Specify predictor term with missing values in brms. The function does
not evaluate its arguments – it exists purely to help set up a model.
For documentation on how to specify missing values in response variables,
see resp_mi.
Details
For detailed documentation see help(brmsformula).
Examples
# \dontrun{
data("nhanes", package = "mice")
N <- nrow(nhanes)
# simple model with missing data
bform1 <- bf(bmi | mi() ~ age * mi(chl)) +
bf(chl | mi() ~ age) +
set_rescor(FALSE)
fit1 <- brm(bform1, data = nhanes)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.2e-05 seconds
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#> Chain 2: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
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#> Chain 4:
summary(fit1)
#> Family: MV(gaussian, gaussian)
#> Links: mu = identity
#> mu = identity
#> Formula: bmi | mi() ~ age * mi(chl)
#> chl | mi() ~ age
#> Data: nhanes (Number of observations: 25)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> bmi_Intercept 13.45 9.03 -3.98 31.45 1.00 1898 2162
#> chl_Intercept 136.50 22.24 91.67 180.58 1.00 3128 2917
#> bmi_age -4.47 5.86 -16.50 6.89 1.00 1518 1869
#> chl_age 31.62 11.47 8.85 54.58 1.00 3241 3097
#> bmi_michl 0.12 0.05 0.03 0.21 1.00 2133 2491
#> bmi_michl:age -0.01 0.03 -0.06 0.05 1.00 1617 1903
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma_bmi 3.28 0.74 2.17 5.05 1.00 1796 2037
#> sigma_chl 36.88 6.86 26.15 53.42 1.00 2295 2803
#>
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
plot(conditional_effects(fit1, resp = "bmi"), ask = FALSE)
loo(fit1, newdata = na.omit(fit1$data))
#> Warning: Found 2 observations with a pareto_k > 0.7 in model 'fit1'. We recommend to set 'moment_match = TRUE' in order to perform moment matching for problematic observations.
#>
#> Computed from 4000 by 13 log-likelihood matrix.
#>
#> Estimate SE
#> elpd_loo -105.5 4.8
#> p_loo 6.9 1.9
#> looic 210.9 9.5
#> ------
#> MCSE of elpd_loo is NA.
#> MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 0.8]).
#>
#> Pareto k diagnostic values:
#> Count Pct. Min. ESS
#> (-Inf, 0.7] (good) 11 84.6% 747
#> (0.7, 1] (bad) 2 15.4% <NA>
#> (1, Inf) (very bad) 0 0.0% <NA>
#> See help('pareto-k-diagnostic') for details.
# simulate some measurement noise
nhanes$se <- rexp(N, 2)
# measurement noise can be handled within 'mi' terms
# with or without the presence of missing values
bform2 <- bf(bmi | mi() ~ age * mi(chl)) +
bf(chl | mi(se) ~ age) +
set_rescor(FALSE)
fit2 <- brm(bform2, data = nhanes)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 4.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
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#> Chain 4:
#> Warning: There were 6 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
summary(fit2)
#> Warning: There were 6 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> Family: MV(gaussian, gaussian)
#> Links: mu = identity
#> mu = identity
#> Formula: bmi | mi() ~ age * mi(chl)
#> chl | mi(se) ~ age
#> Data: nhanes (Number of observations: 25)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> bmi_Intercept 13.48 9.04 -4.31 31.41 1.00 2293 2045
#> chl_Intercept 137.46 22.50 91.23 180.96 1.00 3154 2853
#> bmi_age -4.57 5.73 -16.17 6.50 1.00 1984 2299
#> chl_age 31.29 11.57 9.00 55.20 1.00 3214 2846
#> bmi_michl 0.12 0.05 0.03 0.21 1.00 2312 1650
#> bmi_michl:age -0.01 0.03 -0.06 0.05 1.00 2027 2244
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma_bmi 3.30 0.75 2.19 5.07 1.00 1615 2494
#> sigma_chl 36.85 6.55 26.42 52.21 1.00 2828 2360
#>
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
plot(conditional_effects(fit2, resp = "bmi"), ask = FALSE)
# 'mi' terms can also be used when some responses are subsetted
nhanes$sub <- TRUE
nhanes$sub[1:2] <- FALSE
nhanes$id <- 1:N
nhanes$idx <- sample(3:N, N, TRUE)
# this requires the addition term 'index' being specified
# in the subsetted part of the model
bform3 <- bf(bmi | mi() ~ age * mi(chl, idx)) +
bf(chl | mi(se) + subset(sub) + index(id) ~ age) +
set_rescor(FALSE)
fit3 <- brm(bform3, data = nhanes)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
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#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
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#> Chain 4: 3.031 seconds (Total)
#> Chain 4:
#> Warning: There were 2 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.53, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
summary(fit3)
#> Warning: Inference for the model posterior has not converged (some Rhats are > 1.05). Be careful when analysing the results! We recommend running more iterations or setting stronger priors.
#> Warning: There were 2 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> Family: MV(gaussian, gaussian)
#> Links: mu = identity
#> mu = identity
#> Formula: bmi | mi() ~ age * mi(chl, idx)
#> chl | mi(se) + subset(sub) + index(id) ~ age
#> Data: nhanes (Number of observations: 25)
#> Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup draws = 4000
#>
#> Regression Coefficients:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> bmi_Intercept 19.71 17.80 -17.06 54.62 1.03 365 1085
#> chl_Intercept 112.63 82.81 -118.55 196.65 1.38 11 30
#> bmi_age -7.77 10.89 -30.00 12.52 1.01 691 1095
#> chl_age 15.96 32.92 -73.98 67.69 1.24 105 30
#> bmi_michlidx 0.07 0.10 -0.13 0.27 1.03 484 951
#> bmi_michlidx:age 0.03 0.06 -0.08 0.15 1.01 668 1093
#>
#> Further Distributional Parameters:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma_bmi 3.75 0.92 2.40 5.82 1.01 1164 1664
#> sigma_chl 75.96 65.64 29.15 229.88 1.53 7 31
#>
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
plot(conditional_effects(fit3, resp = "bmi"), ask = FALSE)
#> Error in .frame_index.brmsterms(X[[i]], ...): Index of response 'chl' contains duplicated values.
# }