Helper function for binding draws from several brms models to make a data.frame
for use with brms::hypothesis(). This will also check that the draws are comparable using
basic model metrics.
Arguments
- ...
Some number of brmsfit objects and/or dataframes of draws (should generally be the same type of model fit to different data)
- message
Logical, should messages about possible problems be printed? Default is TRUE. This will warn if models may not have converged, if there are different numbers of draws in the objects, or if models have different formulations.
Examples
# note that this example will fit several bayesian models and may run for several minutes.
# \donttest{
simdf <- growthSim("logistic",
n = 20, t = 25,
params = list(
"A" = c(200, 160, 220, 200, 140, 300),
"B" = c(13, 11, 10, 9, 16, 12),
"C" = c(3, 3.5, 3.2, 2.8, 3.3, 2.5)
)
)
ss_ab <- growthSS(
model = "logistic", form = y ~ time | id / group,
sigma = "logistic", df = simdf[simdf$group %in% c("a", "b"), ],
start = list(
"A" = 130, "B" = 12, "C" = 3,
"sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
), type = "brms"
)
ss_cd <- growthSS(
model = "logistic", form = y ~ time | id / group,
sigma = "logistic", df = simdf[simdf$group %in% c("c", "d"), ],
start = list(
"A" = 130, "B" = 12, "C" = 3,
"sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
), type = "brms"
)
ss_ef <- growthSS(
model = "logistic", form = y ~ time | id / group,
sigma = "logistic", df = simdf[simdf$group %in% c("e", "f"), ],
start = list(
"A" = 130, "B" = 12, "C" = 3,
"sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
), type = "brms"
)
ss_ef2 <- growthSS(
model = "gompertz", form = y ~ time | id / group,
sigma = "logistic", df = simdf[simdf$group %in% c("e", "f"), ],
start = list(
"A" = 130, "B" = 12, "C" = 3,
"sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
), type = "brms"
)
fit_ab <- fitGrowth(ss_ab, chains = 1, cores = 1, iter = 1000)
#> Start sampling
#> Init values were only set for a subset of parameters.
#> Missing init values for the following parameters:
#> Intercept_nu
#>
#> To disable this message use options(cmdstanr_warn_inits = FALSE).
#> Running MCMC with 1 chain...
#>
#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is -nan, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is -nan, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is -nan, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)
#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
#> Chain 1 finished in 7.9 seconds.
fit_ab2 <- fitGrowth(ss_ab, chains = 1, cores = 1, iter = 1200)
#> Start sampling
#> Init values were only set for a subset of parameters.
#> Missing init values for the following parameters:
#> Intercept_nu
#>
#> To disable this message use options(cmdstanr_warn_inits = FALSE).
#> Running MCMC with 1 chain...
#>
#> Chain 1 Iteration: 1 / 1200 [ 0%] (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[503] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Iteration: 100 / 1200 [ 8%] (Warmup)
#> Chain 1 Iteration: 200 / 1200 [ 16%] (Warmup)
#> Chain 1 Iteration: 300 / 1200 [ 25%] (Warmup)
#> Chain 1 Iteration: 400 / 1200 [ 33%] (Warmup)
#> Chain 1 Iteration: 500 / 1200 [ 41%] (Warmup)
#> Chain 1 Iteration: 600 / 1200 [ 50%] (Warmup)
#> Chain 1 Iteration: 601 / 1200 [ 50%] (Sampling)
#> Chain 1 Iteration: 700 / 1200 [ 58%] (Sampling)
#> Chain 1 Iteration: 800 / 1200 [ 66%] (Sampling)
#> Chain 1 Iteration: 900 / 1200 [ 75%] (Sampling)
#> Chain 1 Iteration: 1000 / 1200 [ 83%] (Sampling)
#> Chain 1 Iteration: 1100 / 1200 [ 91%] (Sampling)
#> Chain 1 Iteration: 1200 / 1200 [100%] (Sampling)
#> Chain 1 finished in 8.5 seconds.
fit_cd <- fitGrowth(ss_cd, chains = 1, cores = 1, iter = 1000)
#> Start sampling
#> Init values were only set for a subset of parameters.
#> Missing init values for the following parameters:
#> Intercept_nu
#>
#> To disable this message use options(cmdstanr_warn_inits = FALSE).
#> Running MCMC with 1 chain...
#>
#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[502] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is -nan, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)
#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
#> Chain 1 finished in 7.9 seconds.
fit_ef <- fitGrowth(ss_ef, chains = 1, cores = 1, iter = 1000)
#> Start sampling
#> Init values were only set for a subset of parameters.
#> Missing init values for the following parameters:
#> Intercept_nu
#>
#> To disable this message use options(cmdstanr_warn_inits = FALSE).
#> Running MCMC with 1 chain...
#>
#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d67c016da.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)
#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
#> Chain 1 finished in 7.8 seconds.
fit_ef2 <- fitGrowth(ss_ef2, chains = 1, cores = 1, iter = 1000)
#> Start sampling
#> Init values were only set for a subset of parameters.
#> Missing init values for the following parameters:
#> Intercept_nu
#>
#> To disable this message use options(cmdstanr_warn_inits = FALSE).
#> Running MCMC with 1 chain...
#>
#> Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d52b24384.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[501] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d52b24384.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d52b24384.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d52b24384.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Location parameter[1] is -nan, but must be finite! (in '/tmp/RtmpUvwz4B/model-1a7d52b24384.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is -nan, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d52b24384.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: student_t_lpdf: Scale parameter[1] is inf, but must be positive finite! (in '/tmp/RtmpUvwz4B/model-1a7d52b24384.stan', line 119, column 4 to column 48)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1
#> Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)
#> Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)
#> Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)
#> Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)
#> Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)
#> Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)
#> Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)
#> Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)
#> Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)
#> Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)
#> Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)
#> Chain 1 finished in 21.7 seconds.
x <- combineDraws(fit_ab, fit_cd, fit_ef)
draws_ef <- as.data.frame(fit_ef)
draws_ef <- draws_ef[, grepl("^b_", colnames(draws_ef))]
x2 <- combineDraws(fit_ab2, fit_cd, draws_ef)
#> fit_cd has fewer than 600 draws and will be padded with 100 NAs
#> draws_ef has fewer than 600 draws and will be padded with 100 NAs
x3 <- combineDraws(fit_ab, fit_cd, fit_ef2)
#> Some of these models have different growth formulas, consider if this is what you want.
#> fit_ab: y~A/(1 + exp((B - time)/C)), fit_cd: y~A/(1 + exp((B - time)/C)), fit_ef2: y~A * exp(-B * exp(-C * time))
# }