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Predictors with Measurement Error in brms Models

Source: R/formula-sp.R
me.Rd

(Soft deprecated) Specify predictors with measurement error. The function does not evaluate its arguments – it exists purely to help set up a model.

Usage

me(x, sdx, gr = NULL)

Arguments

x

The variable measured with error.

sdx

Known measurement error of x treated as standard deviation.

gr

Optional grouping factor to specify which values of x correspond to the same value of the latent variable. If NULL (the default) each observation will have its own value of the latent variable.

Details

For detailed documentation see help(brmsformula). me terms are soft deprecated in favor of the more general and consistent mi terms. By default, latent noise-free variables are assumed to be correlated. To change that, add set_mecor(FALSE) to your model formula object (see examples).

See also

brmsformula, brmsformula-helpers

Examples

# \dontrun{
# sample some data
N <- 100
dat <- data.frame(
  y = rnorm(N), x1 = rnorm(N),
  x2 = rnorm(N), sdx = abs(rnorm(N, 1))
 )
# fit a simple error-in-variables model
fit1 <- brm(y ~ me(x1, sdx) + me(x2, sdx), data = dat,
            save_pars = save_pars(latent = TRUE))
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 2.829 seconds (Warm-up)
#> Chain 1:                1.8 seconds (Sampling)
#> Chain 1:                4.629 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 2.882 seconds (Warm-up)
#> Chain 2:                1.693 seconds (Sampling)
#> Chain 2:                4.575 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 2.773 seconds (Warm-up)
#> Chain 3:                1.702 seconds (Sampling)
#> Chain 3:                4.475 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2.1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 3.045 seconds (Warm-up)
#> Chain 4:                3.025 seconds (Sampling)
#> Chain 4:                6.07 seconds (Total)
#> Chain 4: 
summary(fit1)
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: y ~ me(x1, sdx) + me(x2, sdx) 
#>    Data: dat (Number of observations: 100) 
#>   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
#> Intercept     0.07      0.10    -0.13     0.27 1.00     2555     2499
#> mex1sdx       0.25      0.17    -0.11     0.58 1.00     1607     2677
#> mex2sdx      -0.10      0.16    -0.42     0.23 1.00     1886     2772
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma     0.91      0.07     0.78     1.06 1.00     2849     2640
#> 
#> 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).

# turn off modeling of correlations
bform <- bf(y ~ me(x1, sdx) + me(x2, sdx)) + set_mecor(FALSE)
fit2 <- brm(bform, data = dat, save_pars = save_pars(latent = TRUE))
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 2.401 seconds (Warm-up)
#> Chain 1:                1.548 seconds (Sampling)
#> Chain 1:                3.949 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 2.428 seconds (Warm-up)
#> Chain 2:                2.661 seconds (Sampling)
#> Chain 2:                5.089 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 2.367 seconds (Warm-up)
#> Chain 3:                2.208 seconds (Sampling)
#> Chain 3:                4.575 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 2.341 seconds (Warm-up)
#> Chain 4:                1.366 seconds (Sampling)
#> Chain 4:                3.707 seconds (Total)
#> Chain 4: 
summary(fit2)
#>  Family: gaussian 
#>   Links: mu = identity 
#> Formula: y ~ me(x1, sdx) + me(x2, sdx) 
#>    Data: dat (Number of observations: 100) 
#>   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
#> Intercept     0.07      0.11    -0.15     0.28 1.00     3429     2631
#> mex1sdx       0.24      0.18    -0.13     0.59 1.00     1827     2220
#> mex2sdx      -0.10      0.16    -0.42     0.21 1.00     1999     3127
#> 
#> Further Distributional Parameters:
#>       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sigma     0.91      0.07     0.78     1.06 1.00     4228     3188
#> 
#> 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).
# }