brmspy - google colab¶

In [1]:

try: from brmspy import brms
except ImportError:
    %pip install -q brmspy
    from brmspy import brms

try: from brmspy import brms except ImportError: %pip install -q brmspy from brmspy import brms

[brmspy][WARNING] brmspy: brms and other required libraries are not installed. Please call brmspy.install_brms()

In [3]:

brms.install_brms(use_prebuilt=True)

brms.install_brms(use_prebuilt=True)

R callback write-console: CmdStan path set to: /Users/sebastian/.brmspy/runtime/macos-arm64-r4.5-0.2.0/cmdstan
  

In [ ]:

epilepsy = brms.get_brms_data("epilepsy")
formula = "count ~ zAge + zBase * Trt + (1|patient)"
family = "poisson"
brms.default_prior(formula, data=epilepsy, family=family)

epilepsy = brms.get_brms_data("epilepsy") formula = "count ~ zAge + zBase * Trt + (1|patient)" family = "poisson" brms.default_prior(formula, data=epilepsy, family=family)

Out[ ]:

	prior	class	coef	group	resp	dpar	nlpar	lb	ub	tag	source
1		b									default
2		b	Base								default
3		b	count								default
4		b	obs10								default
5		b	obs100								default
...	...	...	...	...	...	...	...	...	...	...	...
299		b	visit3								default
300		b	visit4								default
301		b	zAge								default
302		b	zBase								default
303	student_t(3, 3.3, 2.5)	Intercept									default

303 rows × 11 columns

In [6]:

model = brms.fit(
    formula=formula,
    data=epilepsy,
    family=family,
    warmup=500,
    iter=1000,
    chains=4
)
idata = model.idata

model = brms.fit( formula=formula, data=epilepsy, family=family, warmup=500, iter=1000, chains=4 ) idata = model.idata

[brmspy][<cell line: 0>] Fitting model with brms (backend: cmdstanr)...
WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Compiling Stan program...

WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Start sampling

Running MCMC with 4 sequential chains...

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Chain 4 finished in 2.9 seconds.

All 4 chains finished successfully.
Mean chain execution time: 3.5 seconds.
Total execution time: 14.5 seconds.

In [7]:

brms.save_rds(model, "epilepsy_fixed_effects.rds")
# load later using: model = brms.load_rds_fit("epilepsy_fixed_effects.rds")

brms.save_rds(model, "epilepsy_fixed_effects.rds") # load later using: model = brms.load_rds_fit("epilepsy_fixed_effects.rds")

In [8]:

import arviz as az
summary = az.summary(
    idata,
    hdi_prob=0.95,
    kind="stats",
    round_to=3
)

print("Posterior Summary")
print("="*60)
print(summary)

import arviz as az summary = az.summary( idata, hdi_prob=0.95, kind="stats", round_to=3 ) print("Posterior Summary") print("="*60) print(summary)

Posterior Summary
============================================================
                         mean     sd  hdi_2.5%  hdi_97.5%
Intercept               1.631  0.085     1.473      1.808
b_Intercept             1.777  0.130     1.535      2.049
b_Trt1                 -0.279  0.178    -0.630      0.079
b_zAge                  0.099  0.090    -0.067      0.286
b_zBase                 0.706  0.122     0.492      0.968
...                       ...    ...       ...        ...
r_patient[6,Intercept]  0.011  0.230    -0.424      0.440
r_patient[7,Intercept] -0.200  0.288    -0.712      0.425
r_patient[8,Intercept]  0.581  0.254     0.119      1.105
r_patient[9,Intercept] -0.013  0.261    -0.521      0.508
sd_patient__Intercept   0.588  0.072     0.458      0.731

[68 rows x 4 columns]

In [9]:

import matplotlib.pyplot as plt
import seaborn as sns

fig = az.plot_posterior(
    idata,
    var_names=['b_Intercept', 'b_zAge', 'b_zBase', 'b_Trt1', 'b_zBase:Trt1'],
    figsize=(12, 8),
    textsize=10
)
plt.suptitle('Posterior Distributions - Fixed Effects', y=1.02, fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt import seaborn as sns fig = az.plot_posterior( idata, var_names=['b_Intercept', 'b_zAge', 'b_zBase', 'b_Trt1', 'b_zBase:Trt1'], figsize=(12, 8), textsize=10 ) plt.suptitle('Posterior Distributions - Fixed Effects', y=1.02, fontsize=14, fontweight='bold') plt.tight_layout() plt.show()

In [9]: