The sccomp_estimate function performs linear modeling on a table of cell counts or proportions,
which includes a cell-group identifier, sample identifier, abundance (counts or proportions), and factors
(continuous or discrete). The user can define a linear model using an R formula,
where the first factor is the factor of interest. Alternatively, sccomp accepts
single-cell data containers (e.g., Seurat, SingleCellExperiment, cell metadata, or
group-size) and derives the count data from cell metadata.
Usage
sccomp_estimate(
.data,
formula_composition = ~1,
formula_variability = ~1,
sample,
cell_group,
abundance = NULL,
cores = detectCores(),
bimodal_mean_variability_association = FALSE,
percent_false_positive = 5,
inference_method = "pathfinder",
prior_mean = list(intercept = c(0, 1), coefficients = c(0, 1)),
prior_overdispersion_mean_association = list(intercept = c(5, 2), slope = c(0, 0.6),
standard_deviation = c(10, 20)),
.sample_cell_group_pairs_to_exclude = NULL,
output_directory = "sccomp_draws_files",
verbose = TRUE,
enable_loo = FALSE,
noise_model = "multi_beta_binomial",
exclude_priors = FALSE,
use_data = TRUE,
mcmc_seed = sample_seed(),
max_sampling_iterations = 20000,
pass_fit = TRUE,
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir,
cleanup_draw_files = TRUE,
...,
.count = NULL,
approximate_posterior_inference = NULL,
variational_inference = NULL,
.sample = NULL,
.cell_group = NULL,
.abundance = NULL
)Arguments
- .data
A tibble including cell_group name column, sample name column, abundance column (counts or proportions), and factor columns.
- formula_composition
A formula describing the model for differential abundance.
- formula_variability
A formula describing the model for differential variability.
- sample
A column name as a character string for the sample identifier. Replaces the deprecated
.sample.- cell_group
A column name as a character string for the cell-group identifier. Replaces the deprecated
.cell_group.- abundance
A column name as a character string for the cell-group abundance, which can be counts (> 0) or proportions (between 0 and 1, summing to 1 across
cell_group). Replaces the deprecated.abundanceand.count.- cores
Number of cores to use for parallel calculations.
- bimodal_mean_variability_association
Logical, whether to model mean-variability as bimodal.
- percent_false_positive
A real number between 0 and 100 for outlier identification.
- inference_method
Character string specifying the inference method to use ('pathfinder', 'hmc', or 'variational'). Replaces the deprecated
approximate_posterior_inferenceandvariational_inference.- prior_mean
A list specifying prior knowledge about the mean distribution, including intercept and coefficients.
- prior_overdispersion_mean_association
A list specifying prior knowledge about mean/variability association.
- .sample_cell_group_pairs_to_exclude
A column name indicating sample/cell-group pairs to exclude.
- output_directory
A character string specifying the output directory for Stan draws.
- verbose
Logical, whether to print progression details.
- enable_loo
Logical, whether to enable model comparison using the LOO package.
- noise_model
A character string specifying the noise model (e.g., 'multi_beta_binomial').
- exclude_priors
Logical, whether to run a prior-free model.
- use_data
Logical, whether to run the model data-free.
- mcmc_seed
An integer seed for MCMC reproducibility.
- max_sampling_iterations
Integer to limit the maximum number of iterations for large datasets.
- pass_fit
Logical, whether to include the Stan fit as an attribute in the output.
- sig_figs
Number of significant figures to use for Stan model output. Default is 9.
- cache_stan_model
A character string specifying the cache directory for compiled Stan models. The sccomp version will be automatically appended to ensure version isolation. Default is
sccomp_stan_models_cache_dirwhich points to~/.sccomp_models.- cleanup_draw_files
Logical, whether to automatically delete Stan draw CSV files after extracting results. These files can be large (MBs to GBs) and are typically only needed during the analysis session. Default is TRUE to save disk space. Set to FALSE if you need to inspect draw files for debugging.
- ...
Additional arguments passed to the
cmdstanr::samplefunction.- .count
DEPRECATED. Use
abundanceinstead.- approximate_posterior_inference
DEPRECATED. Use
inference_methodinstead.- variational_inference
DEPRECATED. Use
inference_methodinstead.- .sample
DEPRECATED. Use
sampleinstead.- .cell_group
DEPRECATED. Use
cell_groupinstead.- .abundance
DEPRECATED. Use
abundanceinstead.
Value
A tibble (tbl), with the following columns:
cell_group - The cell groups being tested.
parameter - The parameter being estimated from the design matrix described by the input formula_composition and formula_variability.
factor - The covariate factor in the formula, if applicable (e.g., not present for Intercept or contrasts).
c_lower - Lower (2.5%) quantile of the posterior distribution for a composition (c) parameter.
c_effect - Mean of the posterior distribution for a composition (c) parameter.
c_upper - Upper (97.5%) quantile of the posterior distribution for a composition (c) parameter.
c_pH0 - Probability of the c_effect being smaller or bigger than the
test_composition_above_logit_fold_changeargument.c_FDR - False discovery rate of the c_effect being smaller or bigger than the
test_composition_above_logit_fold_changeargument. False discovery rate for Bayesian models is calculated differently from frequentists models, as detailed in Mangiola et al, PNAS 2023.c_n_eff - Effective sample size, the number of independent draws in the sample. The higher, the better.
c_R_k_hat - R statistic, a measure of chain equilibrium, should be within 0.05 of 1.0.
v_lower - Lower (2.5%) quantile of the posterior distribution for a variability (v) parameter.
v_effect - Mean of the posterior distribution for a variability (v) parameter.
v_upper - Upper (97.5%) quantile of the posterior distribution for a variability (v) parameter.
v_pH0 - Probability of the v_effect being smaller or bigger than the
test_composition_above_logit_fold_changeargument.v_FDR - False discovery rate of the v_effect being smaller or bigger than the
test_composition_above_logit_fold_changeargument. False discovery rate for Bayesian models is calculated differently from frequentists models, as detailed in Mangiola et al, PNAS 2023.v_n_eff - Effective sample size for a variability (v) parameter.
v_R_k_hat - R statistic for a variability (v) parameter, a measure of chain equilibrium.
The function also attaches several attributes to the result:
count_data - The original count data used in the analysis, stored as an attribute for efficient access.
model_input - The model input data used for fitting.
formula_composition - The formula used for composition modeling.
formula_variability - The formula used for variability modeling.
fit - The Stan fit object (if pass_fit = TRUE).
References
S. Mangiola, A.J. Roth-Schulze, M. Trussart, E. Zozaya-Valdés, M. Ma, Z. Gao, A.F. Rubin, T.P. Speed, H. Shim, & A.T. Papenfuss, sccomp: Robust differential composition and variability analysis for single-cell data, Proc. Natl. Acad. Sci. U.S.A. 120 (33) e2203828120, https://doi.org/10.1073/pnas.2203828120 (2023).
Examples
print("cmdstanr is needed to run this example.")
#> [1] "cmdstanr is needed to run this example."
# Note: Before running the example, ensure that the 'cmdstanr' package is installed:
# install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev/", getOption("repos")))
# \donttest{
if (instantiate::stan_cmdstan_exists()) {
data("counts_obj")
estimate <- sccomp_estimate(
counts_obj,
~ type,
~1,
"sample",
"cell_group",
"count",
cores = 1
)
# Note!
# If counts are available, do not use proportion.
# Using proportion ignores the high uncertainty of low counts
estimate_proportion <- sccomp_estimate(
counts_obj,
~ type,
~1,
"sample",
"cell_group",
"proportion",
cores = 1
)
}
#> sccomp says: count column is an integer. The sum-constrained beta binomial model will be used
#> sccomp says: estimation
#> sccomp says: the composition design matrix has columns: (Intercept), typecancer
#> sccomp says: the variability design matrix has columns: (Intercept)
#> Loading model from cache...
#> Path [1] :Initial log joint density = -481654.381653
#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 2.167e-03 2.029e-01 6.600e-01 6.600e-01 3424 -3.697e+03 -3.716e+03
#> Path [1] :Best Iter: [56] ELBO (-3697.434692) evaluations: (3424)
#> Path [2] :Initial log joint density = -481500.229411
#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 55 -4.788e+05 5.412e-03 2.123e-01 1.000e+00 1.000e+00 2971 -3.706e+03 -3.707e+03
#> Path [2] :Best Iter: [39] ELBO (-3705.954588) evaluations: (2971)
#> Path [3] :Initial log joint density = -481822.102920
#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 64 -4.788e+05 1.304e-02 3.860e-01 1.000e+00 1.000e+00 3916 -3.697e+03 -3.708e+03
#> Path [3] :Best Iter: [63] ELBO (-3697.272401) evaluations: (3916)
#> Path [4] :Initial log joint density = -481551.788506
#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 51 -4.788e+05 8.631e-03 2.809e-01 9.750e-01 9.750e-01 2716 -3.707e+03 -3.719e+03
#> Path [4] :Best Iter: [42] ELBO (-3707.097311) evaluations: (2716)
#> Path [5] :Initial log joint density = -484419.961750
#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 7.471e-03 2.344e-01 9.691e-01 9.691e-01 3279 -3.700e+03 -3.711e+03
#> Path [5] :Best Iter: [57] ELBO (-3700.030866) evaluations: (3279)
#> Path [6] :Initial log joint density = -481631.827885
#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 53 -4.788e+05 9.808e-03 2.497e-01 1.000e+00 1.000e+00 2866 -3.709e+03 -3.709e+03
#> Path [6] :Best Iter: [45] ELBO (-3708.540375) evaluations: (2866)
#> Path [7] :Initial log joint density = -481710.030492
#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 61 -4.788e+05 2.104e-02 3.024e-01 1.000e+00 1.000e+00 3539 -3.697e+03 -3.701e+03
#> Path [7] :Best Iter: [60] ELBO (-3697.158723) evaluations: (3539)
#> Path [8] :Initial log joint density = -481330.877215
#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 56 -4.788e+05 1.186e-02 3.337e-01 1.000e+00 1.000e+00 3147 -3.699e+03 -3.710e+03
#> Path [8] :Best Iter: [55] ELBO (-3699.348605) evaluations: (3147)
#> Path [9] :Initial log joint density = -481611.041983
#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 53 -4.788e+05 1.365e-02 2.373e-01 1.000e+00 1.000e+00 3006 -3.707e+03 -3.716e+03
#> Path [9] :Best Iter: [49] ELBO (-3707.409962) evaluations: (3006)
#> Path [10] :Initial log joint density = -481745.971644
#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 62 -4.788e+05 8.569e-03 1.859e-01 1.000e+00 1.000e+00 3566 -3.700e+03 -3.701e+03
#> Path [10] :Best Iter: [58] ELBO (-3699.962136) evaluations: (3566)
#> Path [11] :Initial log joint density = -481872.465900
#> Path [11] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 54 -4.788e+05 7.808e-03 2.991e-01 1.000e+00 1.000e+00 3019 -3.708e+03 -3.722e+03
#> Path [11] :Best Iter: [42] ELBO (-3707.865249) evaluations: (3019)
#> Path [12] :Initial log joint density = -481559.476654
#> Path [12] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 59 -4.788e+05 2.563e-03 2.112e-01 7.010e-01 7.010e-01 3364 -3.699e+03 -3.714e+03
#> Path [12] :Best Iter: [56] ELBO (-3698.876768) evaluations: (3364)
#> Path [13] :Initial log joint density = -481696.894307
#> Path [13] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 63 -4.788e+05 1.038e-02 1.895e-01 1.000e+00 1.000e+00 3810 -3.700e+03 -3.707e+03
#> Path [13] :Best Iter: [61] ELBO (-3700.025273) evaluations: (3810)
#> Path [14] :Initial log joint density = -483510.973696
#> Path [14] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 57 -4.788e+05 1.308e-02 2.432e-01 1.000e+00 1.000e+00 3301 -3.699e+03 -3.705e+03
#> Path [14] :Best Iter: [55] ELBO (-3698.823192) evaluations: (3301)
#> Path [15] :Initial log joint density = -481957.307460
#> Path [15] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 67 -4.788e+05 6.586e-03 1.940e-01 5.008e-01 1.000e+00 4142 -3.702e+03 -3.711e+03
#> Path [15] :Best Iter: [56] ELBO (-3701.545769) evaluations: (4142)
#> Path [16] :Initial log joint density = -482282.152351
#> Path [16] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 60 -4.788e+05 9.781e-03 2.929e-01 1.000e+00 1.000e+00 3503 -3.702e+03 -3.701e+03
#> Path [16] :Best Iter: [60] ELBO (-3701.203964) evaluations: (3503)
#> Path [17] :Initial log joint density = -482230.918879
#> Path [17] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 60 -4.788e+05 1.252e-02 2.596e-01 1.000e+00 1.000e+00 3535 -3.701e+03 -3.701e+03
#> Path [17] :Best Iter: [59] ELBO (-3700.816082) evaluations: (3535)
#> Path [18] :Initial log joint density = -482065.406703
#> Path [18] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 57 -4.788e+05 5.765e-03 1.403e-01 1.000e+00 1.000e+00 3192 -3.706e+03 -3.710e+03
#> Path [18] :Best Iter: [47] ELBO (-3705.670744) evaluations: (3192)
#> Path [19] :Initial log joint density = -481648.673557
#> Path [19] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 7.943e-03 2.073e-01 8.565e-01 8.565e-01 3427 -3.702e+03 -3.716e+03
#> Path [19] :Best Iter: [57] ELBO (-3702.467771) evaluations: (3427)
#> Path [20] :Initial log joint density = -481614.512691
#> Path [20] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 54 -4.788e+05 1.290e-02 3.085e-01 1.000e+00 1.000e+00 2994 -3.708e+03 -3.706e+03
#> Path [20] :Best Iter: [54] ELBO (-3706.201107) evaluations: (2994)
#> Path [21] :Initial log joint density = -481743.073235
#> Path [21] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 61 -4.788e+05 8.344e-03 1.823e-01 9.147e-01 9.147e-01 3621 -3.698e+03 -3.710e+03
#> Path [21] :Best Iter: [59] ELBO (-3698.046204) evaluations: (3621)
#> Path [22] :Initial log joint density = -481521.666538
#> Path [22] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 56 -4.788e+05 8.577e-03 2.271e-01 1.000e+00 1.000e+00 3312 -3.702e+03 -3.701e+03
#> Path [22] :Best Iter: [56] ELBO (-3700.529150) evaluations: (3312)
#> Path [23] :Initial log joint density = -481608.246511
#> Path [23] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 7.073e-03 2.285e-01 1.000e+00 1.000e+00 3273 -3.701e+03 -3.702e+03
#> Path [23] :Best Iter: [55] ELBO (-3701.494855) evaluations: (3273)
#> Path [24] :Initial log joint density = -481590.757812
#> Path [24] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 56 -4.788e+05 9.357e-03 3.162e-01 1.000e+00 1.000e+00 3109 -3.699e+03 -3.711e+03
#> Path [24] :Best Iter: [55] ELBO (-3698.947510) evaluations: (3109)
#> Path [25] :Initial log joint density = -481883.127110
#> Path [25] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 57 -4.788e+05 9.923e-03 2.298e-01 1.000e+00 1.000e+00 3265 -3.702e+03 -3.701e+03
#> Path [25] :Best Iter: [57] ELBO (-3701.212455) evaluations: (3265)
#> Path [26] :Initial log joint density = -481610.802137
#> Path [26] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 52 -4.788e+05 2.457e-03 1.994e-01 6.760e-01 6.760e-01 2923 -3.706e+03 -3.721e+03
#> Path [26] :Best Iter: [39] ELBO (-3706.333994) evaluations: (2923)
#> Path [27] :Initial log joint density = -483043.100237
#> Path [27] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 55 -4.788e+05 3.976e-03 2.161e-01 7.877e-01 7.877e-01 3162 -3.708e+03 -3.708e+03
#> Path [27] :Best Iter: [52] ELBO (-3707.686934) evaluations: (3162)
#> Path [28] :Initial log joint density = -481568.764842
#> Path [28] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 5.104e-03 1.708e-01 9.489e-01 9.489e-01 3270 -3.701e+03 -3.709e+03
#> Path [28] :Best Iter: [56] ELBO (-3700.609982) evaluations: (3270)
#> Path [29] :Initial log joint density = -481709.139730
#> Path [29] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 55 -4.788e+05 2.489e-03 2.384e-01 6.879e-01 6.879e-01 3115 -3.710e+03 -3.716e+03
#> Path [29] :Best Iter: [49] ELBO (-3710.466699) evaluations: (3115)
#> Path [30] :Initial log joint density = -483078.070446
#> Path [30] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 54 -4.788e+05 1.035e-02 2.263e-01 1.000e+00 1.000e+00 3049 -3.709e+03 -3.709e+03
#> Path [30] :Best Iter: [51] ELBO (-3708.530262) evaluations: (3049)
#> Path [31] :Initial log joint density = -481626.358297
#> Path [31] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 56 -4.788e+05 1.094e-02 2.166e-01 1.000e+00 1.000e+00 3103 -3.701e+03 -3.699e+03
#> Path [31] :Best Iter: [56] ELBO (-3699.436563) evaluations: (3103)
#> Path [32] :Initial log joint density = -481527.834220
#> Path [32] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 52 -4.788e+05 7.462e-03 2.762e-01 8.202e-01 8.202e-01 2876 -3.710e+03 -3.726e+03
#> Path [32] :Best Iter: [51] ELBO (-3709.758603) evaluations: (2876)
#> Path [33] :Initial log joint density = -481526.836811
#> Path [33] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 57 -4.788e+05 4.923e-03 1.674e-01 8.020e-01 8.020e-01 3292 -3.701e+03 -3.712e+03
#> Path [33] :Best Iter: [55] ELBO (-3700.516092) evaluations: (3292)
#> Path [34] :Initial log joint density = -481442.115160
#> Path [34] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 55 -4.788e+05 1.005e-02 2.795e-01 1.000e+00 1.000e+00 3083 -3.704e+03 -3.712e+03
#> Path [34] :Best Iter: [52] ELBO (-3704.168963) evaluations: (3083)
#> Path [35] :Initial log joint density = -482817.992860
#> Path [35] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 8.217e-03 1.964e-01 1.000e+00 1.000e+00 3383 -3.699e+03 -3.706e+03
#> Path [35] :Best Iter: [56] ELBO (-3699.012032) evaluations: (3383)
#> Path [36] :Initial log joint density = -482371.212869
#> Path [36] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 59 -4.788e+05 8.694e-03 1.702e-01 1.000e+00 1.000e+00 3463 -3.706e+03 -3.702e+03
#> Path [36] :Best Iter: [59] ELBO (-3702.118871) evaluations: (3463)
#> Path [37] :Initial log joint density = -481669.243196
#> Path [37] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 8.186e-03 2.434e-01 8.575e-01 8.575e-01 3382 -3.702e+03 -3.708e+03
#> Path [37] :Best Iter: [57] ELBO (-3702.403673) evaluations: (3382)
#> Path [38] :Initial log joint density = -481726.485038
#> Path [38] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 61 -4.788e+05 5.826e-03 1.837e-01 1.000e+00 1.000e+00 3478 -3.701e+03 -3.706e+03
#> Path [38] :Best Iter: [58] ELBO (-3700.519387) evaluations: (3478)
#> Path [39] :Initial log joint density = -486797.702276
#> Path [39] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 60 -4.788e+05 1.542e-02 3.756e-01 1.000e+00 1.000e+00 3504 -3.700e+03 -3.707e+03
#> Path [39] :Best Iter: [59] ELBO (-3700.368903) evaluations: (3504)
#> Path [40] :Initial log joint density = -482343.229078
#> Path [40] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 1.091e-02 2.573e-01 1.000e+00 1.000e+00 3428 -3.702e+03 -3.704e+03
#> Path [40] :Best Iter: [57] ELBO (-3702.001015) evaluations: (3428)
#> Path [41] :Initial log joint density = -482326.036142
#> Path [41] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 59 -4.788e+05 1.060e-02 3.173e-01 4.180e-01 1.000e+00 3465 -3.700e+03 -3.708e+03
#> Path [41] :Best Iter: [58] ELBO (-3700.247131) evaluations: (3465)
#> Path [42] :Initial log joint density = -484233.788559
#> Path [42] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 59 -4.788e+05 1.209e-02 3.638e-01 1.000e+00 1.000e+00 3519 -3.702e+03 -3.710e+03
#> Path [42] :Best Iter: [58] ELBO (-3702.496905) evaluations: (3519)
#> Path [43] :Initial log joint density = -481499.079984
#> Path [43] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 52 -4.788e+05 7.917e-03 2.701e-01 1.000e+00 1.000e+00 2921 -3.709e+03 -3.722e+03
#> Path [43] :Best Iter: [48] ELBO (-3708.901039) evaluations: (2921)
#> Path [44] :Initial log joint density = -481690.725613
#> Path [44] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 51 -4.788e+05 1.690e-02 2.872e-01 1.000e+00 1.000e+00 2791 -3.708e+03 -3.712e+03
#> Path [44] :Best Iter: [38] ELBO (-3708.309108) evaluations: (2791)
#> Path [45] :Initial log joint density = -481978.836665
#> Path [45] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 59 -4.788e+05 1.012e-02 3.314e-01 1.000e+00 1.000e+00 3356 -3.700e+03 -3.709e+03
#> Path [45] :Best Iter: [58] ELBO (-3699.877352) evaluations: (3356)
#> Path [46] :Initial log joint density = -482134.522441
#> Path [46] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 57 -4.788e+05 7.966e-03 1.688e-01 1.000e+00 1.000e+00 3243 -3.701e+03 -3.702e+03
#> Path [46] :Best Iter: [55] ELBO (-3700.875823) evaluations: (3243)
#> Path [47] :Initial log joint density = -481964.238753
#> Path [47] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 63 -4.788e+05 9.695e-03 2.962e-01 9.379e-01 9.379e-01 3655 -3.701e+03 -3.709e+03
#> Path [47] :Best Iter: [61] ELBO (-3700.571289) evaluations: (3655)
#> Path [48] :Initial log joint density = -482907.260269
#> Path [48] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 58 -4.788e+05 6.646e-03 2.683e-01 8.935e-01 8.935e-01 3277 -3.698e+03 -3.713e+03
#> Path [48] :Best Iter: [55] ELBO (-3698.324182) evaluations: (3277)
#> Path [49] :Initial log joint density = -481811.295994
#> Path [49] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 56 -4.788e+05 1.082e-02 2.068e-01 1.000e+00 1.000e+00 3161 -3.702e+03 -3.700e+03
#> Path [49] :Best Iter: [56] ELBO (-3699.722007) evaluations: (3161)
#> Path [50] :Initial log joint density = -482767.869635
#> Path [50] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 55 -4.788e+05 1.034e-02 1.957e-01 1.000e+00 1.000e+00 3025 -3.709e+03 -3.709e+03
#> Path [50] :Best Iter: [49] ELBO (-3708.691571) evaluations: (3025)
#> Finished in 13.6 seconds.
#> sccomp says: to do hypothesis testing run `sccomp_test()`,
#> the `test_composition_above_logit_fold_change` = 0.1 equates to a change of ~10%, and
#> 0.7 equates to ~100% increase, if the baseline is ~0.1 proportion.
#> Use `sccomp_proportional_fold_change` to convert c_effect (linear) to proportion difference (non-linear).
#> sccomp says: auto-cleanup removed 1 draw files from 'sccomp_draws_files'
#> sccomp says: proportion column is a proportion. The sum-constrained beta model will be used. When possible using counts is preferred as the binomial noise component is often dominating for rare groups (e.g. rare cell types).
#> sccomp says: estimation
#> sccomp says: the composition design matrix has columns: (Intercept), typecancer
#> sccomp says: the variability design matrix has columns: (Intercept)
#> Loading model from cache...
#> Warning: sccomp says: your proportion values include 0. Assuming that 0s derive from a precision threshold (e.g. deconvolution), 0s are converted to the smaller non 0 proportion value.
#> Path [1] :Initial log joint density = -902.968750
#> Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.547e+03 5.469e-03 4.581e+03 4.295e-03 4.295e-03 8579 2.282e+03 -1.504e+04
#> Path [1] :Best Iter: [41] ELBO (2281.755314) evaluations: (8579)
#> Path [2] :Initial log joint density = -1161.963117
#> Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.544e+03 1.790e-02 3.196e+03 1.307e-02 1.307e-02 8383 2.282e+03 -1.199e+04
#> Path [2] :Best Iter: [33] ELBO (2281.917961) evaluations: (8383)
#> Path [3] :Initial log joint density = -482.168479
#> Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.545e+03 2.372e-02 4.608e+03 2.556e-02 2.556e-02 8742 2.282e+03 -8.034e+04
#> Path [3] :Best Iter: [30] ELBO (2281.982669) evaluations: (8742)
#> Path [4] :Initial log joint density = -459.558926
#> Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.555e+03 1.444e-01 7.699e+03 4.006e-02 4.006e-02 8779 2.278e+03 -1.391e+04
#> Path [4] :Best Iter: [30] ELBO (2278.229108) evaluations: (8779)
#> Path [5] :Initial log joint density = -383.381341
#> Path [5] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.549e+03 6.764e-03 3.918e+03 2.849e-02 2.849e-02 9165 2.282e+03 -6.903e+03
#> Path [5] :Best Iter: [26] ELBO (2282.000385) evaluations: (9165)
#> Path [6] :Initial log joint density = -365.395131
#> Path [6] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.548e+03 1.030e-01 1.925e+03 3.104e-02 6.174e-02 8257 2.280e+03 -2.210e+04
#> Path [6] :Best Iter: [27] ELBO (2280.349779) evaluations: (8257)
#> Path [7] :Initial log joint density = -428.442405
#> Path [7] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.567e+03 1.008e-01 4.908e+03 3.536e-02 3.536e-02 8646 2.280e+03 -8.924e+03
#> Path [7] :Best Iter: [32] ELBO (2280.208431) evaluations: (8646)
#> Path [8] :Initial log joint density = -220.070657
#> Path [8] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.560e+03 4.852e-02 4.520e+03 1.177e-02 1.177e-02 8617 2.277e+03 -4.195e+10
#> Path [8] :Best Iter: [33] ELBO (2276.719359) evaluations: (8617)
#> Path [9] :Initial log joint density = -557.215748
#> Path [9] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.550e+03 4.432e-02 2.538e+03 3.787e-02 3.787e-02 8132 2.282e+03 -4.631e+03
#> Path [9] :Best Iter: [34] ELBO (2282.026798) evaluations: (8132)
#> Path [10] :Initial log joint density = -524.013626
#> Path [10] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.535e+03 3.022e-02 1.783e+03 3.389e-02 3.389e-02 8957 2.278e+03 5.884e+02
#> Path [10] :Best Iter: [35] ELBO (2277.544277) evaluations: (8957)
#> Path [11] :Initial log joint density = -1079.727001
#> Path [11] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.542e+03 3.335e-02 3.996e+03 1.907e-02 1.907e-02 8646 2.281e+03 -5.303e+04
#> Path [11] :Best Iter: [31] ELBO (2281.186277) evaluations: (8646)
#> Path [12] :Initial log joint density = -465.690189
#> Path [12] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.558e+03 4.113e-02 4.346e+03 1.451e-02 3.513e-02 8493 2.280e+03 -3.931e+04
#> Path [12] :Best Iter: [30] ELBO (2280.309476) evaluations: (8493)
#> Path [13] :Initial log joint density = -178.211915
#> Path [13] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.568e+03 6.868e-02 1.368e+04 1.353e-02 2.792e-02 8674 2.282e+03 -4.164e+05
#> Path [13] :Best Iter: [30] ELBO (2281.639912) evaluations: (8674)
#> Path [14] :Initial log joint density = -230.265684
#> Path [14] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.557e+03 8.442e-02 4.200e+03 4.441e-02 4.441e-02 8748 2.281e+03 -2.903e+04
#> Path [14] :Best Iter: [34] ELBO (2280.937740) evaluations: (8748)
#> Path [15] :Initial log joint density = -793.051993
#> Path [15] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.552e+03 1.684e-02 3.400e+03 2.031e-02 2.031e-02 8790 2.280e+03 6.368e+02
#> Path [15] :Best Iter: [25] ELBO (2280.073105) evaluations: (8790)
#> Path [16] :Initial log joint density = -578.436960
#> Path [16] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.564e+03 8.278e-02 5.328e+03 5.374e-02 5.374e-02 8377 2.280e+03 -6.433e+05
#> Path [16] :Best Iter: [33] ELBO (2280.497199) evaluations: (8377)
#> Path [17] :Initial log joint density = -2028.358989
#> Path [17] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.541e+03 3.679e-02 2.322e+03 1.005e-02 3.663e-02 8500 2.284e+03 -3.824e+06
#> Path [17] :Best Iter: [37] ELBO (2283.887134) evaluations: (8500)
#> Path [18] :Initial log joint density = -1432.743671
#> Path [18] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.535e+03 1.013e-01 2.024e+03 4.631e-02 8.259e-02 8283 2.281e+03 -7.973e+04
#> Path [18] :Best Iter: [32] ELBO (2281.183175) evaluations: (8283)
#> Path [19] :Initial log joint density = -588.197326
#> Path [19] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.566e+03 8.220e-02 1.108e+04 1.056e-02 2.928e-02 8635 2.283e+03 -5.722e+06
#> Path [19] :Best Iter: [31] ELBO (2283.309332) evaluations: (8635)
#> Path [20] :Initial log joint density = -623.767183
#> Path [20] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.535e+03 7.503e-02 2.004e+03 5.798e-02 5.798e-02 8777 2.280e+03 -3.290e+04
#> Path [20] :Best Iter: [39] ELBO (2280.071008) evaluations: (8777)
#> Path [21] :Initial log joint density = -634.509077
#> Path [21] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.563e+03 1.285e-01 1.165e+04 3.788e-02 7.475e-02 8477 2.281e+03 -6.214e+05
#> Path [21] :Best Iter: [31] ELBO (2281.210964) evaluations: (8477)
#> Path [22] :Initial log joint density = -2499.201417
#> Path [22] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.557e+03 7.821e-02 4.823e+03 3.161e-02 3.161e-02 8625 2.282e+03 -3.440e+04
#> Path [22] :Best Iter: [33] ELBO (2281.623174) evaluations: (8625)
#> Path [23] :Initial log joint density = -411.836333
#> Path [23] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.554e+03 7.747e-02 4.734e+03 3.328e-02 6.266e-02 8636 2.277e+03 -1.151e+05
#> Path [23] :Best Iter: [28] ELBO (2277.209557) evaluations: (8636)
#> Path [24] :Initial log joint density = -559.424689
#> Path [24] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.546e+03 3.691e-02 3.027e+03 2.760e-02 2.760e-02 8434 2.275e+03 -1.843e+04
#> Path [24] :Best Iter: [33] ELBO (2275.382033) evaluations: (8434)
#> Path [25] :Initial log joint density = -522.532230
#> Path [25] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.533e+03 2.821e-02 1.835e+03 1.866e-02 1.866e-02 8243 2.279e+03 -1.575e+03
#> Path [25] :Best Iter: [40] ELBO (2278.849103) evaluations: (8243)
#> Path [26] :Initial log joint density = -691.995282
#> Path [26] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.556e+03 2.518e-02 3.997e+03 1.741e-02 1.741e-02 8682 2.282e+03 -2.655e+04
#> Path [26] :Best Iter: [35] ELBO (2281.514117) evaluations: (8682)
#> Path [27] :Initial log joint density = -512.403578
#> Path [27] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.538e+03 2.831e-02 3.648e+03 2.495e-02 2.495e-02 8365 2.283e+03 -5.230e+04
#> Path [27] :Best Iter: [39] ELBO (2282.767566) evaluations: (8365)
#> Path [28] :Initial log joint density = -1575.241488
#> Path [28] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.528e+03 3.966e-02 2.725e+03 2.048e-02 2.048e-02 8323 2.282e+03 -1.524e+06
#> Path [28] :Best Iter: [40] ELBO (2282.192954) evaluations: (8323)
#> Path [29] :Initial log joint density = -275.740487
#> Path [29] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.558e+03 7.615e-02 5.330e+03 6.129e-02 6.129e-02 9127 2.283e+03 -3.109e+05
#> Path [29] :Best Iter: [31] ELBO (2282.530924) evaluations: (9127)
#> Path [30] :Initial log joint density = -715.116811
#> Path [30] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.544e+03 6.860e-02 2.617e+03 1.621e-02 3.373e-02 8722 2.279e+03 -1.400e+04
#> Path [30] :Best Iter: [38] ELBO (2279.198273) evaluations: (8722)
#> Path [31] :Initial log joint density = -2684.787615
#> Path [31] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.554e+03 1.710e-02 8.064e+03 1.529e-02 1.529e-02 8611 2.282e+03 -4.490e+03
#> Path [31] :Best Iter: [29] ELBO (2281.915604) evaluations: (8611)
#> Path [32] :Initial log joint density = -485.198740
#> Path [32] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.565e+03 7.640e-02 1.268e+04 6.079e-02 6.079e-02 8925 2.283e+03 6.951e+02
#> Path [32] :Best Iter: [30] ELBO (2283.078447) evaluations: (8925)
#> Path [33] :Initial log joint density = -6078.335094
#> Path [33] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.541e+03 7.620e-02 3.937e+03 4.242e-02 4.242e-02 8665 2.279e+03 -3.366e+03
#> Path [33] :Best Iter: [36] ELBO (2279.453213) evaluations: (8665)
#> Path [34] :Initial log joint density = -1173.869259
#> Path [34] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.540e+03 4.209e-02 2.384e+03 1.676e-02 1.676e-02 8615 2.284e+03 -9.163e+03
#> Path [34] :Best Iter: [34] ELBO (2284.132407) evaluations: (8615)
#> Path [35] :Initial log joint density = -332.811121
#> Path [35] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.556e+03 4.870e-02 5.141e+03 2.172e-02 2.172e-02 8255 2.279e+03 -3.792e+05
#> Path [35] :Best Iter: [30] ELBO (2278.778488) evaluations: (8255)
#> Path [36] :Initial log joint density = -747.060435
#> Path [36] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.559e+03 1.503e-02 3.095e+03 1.378e-02 1.378e-02 8201 2.278e+03 -2.279e+04
#> Path [36] :Best Iter: [32] ELBO (2277.511493) evaluations: (8201)
#> Path [37] :Initial log joint density = -526.723191
#> Path [37] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.561e+03 6.232e-02 5.949e+03 4.551e-02 4.551e-02 8650 2.281e+03 -2.269e+03
#> Path [37] :Best Iter: [30] ELBO (2280.558136) evaluations: (8650)
#> Path [38] :Initial log joint density = -404.384347
#> Path [38] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.568e+03 1.375e-01 6.303e+03 5.706e-02 9.788e-02 8664 2.282e+03 -7.229e+04
#> Path [38] :Best Iter: [27] ELBO (2282.465134) evaluations: (8664)
#> Path [39] :Initial log joint density = -632.941179
#> Path [39] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.550e+03 4.273e-02 2.624e+03 1.527e-02 1.527e-02 9212 2.279e+03 -1.368e+04
#> Path [39] :Best Iter: [33] ELBO (2279.162542) evaluations: (9212)
#> Path [40] :Initial log joint density = -775.346346
#> Path [40] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.530e+03 6.272e-02 1.528e+03 8.953e-02 8.953e-02 8525 2.282e+03 5.353e+01
#> Path [40] :Best Iter: [36] ELBO (2282.472913) evaluations: (8525)
#> Path [41] :Initial log joint density = -712.964437
#> Path [41] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.557e+03 1.158e-01 4.045e+03 2.051e-02 4.673e-02 8777 2.279e+03 -1.554e+06
#> Path [41] :Best Iter: [30] ELBO (2279.017463) evaluations: (8777)
#> Path [42] :Initial log joint density = -742.481724
#> Path [42] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.535e+03 4.342e-02 1.681e+03 2.675e-02 2.675e-02 8150 2.285e+03 -1.228e+04
#> Path [42] :Best Iter: [38] ELBO (2284.697530) evaluations: (8150)
#> Path [43] :Initial log joint density = -896.167512
#> Path [43] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.540e+03 5.332e-02 3.331e+03 1.454e-02 1.454e-02 8271 2.279e+03 -7.228e+06
#> Path [43] :Best Iter: [40] ELBO (2278.959754) evaluations: (8271)
#> Path [44] :Initial log joint density = -685.456581
#> Path [44] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.553e+03 1.849e-02 3.474e+03 2.683e-02 2.683e-02 8431 2.279e+03 -1.661e+04
#> Path [44] :Best Iter: [33] ELBO (2278.727162) evaluations: (8431)
#> Path [45] :Initial log joint density = -643.163599
#> Path [45] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.559e+03 7.774e-02 4.767e+03 5.292e-02 5.292e-02 8529 2.282e+03 -4.488e+05
#> Path [45] :Best Iter: [36] ELBO (2281.509429) evaluations: (8529)
#> Path [46] :Initial log joint density = -1335.371318
#> Path [46] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.537e+03 8.866e-02 2.322e+03 3.951e-02 3.951e-02 8568 2.283e+03 -3.178e+05
#> Path [46] :Best Iter: [38] ELBO (2282.753006) evaluations: (8568)
#> Path [47] :Initial log joint density = -1070.599605
#> Path [47] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.545e+03 7.592e-02 5.141e+03 2.477e-02 2.477e-02 8665 2.281e+03 -1.938e+05
#> Path [47] :Best Iter: [32] ELBO (2281.286466) evaluations: (8665)
#> Path [48] :Initial log joint density = -616.101402
#> Path [48] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.554e+03 3.762e-02 1.335e+04 1.550e-02 1.550e-02 8515 2.281e+03 -2.545e+04
#> Path [48] :Best Iter: [36] ELBO (2281.348816) evaluations: (8515)
#> Path [49] :Initial log joint density = -515.544733
#> Path [49] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.559e+03 4.951e-02 4.184e+03 6.724e-02 6.724e-02 8553 2.282e+03 -4.611e+03
#> Path [49] :Best Iter: [23] ELBO (2281.706488) evaluations: (8553)
#> Path [50] :Initial log joint density = -461.375064
#> Path [50] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
#> 100 2.567e+03 5.172e-02 7.435e+03 2.628e-02 2.628e-02 8422 2.280e+03 -1.284e+06
#> Path [50] :Best Iter: [33] ELBO (2279.898143) evaluations: (8422)
#> Finished in 15.9 seconds.
#> sccomp says: to do hypothesis testing run `sccomp_test()`,
#> the `test_composition_above_logit_fold_change` = 0.1 equates to a change of ~10%, and
#> 0.7 equates to ~100% increase, if the baseline is ~0.1 proportion.
#> Use `sccomp_proportional_fold_change` to convert c_effect (linear) to proportion difference (non-linear).
#> sccomp says: auto-cleanup removed 1 draw files from 'sccomp_draws_files'
# }