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 0.000427 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.27 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: 4.939 seconds (Warm-up)
#> Chain 1: 12.167 seconds (Sampling)
#> Chain 1: 17.106 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000438 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 4.38 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: 4.915 seconds (Warm-up)
#> Chain 2: 14.978 seconds (Sampling)
#> Chain 2: 19.893 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000447 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 4.47 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: 4.372 seconds (Warm-up)
#> Chain 3: 13.231 seconds (Sampling)
#> Chain 3: 17.603 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000446 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 4.46 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: 4.723 seconds (Warm-up)
#> Chain 4: 14.453 seconds (Sampling)
#> Chain 4: 19.176 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] 0.107 0.000 0.027 0.061 0.169 12702.739 1
#> mu[1,2] 0.083 0.000 0.022 0.046 0.133 11988.019 1
#> mu[2,1] 0.033 0.000 0.033 0.001 0.123 12975.708 1
#> mu[2,2] 0.025 0.000 0.026 0.001 0.096 13069.307 1
#> mu[3,1] 0.017 0.000 0.018 0.000 0.066 12256.007 1
#> mu[3,2] 0.013 0.000 0.014 0.000 0.051 12023.938 1
#> mu[4,1] 0.684 0.001 0.111 0.492 0.923 10476.855 1
#> mu[4,2] 0.530 0.001 0.091 0.373 0.725 13027.597 1
#> mu[5,1] 0.100 0.001 0.110 0.002 0.399 13716.752 1
#> mu[5,2] 0.078 0.001 0.086 0.002 0.302 13963.735 1
#> mu[6,1] 0.120 0.001 0.132 0.003 0.484 11750.037 1
#> mu[6,2] 0.093 0.001 0.102 0.002 0.373 11771.604 1
#> mu[7,1] 0.013 0.000 0.013 0.000 0.046 13321.659 1
#> mu[7,2] 0.010 0.000 0.010 0.000 0.036 13408.403 1
#> mu[8,1] 0.307 0.003 0.287 0.014 1.062 12443.399 1
#> mu[8,2] 0.238 0.002 0.223 0.011 0.823 13044.897 1
#> mu[9,1] 0.034 0.000 0.033 0.001 0.122 13782.811 1
#> mu[9,2] 0.026 0.000 0.025 0.001 0.093 13607.916 1
#> mu[10,1] 1.059 0.002 0.245 0.657 1.608 10772.437 1
#> mu[10,2] 0.819 0.002 0.188 0.510 1.244 13850.627 1
#> mu[11,1] 0.309 0.003 0.291 0.014 1.070 12613.458 1
#> mu[11,2] 0.239 0.002 0.225 0.010 0.819 12858.840 1
#> mu[12,1] 0.021 0.000 0.022 0.001 0.079 13458.178 1
#> mu[12,2] 0.016 0.000 0.017 0.000 0.061 13604.364 1
#> mu[13,1] 7.765 0.014 1.322 5.561 10.737 8689.664 1
#> mu[13,2] 5.999 0.008 0.973 4.360 8.190 13863.218 1
#> mu[14,1] 0.120 0.000 0.020 0.084 0.163 10735.956 1
#> mu[14,2] 0.093 0.000 0.016 0.064 0.126 13666.153 1
#> mu[15,1] 0.777 0.005 0.548 0.121 2.209 10675.283 1
#> mu[15,2] 0.602 0.004 0.423 0.094 1.692 11170.318 1
#> mu[16,1] 3.847 0.007 0.671 2.694 5.347 8953.153 1
#> mu[16,2] 2.968 0.004 0.476 2.153 4.007 15004.688 1
#> mu[17,1] 0.164 0.002 0.189 0.003 0.668 10913.547 1
#> mu[17,2] 0.127 0.001 0.147 0.002 0.517 11186.028 1
#> mu[18,1] 3.381 0.013 1.145 1.751 6.161 7640.158 1
#> mu[18,2] 2.616 0.010 0.888 1.355 4.796 8298.920 1
#> mu[19,1] 3.978 0.008 0.732 2.784 5.653 9087.636 1
#> mu[19,2] 3.081 0.005 0.587 2.135 4.402 11731.000 1
#> mu[20,1] 0.121 0.001 0.070 0.027 0.290 12980.607 1
#> mu[20,2] 0.093 0.000 0.054 0.021 0.223 13434.916 1
#> q[1] 0.780 0.001 0.101 0.603 0.996 7067.901 1
#> p10 0.018 0.000 0.010 0.005 0.042 8670.297 1
#> beta[1] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[2] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[3] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[4] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[5] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[6] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[7] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[8] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[9] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[10] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[11] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[12] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[13] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[14] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[15] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[16] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[17] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[18] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[19] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> beta[20] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> alpha[1] 1.273 0.003 0.252 0.788 1.771 8270.141 1
#> phi 0.914 0.001 0.129 0.688 1.196 9147.870 1
# 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 0.018 0 0.01 0.005 0.042 8670.297 1
# }