47 0.05 0.41 4.77 5.22 5.43 5.69
#> lambda_mean[21] 5.54 0.05 0.41 4.91 5.32 5.52 5.68
#> lambda_mean[22] 4.83 0.08 0.58 3.79 4.44 4.84 5.17
#> lambda_mean[23] 5.13 0.06 0.54 4.28 4.77 5.16 5.45
#> lambda_mean[24] 5.26 0.08 0.59 4.20 4.85 5.17 5.65
#> lambda_mean[25] 5.68 0.04 0.35 5.07 5.43 5.65 5.87
#> lambda_mean[26] 5.44 0.06 0.41 4.70 5.20 5.42 5.67
#> lambda_mean[27] 5.37 0.06 0.46 4.64 5.04 5.39 5.67
#> lambda_mean[28] 5.37 0.06 0.51 4.49 5.12 5.34 5.63
#> lambda_mean[29] 5.66 0.05 0.42 4.63 5.47 5.59 5.89
#> lambda_mean[30] 4.78 0.08 0.59 3.68 4.52 4.75 5.10
#> lambda_mean[31] 5.14 0.06 0.60 4.18 4.72 5.18 5.49
#> lambda_mean[32] 5.62 0.05 0.42 4.83 5.33 5.62 5.87
#> lambda_mean[33] 5.46 0.06 0.42 4.74 5.26 5.42 5.71
#> lambda_mean[34] 5.35 0.07 0.51 4.38 4.96 5.41 5.66
#> lambda_mean[35] 5.33 0.06 0.47 4.59 5.01 5.24 5.65
#> lambda_mean[36] 5.73 0.05 0.36 5.22 5.52 5.65 5.94
#> lambda_mean[37] 4.70 0.09 0.65 3.38 4.43 4.59 5.12
#> lambda_mean[38] 5.65 0.05 0.43 5.04 5.38 5.59 5.80
#> lambda_mean[39] 5.56 0.05 0.43 4.97 5.28 5.45 5.75
#> lambda_mean[40] 5.36 0.08 0.47 4.59 4.96 5.39 5.66
#> lambda_mean[41] 5.37 0.05 0.43 4.71 5.05 5.34 5.61
#> lambda_mean[42] 5.84 0.07 0.48 4.92 5.51 5.79 6.11
#> lambda_mean[43] 5.68 0.05 0.47 4.96 5.39 5.59 5.80
#> lambda_mean[44] 5.39 0.07 0.55 4.40 5.12 5.36 5.70
#> lambda_mean[45] 5.46 0.06 0.44 4.63 5.13 5.49 5.70
#> lambda_mean[46] 5.35 0.06 0.51 4.49 5.03 5.42 5.65
#> lambda_mean[47] 5.79 0.06 0.47 5.04 5.49 5.78 6.04
#> lambda_mean[48] 5.41 0.07 0.57 4.24 5.14 5.45 5.71
#> lambda_mean[49] 5.39 0.09 0.56 4.34 5.02 5.40 5.69
#> lambda_mean[50] 5.88 0.06 0.54 5.10 5.43 5.82 6.15
#> lambda_mean[51] 5.37 0.08 0.55 4.22 5.09 5.36 5.63
#> lambda_mean[52] 5.99 0.07 0.60 5.13 5.55 5.90 6.25
#> lprior -239.82 1.96 7.66 -255.05 -244.43 -238.95 -234.40
#> lp__ -583.92 2.29 8.20 -599.83 -588.51 -584.07 -577.79
#> 97.5% n_eff Rhat
#> mu_0_centered[1,1] 2.37 55 0.98
#> mu_0_centered[2,1] 1.65 85 1.03
#> mu_0_centered[3,1] 2.20 41 1.00
#> mu_0_centered[4,1] 2.06 85 1.00
#> mu_0_centered[5,1] 1.91 85 1.04
#> mu_0_centered[6,1] 1.96 80 1.00
#> mu_0_centered[7,1] 1.73 69 1.01
#> mu_0_centered[8,1] 2.04 72 1.00
#> nu_0_centered[1,1] 2.09 82 1.00
#> nu_0_centered[2,1] 1.64 77 1.00
#> nu_0_centered[3,1] 1.87 85 1.04
#> nu_0_centered[4,1] 1.68 85 0.98
#> nu_0_centered[5,1] 1.94 85 0.98
#> nu_0_centered[6,1] 1.03 85 1.01
#> nu_0_centered[7,1] 1.53 85 0.99
#> nu_0_centered[8,1] 1.33 85 0.98
#> mu_0_mean 4.27 85 0.98
#> nu_0_mean 3.86 85 1.00
#> mu_0_sd 1.01 43 1.02
#> nu_0_sd 1.43 29 1.01
#> mu_sd 0.57 49 1.00
#> nu_sd 2.92 85 1.00
#> xi_mu_centered[1,1,1] 2.11 85 0.99
#> xi_mu_centered[1,2,1] 1.19 85 1.02
#> xi_mu_centered[1,3,1] 2.20 85 0.98
#> xi_mu_centered[1,4,1] 2.00 85 0.99
#> xi_mu_centered[1,5,1] 1.81 85 0.98
#> xi_mu_centered[1,6,1] 1.51 63 1.00
#> xi_mu_centered[1,7,1] 1.91 85 0.98
#> xi_mu_centered[1,8,1] 1.53 85 0.99
#> xi_mu_centered[2,1,1] 1.61 75 0.99
#> xi_mu_centered[2,2,1] 1.49 85 0.98
#> xi_mu_centered[2,3,1] 1.66 85 0.99
#> xi_mu_centered[2,4,1] 1.85 85 0.98
#> xi_mu_centered[2,5,1] 1.27 85 0.98
#> xi_mu_centered[2,6,1] 1.65 75 0.98
#> xi_mu_centered[2,7,1] 1.68 85 1.01
#> xi_mu_centered[2,8,1] 1.74 85 0.99
#> xi_mu_centered[3,1,1] 1.71 85 0.99
#> xi_mu_centered[3,2,1] 1.36 62 0.98
#> xi_mu_centered[3,3,1] 1.34 85 1.05
#> xi_mu_centered[3,4,1] 1.26 85 1.00
#> xi_mu_centered[3,5,1] 2.43 64 0.99
#> xi_mu_centered[3,6,1] 1.33 85 1.00
#> xi_mu_centered[3,7,1] 1.32 85 0.98
#> xi_mu_centered[3,8,1] 1.46 85 0.98
#> xi_mu_centered[4,1,1] 2.08 85 0.98
#> xi_mu_centered[4,2,1] 1.80 85 1.01
#> xi_mu_centered[4,3,1] 2.11 85 0.99
#> xi_mu_centered[4,4,1] 1.64 85 0.99
#> xi_mu_centered[4,5,1] 1.65 85 0.98
#> xi_mu_centered[4,6,1] 1.14 84 1.01
#> xi_mu_centered[4,7,1] 1.76 85 1.01
#> xi_mu_centered[4,8,1] 2.09 85 0.98
#> xi_mu_centered[5,1,1] 1.64 85 0.98
#> xi_mu_centered[5,2,1] 1.97 85 0.99
#> xi_mu_centered[5,3,1] 2.01 46 0.98
#> xi_mu_centered[5,4,1] 1.77 79 0.99
#> xi_mu_centered[5,5,1] 2.50 85 0.98
#> xi_mu_centered[5,6,1] 1.05 69 0.98
#> xi_mu_centered[5,7,1] 1.99 85 0.98
#> xi_mu_centered[5,8,1] 1.81 85 0.98
#> xi_mu_centered[6,1,1] 1.69 85 1.02
#> xi_mu_centered[6,2,1] 2.19 57 0.98
#> xi_mu_centered[6,3,1] 2.22 85 0.98
#> xi_mu_centered[6,4,1] 2.48 85 0.98
#> xi_mu_centered[6,5,1] 1.54 85 0.98
#> xi_mu_centered[6,6,1] 1.49 85 0.98
#> xi_mu_centered[6,7,1] 1.59 51 1.02
#> xi_mu_centered[6,8,1] 1.98 63 1.01
#> xi_mu_centered[7,1,1] 2.28 85 0.98
#> xi_mu_centered[7,2,1] 1.32 60 1.01
#> xi_mu_centered[7,3,1] 1.73 85 1.00
#> xi_mu_centered[7,4,1] 2.10 85 0.98
#> xi_mu_centered[7,5,1] 1.90 85 0.98
#> xi_mu_centered[7,6,1] 1.46 75 0.99
#> xi_mu_centered[7,7,1] 1.56 85 0.98
#> xi_mu_centered[7,8,1] 2.24 85 1.01
#> xi_mu_centered[8,1,1] 2.09 85 0.98
#> xi_mu_centered[8,2,1] 1.52 85 1.01
#> xi_mu_centered[8,3,1] 2.01 85 0.99
#> xi_mu_centered[8,4,1] 1.60 85 0.98
#> xi_mu_centered[8,5,1] 1.44 81 0.98
#> xi_mu_centered[8,6,1] 2.05 85 0.98
#> xi_mu_centered[8,7,1] 2.05 85 0.98
#> xi_mu_centered[8,8,1] 1.90 85 0.99
#> xi_mu_centered[9,1,1] 1.84 85 0.98
#> xi_mu_centered[9,2,1] 1.52 59 0.98
#> xi_mu_centered[9,3,1] 1.44 85 0.98
#> xi_mu_centered[9,4,1] 1.85 85 0.98
#> xi_mu_centered[9,5,1] 1.56 85 0.98
#> xi_mu_centered[9,6,1] 1.43 85 0.98
#> xi_mu_centered[9,7,1] 1.61 85 0.99
#> xi_mu_centered[9,8,1] 2.33 85 1.00
#> xi_nu_centered[1,1,1] 1.61 85 0.98
#> xi_nu_centered[1,2,1] 1.74 85 1.02
#> xi_nu_centered[1,3,1] 2.14 85 1.01
#> xi_nu_centered[1,4,1] 1.61 85 0.98
#> xi_nu_centered[1,5,1] 1.20 85 0.98
#> xi_nu_centered[1,6,1] 1.97 85 0.98
#> xi_nu_centered[1,7,1] 1.38 85 0.99
#> xi_nu_centered[1,8,1] 2.24 85 0.98
#> xi_nu_centered[2,1,1] 1.72 82 1.01
#> xi_nu_centered[2,2,1] 1.58 85 0.99
#> xi_nu_centered[2,3,1] 1.65 83 1.00
#> xi_nu_centered[2,4,1] 1.60 85 1.01
#> xi_nu_centered[2,5,1] 1.57 85 0.98
#> xi_nu_centered[2,6,1] 1.82 85 0.98
#> xi_nu_centered[2,7,1] 1.16 85 0.99
#> xi_nu_centered[2,8,1] 2.29 85 0.98
#> xi_nu_centered[3,1,1] 1.92 85 1.00
#> xi_nu_centered[3,2,1] 1.51 73 0.99
#> xi_nu_centered[3,3,1] 1.72 66 1.00
#> xi_nu_centered[3,4,1] 1.98 85 0.99
#> xi_nu_centered[3,5,1] 1.89 85 0.99
#> xi_nu_centered[3,6,1] 1.52 85 0.98
#> xi_nu_centered[3,7,1] 1.06 85 1.02
#> xi_nu_centered[3,8,1] 1.22 48 1.05
#> xi_nu_centered[4,1,1] 1.92 85 0.98
#> xi_nu_centered[4,2,1] 1.73 85 0.98
#> xi_nu_centered[4,3,1] 1.59 58 1.05
#> xi_nu_centered[4,4,1] 2.10 85 1.00
#> xi_nu_centered[4,5,1] 1.87 85 1.03
#> xi_nu_centered[4,6,1] 1.79 68 1.03
#> xi_nu_centered[4,7,1] 1.62 85 0.98
#> xi_nu_centered[4,8,1] 1.69 85 0.98
#> xi_nu_centered[5,1,1] 1.67 85 0.98
#> xi_nu_centered[5,2,1] 1.85 85 0.98
#> xi_nu_centered[5,3,1] 1.67 85 0.98
#> xi_nu_centered[5,4,1] 2.48 85 0.99
#> xi_nu_centered[5,5,1] 2.18 85 0.98
#> xi_nu_centered[5,6,1] 1.34 85 1.00
#> xi_nu_centered[5,7,1] 1.88 85 0.98
#> xi_nu_centered[5,8,1] 2.58 85 1.00
#> xi_nu_centered[6,1,1] 1.85 85 1.00
#> xi_nu_centered[6,2,1] 1.37 85 0.98
#> xi_nu_centered[6,3,1] 1.73 85 0.99
#> xi_nu_centered[6,4,1] 2.05 85 0.99
#> xi_nu_centered[6,5,1] 1.33 64 1.01
#> xi_nu_centered[6,6,1] 2.25 85 0.98
#> xi_nu_centered[6,7,1] 1.35 51 0.99
#> xi_nu_centered[6,8,1] 1.49 85 0.98
#> xi_nu_centered[7,1,1] 1.23 81 1.04
#> xi_nu_centered[7,2,1] 1.71 85 0.98
#> xi_nu_centered[7,3,1] 2.30 85 0.98
#> xi_nu_centered[7,4,1] 2.10 55 0.98
#> xi_nu_centered[7,5,1] 2.38 85 1.01
#> xi_nu_centered[7,6,1] 2.53 85 1.01
#> xi_nu_centered[7,7,1] 2.04 85 0.99
#> xi_nu_centered[7,8,1] 2.07 85 0.99
#> xi_nu_centered[8,1,1] 1.42 81 0.99
#> xi_nu_centered[8,2,1] 1.51 85 0.98
#> xi_nu_centered[8,3,1] 1.71 85 0.98
#> xi_nu_centered[8,4,1] 1.98 85 0.99
#> xi_nu_centered[8,5,1] 1.21 85 0.98
#> xi_nu_centered[8,6,1] 1.64 85 0.98
#> xi_nu_centered[8,7,1] 2.09 85 0.98
#> xi_nu_centered[8,8,1] 2.07 85 0.99
#> xi_nu_centered[9,1,1] 1.90 57 0.99
#> xi_nu_centered[9,2,1] 1.82 85 1.01
#> xi_nu_centered[9,3,1] 1.46 85 0.98
#> xi_nu_centered[9,4,1] 1.97 85 1.00
#> xi_nu_centered[9,5,1] 1.47 85 1.00
#> xi_nu_centered[9,6,1] 1.60 85 1.01
#> xi_nu_centered[9,7,1] 1.70 85 1.01
#> xi_nu_centered[9,8,1] 1.23 85 0.98
#> r[1] 1.06 55 0.98
#> mu_0[1,1] 4.34 77 0.98
#> mu_0[2,1] 4.31 85 0.98
#> mu_0[3,1] 4.13 78 0.98
#> mu_0[4,1] 4.42 85 0.98
#> mu_0[5,1] 4.19 85 0.99
#> mu_0[6,1] 4.30 85 0.98
#> mu_0[7,1] 4.25 85 0.99
#> mu_0[8,1] 4.54 85 0.99
#> nu_0[1,1] 4.41 69 0.98
#> nu_0[2,1] 4.17 85 0.99
#> nu_0[3,1] 3.90 85 0.98
#> nu_0[4,1] 3.84 85 0.98
#> nu_0[5,1] 4.30 85 0.98
#> nu_0[6,1] 3.98 85 0.99
#> nu_0[7,1] 4.10 85 0.98
#> nu_0[8,1] 4.13 85 0.99
#> xi_mu[1,1,1] 0.61 85 0.98
#> xi_mu[1,2,1] 0.45 85 1.01
#> xi_mu[1,3,1] 1.00 85 0.98
#> xi_mu[1,4,1] 0.82 85 0.99
#> xi_mu[1,5,1] 0.54 84 0.98
#> xi_mu[1,6,1] 0.62 53 0.98
#> xi_mu[1,7,1] 0.53 85 0.99
#> xi_mu[1,8,1] 0.40 30 1.00
#> xi_mu[2,1,1] 0.71 52 0.98
#> xi_mu[2,2,1] 0.41 85 1.05
#> xi_mu[2,3,1] 0.67 85 0.99
#> xi_mu[2,4,1] 0.72 50 1.01
#> xi_mu[2,5,1] 0.33 60 0.98
#> xi_mu[2,6,1] 0.41 51 0.99
#> xi_mu[2,7,1] 0.51 66 0.99
#> xi_mu[2,8,1] 0.22 60 1.01
#> xi_mu[3,1,1] 0.47 55 1.02
#> xi_mu[3,2,1] 0.59 56 1.01
#> xi_mu[3,3,1] 0.23 72 0.99
#> xi_mu[3,4,1] 0.34 59 0.98
#> xi_mu[3,5,1] 0.63 29 0.98
#> xi_mu[3,6,1] 0.43 53 1.03
#> xi_mu[3,7,1] 0.44 32 0.98
#> xi_mu[3,8,1] 0.37 85 0.99
#> xi_mu[4,1,1] 0.40 42 0.99
#> xi_mu[4,2,1] 0.67 85 0.99
#> xi_mu[4,3,1] 0.71 58 1.03
#> xi_mu[4,4,1] 0.56 68 1.00
#> xi_mu[4,5,1] 0.62 76 0.98
#> xi_mu[4,6,1] 0.38 63 0.98
#> xi_mu[4,7,1] 0.79 55 1.05
#> xi_mu[4,8,1] 0.57 55 1.00
#> xi_mu[5,1,1] 0.58 59 0.99
#> xi_mu[5,2,1] 0.81 81 0.98
#> xi_mu[5,3,1] 0.71 41 0.99
#> xi_mu[5,4,1] 0.69 66 0.98
#> xi_mu[5,5,1] 0.58 36 0.98
#> xi_mu[5,6,1] 0.31 59 0.99
#> xi_mu[5,7,1] 0.60 85 0.98
#> xi_mu[5,8,1] 0.82 85 0.98
#> xi_mu[6,1,1] 0.80 34 0.99
#> xi_mu[6,2,1] 0.26 45 0.98
#> xi_mu[6,3,1] 0.86 79 1.02
#> xi_mu[6,4,1] 0.59 85 1.02
#> xi_mu[6,5,1] 0.46 85 0.99
#> xi_mu[6,6,1] 0.38 85 1.01
#> xi_mu[6,7,1] 0.44 42 1.03
#> xi_mu[6,8,1] 0.74 47 1.03
#> xi_mu[7,1,1] 0.89 85 1.01
#> xi_mu[7,2,1] 0.47 85 1.03
#> xi_mu[7,3,1] 0.61 46 0.99
#> xi_mu[7,4,1] 1.00 85 0.99
#> xi_mu[7,5,1] 0.55 60 1.00
#> xi_mu[7,6,1] 0.40 48 0.99
#> xi_mu[7,7,1] 0.48 85 1.00
#> xi_mu[7,8,1] 1.14 85 1.08
#> xi_mu[8,1,1] 0.81 85 0.98
#> xi_mu[8,2,1] 0.36 85 0.98
#> xi_mu[8,3,1] 0.81 60 1.07
#> xi_mu[8,4,1] 0.85 55 0.99
#> xi_mu[8,5,1] 0.33 42 0.99
#> xi_mu[8,6,1] 0.70 85 0.98
#> xi_mu[8,7,1] 0.55 85 0.99
#> xi_mu[8,8,1] 0.23 85 0.99
#> xi_mu[9,1,1] 0.84 73 0.99
#> xi_mu[9,2,1] 0.53 58 0.98
#> xi_mu[9,3,1] 0.63 85 0.98
#> xi_mu[9,4,1] 0.61 85 1.00
#> xi_mu[9,5,1] 0.46 79 0.99
#> xi_mu[9,6,1] 0.37 85 0.99
#> xi_mu[9,7,1] 0.41 85 0.99
#> xi_mu[9,8,1] 0.74 74 1.03
#> xi_nu[1,1,1] 2.19 60 1.01
#> xi_nu[1,2,1] 3.23 70 1.01
#> xi_nu[1,3,1] 4.67 30 1.04
#> xi_nu[1,4,1] 2.75 62 0.98
#> xi_nu[1,5,1] 2.62 52 0.98
#> xi_nu[1,6,1] 2.14 49 0.99
#> xi_nu[1,7,1] 3.22 52 0.99
#> xi_nu[1,8,1] 3.14 59 1.02
#> xi_nu[2,1,1] 2.39 55 1.00
#> xi_nu[2,2,1] 2.82 51 0.99
#> xi_nu[2,3,1] 1.65 56 1.01
#> xi_nu[2,4,1] 1.05 85 0.98
#> xi_nu[2,5,1] 3.47 47 0.99
#> xi_nu[2,6,1] 2.20 44 0.98
#> xi_nu[2,7,1] 1.73 67 0.98
#> xi_nu[2,8,1] 3.21 47 1.00
#> xi_nu[3,1,1] 1.26 71 0.99
#> xi_nu[3,2,1] 1.25 56 1.01
#> xi_nu[3,3,1] 1.61 40 1.00
#> xi_nu[3,4,1] 5.00 30 0.99
#> xi_nu[3,5,1] 1.84 56 1.00
#> xi_nu[3,6,1] 3.22 47 0.99
#> xi_nu[3,7,1] 1.40 38 1.06
#> xi_nu[3,8,1] 1.22 48 1.09
#> xi_nu[4,1,1] 1.92 60 0.98
#> xi_nu[4,2,1] 3.51 73 0.98
#> xi_nu[4,3,1] 1.67 42 1.06
#> xi_nu[4,4,1] 2.36 75 1.00
#> xi_nu[4,5,1] 1.61 46 0.98
#> xi_nu[4,6,1] 2.39 45 1.06
#> xi_nu[4,7,1] 2.97 62 0.98
#> xi_nu[4,8,1] 1.94 49 1.02
#> xi_nu[5,1,1] 2.00 85 0.98
#> xi_nu[5,2,1] 3.68 55 0.98
#> xi_nu[5,3,1] 2.16 74 1.04
#> xi_nu[5,4,1] 4.74 82 0.98
#> xi_nu[5,5,1] 3.45 66 1.01
#> xi_nu[5,6,1] 0.96 48 1.01
#> xi_nu[5,7,1] 0.86 55 0.99
#> xi_nu[5,8,1] 5.03 47 0.99
#> xi_nu[6,1,1] 3.30 59 1.00
#> xi_nu[6,2,1] 2.87 65 0.99
#> xi_nu[6,3,1] 1.59 30 1.01
#> xi_nu[6,4,1] 1.82 83 0.98
#> xi_nu[6,5,1] 1.75 39 1.00
#> xi_nu[6,6,1] 2.01 37 1.00
#> xi_nu[6,7,1] 3.61 33 0.98
#> xi_nu[6,8,1] 3.07 47 1.00
#> xi_nu[7,1,1] 1.33 23 1.14
#> xi_nu[7,2,1] 2.27 68 0.99
#> xi_nu[7,3,1] 3.47 85 0.99
#> xi_nu[7,4,1] 4.23 28 0.99
#> xi_nu[7,5,1] 1.98 85 0.99
#> xi_nu[7,6,1] 4.36 40 0.99
#> xi_nu[7,7,1] 2.76 36 0.99
#> xi_nu[7,8,1] 3.09 53 1.00
#> xi_nu[8,1,1] 2.70 36 1.00
#> xi_nu[8,2,1] 2.88 44 0.98
#> xi_nu[8,3,1] 3.65 29 1.00
#> xi_nu[8,4,1] 2.50 85 0.99
#> xi_nu[8,5,1] 1.07 56 0.99
#> xi_nu[8,6,1] 2.85 41 0.99
#> xi_nu[8,7,1] 3.87 33 0.98
#> xi_nu[8,8,1] 2.74 36 0.99
#> xi_nu[9,1,1] 2.21 36 0.99
#> xi_nu[9,2,1] 2.35 85 1.02
#> xi_nu[9,3,1] 2.77 42 1.00
#> xi_nu[9,4,1] 1.64 28 1.08
#> xi_nu[9,5,1] 2.01 76 0.98
#> xi_nu[9,6,1] 2.16 59 0.99
#> xi_nu[9,7,1] 3.39 51 1.02
#> xi_nu[9,8,1] 1.20 59 0.99
#> lambda[1,1] 6.31 45 1.01
#> lambda[1,2] 6.50 57 1.00
#> lambda[1,3] 6.42 50 0.99
#> lambda[1,4] 6.35 85 0.98
#> lambda[1,5] 6.54 85 0.98
#> lambda[1,6] 6.53 69 0.98
#> lambda[1,7] 6.95 85 0.99
#> lambda[1,8] 6.80 68 0.98
#> lambda[1,9] 7.05 72 0.98
#> lambda[1,10] 7.26 66 0.98
#> lambda[2,1] 6.45 81 1.00
#> lambda[2,2] 6.04 76 0.98
#> lambda[2,3] 6.02 56 1.02
#> lambda[2,4] 6.23 51 0.99
#> lambda[2,5] 6.14 46 1.01
#> lambda[2,6] 6.64 65 1.01
#> lambda[2,7] 6.56 57 1.01
#> lambda[2,8] 6.51 69 0.98
#> lambda[2,9] 6.44 48 0.98
#> lambda[2,10] 6.66 50 0.99
#> lambda[3,1] 5.94 80 1.01
#> lambda[3,2] 6.17 85 1.01
#> lambda[3,3] 6.63 70 1.02
#> lambda[3,4] 6.27 63 1.06
#> lambda[3,5] 6.26 51 0.98
#> lambda[3,6] 6.27 32 0.98
#> lambda[3,7] 6.35 52 1.00
#> lambda[3,8] 6.53 39 0.98
#> lambda[3,9] 6.88 77 1.02
#> lambda[3,10] 7.04 76 1.02
#> lambda[4,1] 5.81 48 0.98
#> lambda[4,2] 6.37 58 0.98
#> lambda[4,3] 6.39 73 0.98
#> lambda[4,4] 6.44 85 0.98
#> lambda[4,5] 6.50 58 1.00
#> lambda[4,6] 6.27 67 1.01
#> lambda[4,7] 6.14 70 1.07
#> lambda[4,8] 7.09 85 1.05
#> lambda[4,9] 7.28 85 1.05
#> lambda[4,10] 7.45 85 1.02
#> lambda[5,1] 6.27 64 1.00
#> lambda[5,2] 6.38 44 0.99
#> lambda[5,3] 6.47 71 0.98
#> lambda[5,4] 6.60 85 0.98
#> lambda[5,5] 6.47 61 0.98
#> lambda[5,6] 6.83 50 0.98
#> lambda[5,7] 6.87 52 0.98
#> lambda[5,8] 7.04 74 0.98
#> lambda[5,9] 7.13 81 0.98
#> lambda[5,10] 7.08 71 0.98
#> lambda[6,1] 5.85 47 0.98
#> lambda[6,2] 6.24 42 0.98
#> lambda[6,3] 6.06 46 0.99
#> lambda[6,4] 6.00 51 0.98
#> lambda[6,5] 5.86 51 0.98
#> lambda[6,6] 6.20 57 0.98
#> lambda[6,7] 6.21 85 0.98
#> lambda[6,8] 5.99 84 0.98
#> lambda[6,9] 6.28 85 0.98
#> lambda[6,10] 6.30 85 0.98
#> lambda[7,1] 6.23 81 0.98
#> lambda[7,2] 6.40 85 0.98
#> lambda[7,3] 6.37 85 0.98
#> lambda[7,4] 6.53 85 0.98
#> lambda[7,5] 6.35 72 1.00
#> lambda[7,6] 6.64 80 1.00
#> lambda[7,7] 6.84 80 0.98
#> lambda[7,8] 6.68 69 0.99
#> lambda[7,9] 6.69 57 0.98
#> lambda[7,10] 6.58 62 0.98
#> lambda[8,1] 6.61 61 0.98
#> lambda[8,2] 6.58 59 0.99
#> lambda[8,3] 6.58 55 1.01
#> lambda[8,4] 6.63 64 1.02
#> lambda[8,5] 6.68 85 0.99
#> lambda[8,6] 6.65 85 1.00
#> lambda[8,7] 6.68 85 0.98
#> lambda[8,8] 7.05 85 1.01
#> lambda[8,9] 6.71 85 1.00
#> lambda[8,10] 6.76 79 1.04
#> lambda_mean[1] 6.31 45 1.01
#> lambda_mean[2] 6.45 81 1.00
#> lambda_mean[3] 5.94 80 1.01
#> lambda_mean[4] 5.81 48 0.98
#> lambda_mean[5] 6.27 64 1.00
#> lambda_mean[6] 5.85 47 0.98
#> lambda_mean[7] 6.23 81 0.98
#> lambda_mean[8] 6.61 61 0.98
#> lambda_mean[9] 6.50 57 1.00
#> lambda_mean[10] 6.04 76 0.98
#> lambda_mean[11] 6.17 85 1.01
#> lambda_mean[12] 6.37 58 0.98
#> lambda_mean[13] 6.38 44 0.99
#> lambda_mean[14] 6.24 42 0.98
#> lambda_mean[15] 6.40 85 0.98
#> lambda_mean[16] 6.58 59 0.99
#> lambda_mean[17] 6.42 50 0.99
#> lambda_mean[18] 6.02 56 1.02
#> lambda_mean[19] 6.63 70 1.02
#> lambda_mean[20] 6.39 73 0.98
#> lambda_mean[21] 6.47 71 0.98
#> lambda_mean[22] 6.06 46 0.99
#> lambda_mean[23] 6.37 85 0.98
#> lambda_mean[24] 6.58 55 1.01
#> lambda_mean[25] 6.35 85 0.98
#> lambda_mean[26] 6.23 51 0.99
#> lambda_mean[27] 6.27 63 1.06
#> lambda_mean[28] 6.44 85 0.98
#> lambda_mean[29] 6.60 85 0.98
#> lambda_mean[30] 6.00 51 0.98
#> lambda_mean[31] 6.53 85 0.98
#> lambda_mean[32] 6.54 85 0.98
#> lambda_mean[33] 6.14 46 1.01
#> lambda_mean[34] 6.26 51 0.98
#> lambda_mean[35] 6.50 58 1.00
#> lambda_mean[36] 6.47 61 0.98
#> lambda_mean[37] 5.86 51 0.98
#> lambda_mean[38] 6.53 69 0.98
#> lambda_mean[39] 6.64 65 1.01
#> lambda_mean[40] 6.27 32 0.98
#> lambda_mean[41] 6.27 67 1.01
#> lambda_mean[42] 6.83 50 0.98
#> lambda_mean[43] 6.95 85 0.99
#> lambda_mean[44] 6.56 57 1.01
#> lambda_mean[45] 6.35 52 1.00
#> lambda_mean[46] 6.14 70 1.07
#> lambda_mean[47] 6.80 68 0.98
#> lambda_mean[48] 6.51 69 0.98
#> lambda_mean[49] 6.53 39 0.98
#> lambda_mean[50] 7.05 72 0.98
#> lambda_mean[51] 6.44 48 0.98
#> lambda_mean[52] 7.26 66 0.98
#> lprior -227.31 15 1.04
#> lp__ -569.11 13 1.04
#>
#> Samples were drawn using NUTS(diag_e) at Wed Sep 18 20:20:26 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
#>