Summarize posterior distributions of model parameters.
Source:R/joint_summarize.R
joint_summarize.RdThis function summarizes the posterior distributions of specified parameters from a model fit. Summary includes mean, sd, and specified quantiles, as well as effective sample size (n_eff) and Rhat for estimated parameters. See more examples in the Package Vignette.
Usage
joint_summarize(model_fit, par = "all", probs = c(0.025, 0.975), digits = 3)Note
Before fitting the model, this function checks to ensure that the function is possible given the inputs. These checks include:
Input model fit is an object of class 'stanfit'.
Input probs is a numeric vector.
Input par is a character vector.
Input par are present in fitted model.
Input model fit has converged (i.e. no divergent transitions after warm-up).
If any of these checks fail, the function returns an error message.
Examples
# \donttest{
data(green_crab_data)
# Fit a model
model_fit <- joint_model(data = green_crab_data, family = "negbin", q = TRUE,
multicore = FALSE)
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000331 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.31 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 1: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 1: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 1: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 1: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 1: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 1: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 3.645 seconds (Warm-up)
#> Chain 1: 12.612 seconds (Sampling)
#> Chain 1: 16.257 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000356 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.56 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 2: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 2: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 2: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 2: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 2: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 2: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 2: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 4.06 seconds (Warm-up)
#> Chain 2: 8.504 seconds (Sampling)
#> Chain 2: 12.564 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000363 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 3.63 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 3: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 3: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 3: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 3: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 3: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 3: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 3: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 3.619 seconds (Warm-up)
#> Chain 3: 9.483 seconds (Sampling)
#> Chain 3: 13.102 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000364 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 3.64 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 3000 [ 0%] (Warmup)
#> Chain 4: Iteration: 500 / 3000 [ 16%] (Warmup)
#> Chain 4: Iteration: 501 / 3000 [ 16%] (Sampling)
#> Chain 4: Iteration: 1000 / 3000 [ 33%] (Sampling)
#> Chain 4: Iteration: 1500 / 3000 [ 50%] (Sampling)
#> Chain 4: Iteration: 2000 / 3000 [ 66%] (Sampling)
#> Chain 4: Iteration: 2500 / 3000 [ 83%] (Sampling)
#> Chain 4: Iteration: 3000 / 3000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 3.668 seconds (Warm-up)
#> Chain 4: 8.421 seconds (Sampling)
#> Chain 4: 12.089 seconds (Total)
#> Chain 4:
#> Refer to the eDNAjoint guide for visualization tips: https://ednajoint.netlify.app/tips#visualization-tips
# Create summary table of all parameters
joint_summarize(model_fit$model)
#> mean se_mean sd 2.5% 97.5% n_eff Rhat
#> mu[1,1] 0.106 0.000 0.027 0.061 0.166 12380.617 1.000
#> mu[1,2] 0.084 0.000 0.023 0.046 0.135 13534.480 1.000
#> mu[2,1] 0.033 0.000 0.032 0.001 0.122 15282.459 1.000
#> mu[2,2] 0.026 0.000 0.025 0.001 0.096 14933.114 1.000
#> mu[3,1] 0.017 0.000 0.017 0.000 0.064 13326.894 1.000
#> mu[3,2] 0.013 0.000 0.014 0.000 0.050 13103.138 1.000
#> mu[4,1] 0.678 0.001 0.106 0.493 0.907 9235.392 1.000
#> mu[4,2] 0.534 0.001 0.089 0.380 0.730 12082.022 1.000
#> mu[5,1] 0.099 0.001 0.107 0.002 0.399 12484.283 1.000
#> mu[5,2] 0.078 0.001 0.085 0.002 0.311 12637.189 1.000
#> mu[6,1] 0.120 0.001 0.135 0.003 0.474 11524.459 1.000
#> mu[6,2] 0.094 0.001 0.106 0.002 0.370 11807.473 1.000
#> mu[7,1] 0.013 0.000 0.013 0.000 0.046 13747.883 1.000
#> mu[7,2] 0.010 0.000 0.010 0.000 0.036 13973.315 1.000
#> mu[8,1] 0.301 0.003 0.287 0.014 1.048 12615.161 1.000
#> mu[8,2] 0.238 0.002 0.229 0.011 0.825 12736.316 1.000
#> mu[9,1] 0.033 0.000 0.032 0.001 0.119 12322.959 1.000
#> mu[9,2] 0.026 0.000 0.025 0.001 0.093 12529.840 1.000
#> mu[10,1] 1.047 0.003 0.246 0.645 1.609 9615.298 1.000
#> mu[10,2] 0.822 0.002 0.193 0.506 1.261 14061.546 1.000
#> mu[11,1] 0.304 0.003 0.288 0.012 1.079 12190.575 1.000
#> mu[11,2] 0.240 0.002 0.227 0.009 0.839 12707.725 1.000
#> mu[12,1] 0.021 0.000 0.021 0.001 0.079 13420.422 1.000
#> mu[12,2] 0.017 0.000 0.017 0.000 0.062 13640.437 1.000
#> mu[13,1] 7.689 0.015 1.320 5.490 10.657 8157.489 1.000
#> mu[13,2] 6.036 0.008 0.987 4.331 8.266 13843.566 1.000
#> mu[14,1] 0.119 0.000 0.020 0.084 0.161 9214.932 1.000
#> mu[14,2] 0.094 0.000 0.016 0.065 0.129 12944.167 1.000
#> mu[15,1] 0.769 0.006 0.556 0.119 2.184 9952.528 1.000
#> mu[15,2] 0.604 0.004 0.435 0.093 1.714 10546.127 1.000
#> mu[16,1] 3.818 0.007 0.661 2.688 5.281 8093.417 1.000
#> mu[16,2] 2.994 0.004 0.470 2.185 4.012 16680.209 1.000
#> mu[17,1] 0.164 0.002 0.186 0.004 0.671 12385.708 1.000
#> mu[17,2] 0.129 0.001 0.146 0.003 0.530 12092.711 1.000
#> mu[18,1] 3.344 0.012 1.120 1.747 6.011 8621.316 1.000
#> mu[18,2] 2.629 0.009 0.880 1.368 4.775 10507.626 1.000
#> mu[19,1] 3.943 0.008 0.727 2.757 5.595 8332.283 1.000
#> mu[19,2] 3.100 0.005 0.576 2.134 4.396 12740.153 1.000
#> mu[20,1] 0.119 0.001 0.069 0.024 0.289 11857.371 1.000
#> mu[20,2] 0.093 0.000 0.054 0.019 0.228 12514.211 1.000
#> q[1] 0.793 0.001 0.103 0.609 1.010 5888.827 1.001
#> p10 0.019 0.000 0.010 0.005 0.042 10345.316 1.000
#> beta[1] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[2] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[3] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[4] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[5] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[6] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[7] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[8] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[9] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[10] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[11] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[12] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[13] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[14] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[15] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[16] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[17] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[18] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[19] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> beta[20] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> alpha[1] 1.264 0.003 0.248 0.782 1.760 7800.821 1.000
#> phi 0.918 0.001 0.132 0.690 1.205 9159.590 1.000
# Summarize just 'p10' parameter
joint_summarize(model_fit$model, par = "p10", probs = c(0.025, 0.975),
digits = 3)
#> mean se_mean sd 2.5% 97.5% n_eff Rhat
#> p10 0.019 0 0.01 0.005 0.042 10345.32 1
# }