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Stanova does not like to be called with :: #4

Closed vandenman closed 4 years ago

vandenman commented 4 years ago

The first example from ?stanova::stanova crashes if you do not call library but instead do stanova::stanova. See below for a reprex.

data("warpbreaks")
fit_warp <- stanova::stanova(breaks ~ wool * tension, data = warpbreaks,
                    prior = rstanarm::student_t(3, 0, 3, autoscale = FALSE),
                    model_fun = "glm",
                    chains = 2, iter = 500)
#> Loading required namespace: rstanarm
#> Contrasts set to contr.bayes for the following variables: wool, tension
#> Error in get(ctr, mode = "function", envir = parent.frame()): object 'contr.bayes' of mode 'function' was not found

Created on 2020-09-18 by the reprex package (v0.3.0)

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singmann commented 4 years ago

Indeed, but honestly a rather minor issue that can be easily avoided. Nevertheless, it should now (1e764cb7fa9b1a60951dc68f13723138adc6cee7) work. However, you need to pass the contrast function as a function and not as the name of a function to check_contrasts (e.g., check_contrasts = stanova::contr.bayes):

fit_warp <- stanova::stanova(breaks ~ wool * tension, data = warpbreaks,
                    prior = rstanarm::student_t(3, 0, 3, autoscale = FALSE),
                    model_fun = "glm",
                    chains = 2, iter = 500)
#> Loading required namespace: rstanarm
#> Contrasts set to contr.bayes for the following variables: wool, tension
#> Error in get(ctr, mode = "function", envir = parent.frame()): object 'contr.bayes' of mode 'function' was not found

fit_warp <- stanova::stanova(breaks ~ wool * tension, data = warpbreaks,
                    prior = rstanarm::student_t(3, 0, 3, autoscale = FALSE),
                    model_fun = "glm",
                    chains = 2, iter = 500, 
                    check_contrasts = stanova::contr.bayes)
#> Contrasts set to passed contrast function for the following variables: wool, tension
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 500 [  0%]  (Warmup)
#> Chain 1: Iteration:  50 / 500 [ 10%]  (Warmup)
#> Chain 1: Iteration: 100 / 500 [ 20%]  (Warmup)
#> Chain 1: Iteration: 150 / 500 [ 30%]  (Warmup)
#> Chain 1: Iteration: 200 / 500 [ 40%]  (Warmup)
#> Chain 1: Iteration: 250 / 500 [ 50%]  (Warmup)
#> Chain 1: Iteration: 251 / 500 [ 50%]  (Sampling)
#> Chain 1: Iteration: 300 / 500 [ 60%]  (Sampling)
#> Chain 1: Iteration: 350 / 500 [ 70%]  (Sampling)
#> Chain 1: Iteration: 400 / 500 [ 80%]  (Sampling)
#> Chain 1: Iteration: 450 / 500 [ 90%]  (Sampling)
#> Chain 1: Iteration: 500 / 500 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.061 seconds (Warm-up)
#> Chain 1:                0.036 seconds (Sampling)
#> Chain 1:                0.097 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:   1 / 500 [  0%]  (Warmup)
#> Chain 2: Iteration:  50 / 500 [ 10%]  (Warmup)
#> Chain 2: Iteration: 100 / 500 [ 20%]  (Warmup)
#> Chain 2: Iteration: 150 / 500 [ 30%]  (Warmup)
#> Chain 2: Iteration: 200 / 500 [ 40%]  (Warmup)
#> Chain 2: Iteration: 250 / 500 [ 50%]  (Warmup)
#> Chain 2: Iteration: 251 / 500 [ 50%]  (Sampling)
#> Chain 2: Iteration: 300 / 500 [ 60%]  (Sampling)
#> Chain 2: Iteration: 350 / 500 [ 70%]  (Sampling)
#> Chain 2: Iteration: 400 / 500 [ 80%]  (Sampling)
#> Chain 2: Iteration: 450 / 500 [ 90%]  (Sampling)
#> Chain 2: Iteration: 500 / 500 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.093 seconds (Warm-up)
#> Chain 2:                0.035 seconds (Sampling)
#> Chain 2:                0.128 seconds (Total)
#> Chain 2:
summary(fit_warp)
#> 
#> Model Info:
#>  function:     stanova_glm
#>  family:       gaussian [identity]
#>  formula:      breaks ~ wool * tension
#>  algorithm:    sampling
#>  chains:       2
#>  sample:       500 (posterior sample size)
#>  priors:       see help('prior_summary', package = 'rstanarm')
#>  observations: 54
#> 
#> Estimate Intercept:
#>      Variable   Mean MAD_SD     5%    50%    95%  rhat ess_bulk ess_tail
#> 1 (Intercept) 28.027  1.662 25.514 28.069 30.656 1.004  482.179  357.213
#> 
#> 
#> Estimates 'wool' - difference from intercept:
#>   Variable   Mean MAD_SD     5%    50%   95%  rhat ess_bulk ess_tail
#> 1   wool A  1.963  1.328 -0.162  1.899 4.131 1.002  353.813  377.590
#> 2   wool B -1.963  1.328 -4.131 -1.899 0.162 1.002  353.813  377.590
#> 
#> Estimates 'tension' - difference from intercept:
#>    Variable   Mean MAD_SD     5%    50%    95%  rhat ess_bulk ess_tail
#> 1 tension L  6.312  2.390  2.640  6.252 10.133 1.003  398.264  357.729
#> 2 tension M -1.735  1.968 -4.888 -1.736  1.052 1.009  564.715  379.546
#> 3 tension H -4.576  1.803 -7.760 -4.511 -1.803 1.003  402.858  408.003
#> 
#> Estimates 'wool:tension' - difference from intercept:
#>            Variable   Mean MAD_SD      5%    50%    95%  rhat ess_bulk ess_tail
#> 1 wool A, tension L 11.212  3.536   5.837 11.145 16.415 1.006  340.548  336.503
#> 2 wool B, tension L  1.411  3.378  -3.605  1.329  6.487 0.998  418.178  349.710
#> 3 wool A, tension M -2.404  2.951  -7.211 -2.604  2.581 1.006  376.438  310.679
#> 4 wool B, tension M -1.067  2.615  -5.217 -1.201  3.377 1.002  476.715  424.271
#> 5 wool A, tension H -2.919  2.942  -7.445 -3.009  1.839 1.006  422.376  441.116
#> 6 wool B, tension H -6.233  2.745 -10.941 -6.019 -1.574 0.999  442.895  353.688

Created on 2020-09-18 by the reprex package (v0.3.0)