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 0.000414 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 4.14 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: 4.816 seconds (Warm-up)
#> Chain 1: 13.379 seconds (Sampling)
#> Chain 1: 18.195 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000432 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 4.32 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: 4.79 seconds (Warm-up)
#> Chain 2: 16.165 seconds (Sampling)
#> Chain 2: 20.955 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000522 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 5.22 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: 5.208 seconds (Warm-up)
#> Chain 3: 14.258 seconds (Sampling)
#> Chain 3: 19.466 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'joint_count' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000401 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 4.01 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: 4.339 seconds (Warm-up)
#> Chain 4: 12.113 seconds (Sampling)
#> Chain 4: 16.452 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 0.028 0.060 0.168 11249.775 1
#> mu[1,2] 0.083 0.000 0.022 0.045 0.131 12030.000 1
#> mu[2,1] 0.032 0.000 0.035 0.001 0.126 13003.137 1
#> mu[2,2] 0.025 0.000 0.027 0.000 0.098 13096.576 1
#> mu[3,1] 0.017 0.000 0.018 0.000 0.065 12483.927 1
#> mu[3,2] 0.013 0.000 0.014 0.000 0.051 12801.222 1
#> mu[4,1] 0.684 0.001 0.109 0.496 0.919 10242.573 1
#> mu[4,2] 0.528 0.001 0.089 0.374 0.725 13242.074 1
#> mu[5,1] 0.101 0.001 0.108 0.002 0.395 13408.103 1
#> mu[5,2] 0.078 0.001 0.084 0.002 0.306 13187.346 1
#> mu[6,1] 0.120 0.001 0.133 0.003 0.471 12798.324 1
#> mu[6,2] 0.093 0.001 0.102 0.002 0.368 13251.577 1
#> mu[7,1] 0.013 0.000 0.013 0.000 0.047 13401.229 1
#> mu[7,2] 0.010 0.000 0.010 0.000 0.036 13469.004 1
#> mu[8,1] 0.307 0.003 0.297 0.010 1.099 11825.458 1
#> mu[8,2] 0.237 0.002 0.230 0.008 0.845 12050.187 1
#> mu[9,1] 0.034 0.000 0.033 0.001 0.120 13660.964 1
#> mu[9,2] 0.026 0.000 0.025 0.001 0.092 13616.333 1
#> mu[10,1] 1.056 0.002 0.247 0.648 1.616 11421.770 1
#> mu[10,2] 0.813 0.002 0.186 0.502 1.233 14735.481 1
#> mu[11,1] 0.302 0.002 0.286 0.013 1.065 13696.951 1
#> mu[11,2] 0.233 0.002 0.222 0.010 0.823 13680.319 1
#> mu[12,1] 0.021 0.000 0.022 0.000 0.082 14162.059 1
#> mu[12,2] 0.016 0.000 0.017 0.000 0.064 14067.922 1
#> mu[13,1] 7.791 0.014 1.362 5.525 10.920 8947.389 1
#> mu[13,2] 5.995 0.008 0.966 4.357 8.142 14352.034 1
#> mu[14,1] 0.120 0.000 0.020 0.084 0.163 11034.311 1
#> mu[14,2] 0.093 0.000 0.016 0.065 0.127 13081.598 1
#> mu[15,1] 0.769 0.006 0.543 0.125 2.138 8432.038 1
#> mu[15,2] 0.595 0.004 0.424 0.095 1.691 9372.344 1
#> mu[16,1] 3.854 0.007 0.664 2.722 5.320 8224.005 1
#> mu[16,2] 2.965 0.004 0.463 2.161 3.975 15936.736 1
#> mu[17,1] 0.163 0.002 0.177 0.004 0.652 12200.077 1
#> mu[17,2] 0.126 0.001 0.138 0.003 0.502 12179.628 1
#> mu[18,1] 3.345 0.012 1.102 1.774 5.965 9135.664 1
#> mu[18,2] 2.582 0.008 0.854 1.354 4.644 11189.034 1
#> mu[19,1] 3.975 0.007 0.717 2.785 5.606 10077.127 1
#> mu[19,2] 3.068 0.005 0.569 2.128 4.371 12332.396 1
#> mu[20,1] 0.119 0.001 0.071 0.025 0.294 13578.177 1
#> mu[20,2] 0.092 0.000 0.055 0.019 0.231 14010.271 1
#> q[1] 0.778 0.001 0.100 0.598 0.988 6848.915 1
#> p10 0.018 0.000 0.010 0.005 0.042 9514.083 1
#> beta[1] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[2] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[3] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[4] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[5] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[6] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[7] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[8] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[9] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[10] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[11] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[12] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[13] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[14] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[15] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[16] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[17] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[18] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[19] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> beta[20] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> alpha[1] 1.271 0.003 0.247 0.796 1.773 8621.853 1
#> phi 0.917 0.001 0.129 0.693 1.196 10776.960 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 9514.083 1
# }