Cross-validation code check
Adam Howes
Abstract
Background
Task
#' Test of cross-validation approach using missing data in Stan
loo_test_sets <- create_folds(mw, type = "LOO")
sloo_test_sets <- create_folds(mw, type = "SLOO")
#' What do the test sets look like?
cowplot::plot_grid(
plotlist = lapply(loo_test_sets, function(x) {
x$data %>%
ggplot(aes(fill = y)) +
geom_sf() +
theme_void() +
theme(
legend.position = "none"
)
}),
ncol = 7
)
cowplot::plot_grid(
plotlist = lapply(sloo_test_sets, function(x) {
x$data %>%
ggplot(aes(fill = y)) +
geom_sf() +
theme_void() +
theme(
legend.position = "none"
)
}),
ncol = 7
)
#' Try fitting one of them in Stan
sf <- loo_test_sets[[1]]$data
#' Index of observations which are not missing
ii_obs <- which(!is.na(sf$y))
#' Index of missing observations
ii_mis <- which(is.na(sf$y))
#' Number of not missing observations
n_obs <- length(ii_obs)
#' Number of missing observations
n_mis <- length(ii_mis)
dat <- list(
n_obs = n_obs,
n_mis = n_mis,
ii_obs = array(ii_obs),
ii_mis = array(ii_mis),
n = nrow(sf),
y_obs = sf$y[ii_obs],
m = sf$n_obs
)
temp <- rstan::stan(file = "constant.stan", data = dat, warmup = 100, iter = 900)##
## SAMPLING FOR MODEL 'constant' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 5.7e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.57 seconds.
## Chain 1: Adjust your expectations accordingly!
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## Chain 1: WARNING: There aren't enough warmup iterations to fit the
## Chain 1: three stages of adaptation as currently configured.
## Chain 1: Reducing each adaptation stage to 15%/75%/10% of
## Chain 1: the given number of warmup iterations:
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##
## SAMPLING FOR MODEL 'constant' NOW (CHAIN 2).
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## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: WARNING: There aren't enough warmup iterations to fit the
## Chain 2: three stages of adaptation as currently configured.
## Chain 2: Reducing each adaptation stage to 15%/75%/10% of
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## Chain 2: init_buffer = 15
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## Chain 2:
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## SAMPLING FOR MODEL 'constant' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 2.9e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
## Chain 3:
## Chain 3: WARNING: There aren't enough warmup iterations to fit the
## Chain 3: three stages of adaptation as currently configured.
## Chain 3: Reducing each adaptation stage to 15%/75%/10% of
## Chain 3: the given number of warmup iterations:
## Chain 3: init_buffer = 15
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## Chain 3:
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## SAMPLING FOR MODEL 'constant' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 2.7e-05 seconds
## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
## Chain 4: Adjust your expectations accordingly!
## Chain 4:
## Chain 4:
## Chain 4: WARNING: There aren't enough warmup iterations to fit the
## Chain 4: three stages of adaptation as currently configured.
## Chain 4: Reducing each adaptation stage to 15%/75%/10% of
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## Chain 4: init_buffer = 15
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## Chain 4: 0.21117 seconds (Total)
## Chain 4:rstan::summary(temp)$summary## mean se_mean sd 2.5% 25% 50% 75%
## y_mis[1] 39.32631322 2.382316e-01 5.897314232 28.54019813 35.11108349 39.03756346 43.2358606
## beta_0 -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[1] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[2] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[3] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[4] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[5] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[6] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[7] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[8] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[9] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[10] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[11] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[12] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[13] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[14] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[15] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[16] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[17] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[18] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[19] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[20] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[21] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[22] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[23] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[24] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[25] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[26] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[27] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## eta[28] -2.35479527 5.528449e-04 0.031133175 -2.41588845 -2.37650492 -2.35539917 -2.3332747
## y[1] 39.32631322 2.382316e-01 5.897314232 28.54019813 35.11108349 39.03756346 43.2358606
## y[2] 29.54181354 0.000000e+00 0.000000000 29.54181354 29.54181354 29.54181354 29.5418135
## y[3] 49.56848581 0.000000e+00 0.000000000 49.56848581 49.56848581 49.56848581 49.5684858
## y[4] 25.41303769 0.000000e+00 0.000000000 25.41303769 25.41303769 25.41303769 25.4130377
## y[5] 46.93140798 NaN 0.000000000 46.93140798 46.93140798 46.93140798 46.9314080
## y[6] 28.46503770 0.000000e+00 0.000000000 28.46503770 28.46503770 28.46503770 28.4650377
## y[7] 89.86246516 0.000000e+00 0.000000000 89.86246516 89.86246516 89.86246516 89.8624652
## y[8] 26.83167324 0.000000e+00 0.000000000 26.83167324 26.83167324 26.83167324 26.8316732
## y[9] 21.08982854 0.000000e+00 0.000000000 21.08982854 21.08982854 21.08982854 21.0898285
## y[10] 36.46588664 0.000000e+00 0.000000000 36.46588664 36.46588664 36.46588664 36.4658866
## y[11] 25.13650708 0.000000e+00 0.000000000 25.13650708 25.13650708 25.13650708 25.1365071
## y[12] 93.79745832 0.000000e+00 0.000000000 93.79745832 93.79745832 93.79745832 93.7974583
## y[13] 32.68935853 0.000000e+00 0.000000000 32.68935853 32.68935853 32.68935853 32.6893585
## y[14] 38.28128687 0.000000e+00 0.000000000 38.28128687 38.28128687 38.28128687 38.2812869
## y[15] 36.02050396 0.000000e+00 0.000000000 36.02050396 36.02050396 36.02050396 36.0205040
## y[16] 18.16454375 0.000000e+00 0.000000000 18.16454375 18.16454375 18.16454375 18.1645438
## y[17] 70.63350982 0.000000e+00 0.000000000 70.63350982 70.63350982 70.63350982 70.6335098
## y[18] 25.83200246 0.000000e+00 0.000000000 25.83200246 25.83200246 25.83200246 25.8320025
## y[19] 13.51610181 0.000000e+00 0.000000000 13.51610181 13.51610181 13.51610181 13.5161018
## y[20] 58.71062406 0.000000e+00 0.000000000 58.71062406 58.71062406 58.71062406 58.7106241
## y[21] 70.58567052 0.000000e+00 0.000000000 70.58567052 70.58567052 70.58567052 70.5856705
## y[22] 34.57662815 0.000000e+00 0.000000000 34.57662815 34.57662815 34.57662815 34.5766281
## y[23] 35.97166622 0.000000e+00 0.000000000 35.97166622 35.97166622 35.97166622 35.9716662
## y[24] 11.12088361 0.000000e+00 0.000000000 11.12088361 11.12088361 11.12088361 11.1208836
## y[25] 15.22465643 0.000000e+00 0.000000000 15.22465643 15.22465643 15.22465643 15.2246564
## y[26] 19.47180929 0.000000e+00 0.000000000 19.47180929 19.47180929 19.47180929 19.4718093
## y[27] 41.00921639 0.000000e+00 0.000000000 41.00921639 41.00921639 41.00921639 41.0092164
## y[28] 22.20170783 0.000000e+00 0.000000000 22.20170783 22.20170783 22.20170783 22.2017078
## log_lik[1] -5.71010969 2.142435e-02 0.643429796 -7.57833227 -5.87267843 -5.45943424 -5.2934677
## log_lik[2] -5.27796568 1.268113e-03 0.070489729 -5.45275287 -5.31676291 -5.26220246 -5.2237735
## log_lik[3] -6.28627413 4.875740e-03 0.274902410 -6.89906216 -6.46036307 -6.25477626 -6.0833544
## log_lik[4] -6.52186682 4.643663e-03 0.262086354 -7.08121876 -6.69407756 -6.50142040 -6.3321250
## log_lik[5] -6.22570588 5.005546e-03 0.279842629 -6.82186354 -6.41156896 -6.21388807 -6.0236790
## log_lik[6] -9.70496859 9.764405e-03 0.550961631 -10.84976365 -10.07399112 -9.67346549 -9.3110084
## log_lik[7] -35.86469432 2.957595e-02 1.664159660 -39.17712263 -37.01683395 -35.88043647 -34.7055216
## log_lik[8] -5.27375045 1.392521e-03 0.077876212 -5.46134650 -5.31872451 -5.25891326 -5.2144550
## log_lik[9] -16.80332850 1.582082e-02 0.892301878 -18.62602609 -17.40759491 -16.76361077 -16.1704020
## log_lik[10] -6.01794724 4.260194e-03 0.238401710 -6.52191626 -6.17714725 -6.00937361 -5.8465236
## log_lik[11] -6.23760530 4.067554e-03 0.229562454 -6.73105156 -6.38761498 -6.21837538 -6.0708685
## log_lik[12] -28.68020925 2.760638e-02 1.552926898 -31.78365042 -29.75307780 -28.69047280 -27.5961903
## log_lik[13] -5.63830665 2.644377e-03 0.148909742 -5.97711008 -5.73069773 -5.61848163 -5.5274256
## log_lik[14] -6.80685223 5.743657e-03 0.322118111 -7.47442630 -7.02477691 -6.80029601 -6.5776173
## log_lik[15] -5.29575614 9.369281e-04 0.050098494 -5.43152119 -5.31700658 -5.27878651 -5.2584173
## log_lik[16] -9.38215350 7.951511e-03 0.448605103 -10.30840351 -9.68387832 -9.35863089 -9.0623320
## log_lik[17] -18.50038456 1.877916e-02 1.056067179 -20.61973323 -19.22835556 -18.50419409 -17.7615455
## log_lik[18] -6.42553709 4.477652e-03 0.252714288 -6.96648465 -6.59121383 -6.40522169 -6.2423330
## log_lik[19] -15.63019914 1.276867e-02 0.720092993 -17.09703566 -16.11868625 -15.59960735 -15.1200984
## log_lik[20] -8.88927856 9.935893e-03 0.557690014 -10.03512545 -9.26858200 -8.88160380 -8.4942007
## log_lik[21] -11.81602127 1.409051e-02 0.791562396 -13.42646422 -12.35751365 -11.81095774 -11.2581781
## log_lik[22] -5.47119029 1.966523e-03 0.110226725 -5.73297879 -5.53614888 -5.45185715 -5.3876977
## log_lik[23] -5.35470885 2.155052e-03 0.119037901 -5.62654711 -5.42917060 -5.34230706 -5.2657454
## log_lik[24] -12.52775978 9.536441e-03 0.537836411 -13.62491945 -12.89228580 -12.50434656 -12.1465052
## log_lik[25] -15.42721074 1.308762e-02 0.738107802 -16.93240784 -15.92757743 -15.39525999 -14.9040739
## log_lik[26] -15.32940145 1.410149e-02 0.795345939 -16.95501297 -15.86781017 -15.29365428 -14.7650888
## log_lik[27] -5.85655394 3.947006e-03 0.220355124 -6.33087746 -6.00179653 -5.84535421 -5.6966235
## log_lik[28] -12.81016049 1.230070e-02 0.693874253 -14.23494524 -13.27851168 -12.77661765 -12.3167656
## rho[1] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[2] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[3] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[4] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[5] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[6] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[7] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[8] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[9] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[10] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[11] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[12] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[13] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## rho[14] 0.08671708 4.375198e-05 0.002466054 0.08196912 0.08498195 0.08663757 0.0884044
## 97.5% n_eff Rhat
## y_mis[1] 52.03921313 612.787445 1.0018539
## beta_0 -2.29342473 3171.321210 1.0006016
## eta[1] -2.29342473 3171.321210 1.0006016
## eta[2] -2.29342473 3171.321210 1.0006016
## eta[3] -2.29342473 3171.321210 1.0006016
## eta[4] -2.29342473 3171.321210 1.0006016
## eta[5] -2.29342473 3171.321210 1.0006016
## eta[6] -2.29342473 3171.321210 1.0006016
## eta[7] -2.29342473 3171.321210 1.0006016
## eta[8] -2.29342473 3171.321210 1.0006016
## eta[9] -2.29342473 3171.321210 1.0006016
## eta[10] -2.29342473 3171.321210 1.0006016
## eta[11] -2.29342473 3171.321210 1.0006016
## eta[12] -2.29342473 3171.321210 1.0006016
## eta[13] -2.29342473 3171.321210 1.0006016
## eta[14] -2.29342473 3171.321210 1.0006016
## eta[15] -2.29342473 3171.321210 1.0006016
## eta[16] -2.29342473 3171.321210 1.0006016
## eta[17] -2.29342473 3171.321210 1.0006016
## eta[18] -2.29342473 3171.321210 1.0006016
## eta[19] -2.29342473 3171.321210 1.0006016
## eta[20] -2.29342473 3171.321210 1.0006016
## eta[21] -2.29342473 3171.321210 1.0006016
## eta[22] -2.29342473 3171.321210 1.0006016
## eta[23] -2.29342473 3171.321210 1.0006016
## eta[24] -2.29342473 3171.321210 1.0006016
## eta[25] -2.29342473 3171.321210 1.0006016
## eta[26] -2.29342473 3171.321210 1.0006016
## eta[27] -2.29342473 3171.321210 1.0006016
## eta[28] -2.29342473 3171.321210 1.0006016
## y[1] 52.03921313 612.787445 1.0018539
## y[2] 29.54181354 2.003757 0.9987492
## y[3] 49.56848581 2.003757 0.9987492
## y[4] 25.41303769 2.003757 0.9987492
## y[5] 46.93140798 NaN 0.9987492
## y[6] 28.46503770 2.003757 0.9987492
## y[7] 89.86246516 2.003757 0.9987492
## y[8] 26.83167324 2.003757 0.9987492
## y[9] 21.08982854 2.003757 0.9987492
## y[10] 36.46588664 2.003757 0.9987492
## y[11] 25.13650708 2.003757 0.9987492
## y[12] 93.79745832 2.003757 0.9987492
## y[13] 32.68935853 2.003757 0.9987492
## y[14] 38.28128687 2.003757 0.9987492
## y[15] 36.02050396 2.003757 0.9987492
## y[16] 18.16454375 2.003757 0.9987492
## y[17] 70.63350982 2.003757 0.9987492
## y[18] 25.83200246 2.003757 0.9987492
## y[19] 13.51610181 2.003757 0.9987492
## y[20] 58.71062406 2.003757 0.9987492
## y[21] 70.58567052 2.003757 0.9987492
## y[22] 34.57662815 2.003757 0.9987492
## y[23] 35.97166622 2.003757 0.9987492
## y[24] 11.12088361 2.003757 0.9987492
## y[25] 15.22465643 2.003757 0.9987492
## y[26] 19.47180929 2.003757 0.9987492
## y[27] 41.00921639 2.003757 0.9987492
## y[28] 22.20170783 2.003757 0.9987492
## log_lik[1] -5.23850822 901.959097 1.0017617
## log_lik[2] -5.18769056 3089.837475 1.0003802
## log_lik[3] -5.82651483 3178.894082 1.0004991
## log_lik[4] -6.05320150 3185.425727 1.0005389
## log_lik[5] -5.72602439 3125.538653 1.0006646
## log_lik[6] -8.68475089 3183.839326 1.0005614
## log_lik[7] -32.63243493 3166.012742 1.0006120
## log_lik[8] -5.16364972 3127.565422 1.0004160
## log_lik[9] -15.11738097 3181.013428 1.0005750
## log_lik[10] -5.58760182 3131.554740 1.0006584
## log_lik[11] -5.83119681 3185.187162 1.0005327
## log_lik[12] -25.67699779 3164.338726 1.0006150
## log_lik[13] -5.39833119 3171.023395 1.0004787
## log_lik[14] -6.20991786 3145.236886 1.0006427
## log_lik[15] -5.25082671 2859.149463 1.0002475
## log_lik[16] -8.54509666 3182.944889 1.0005665
## log_lik[17] -16.46740057 3162.503322 1.0006181
## log_lik[18] -5.97548844 3185.362886 1.0005364
## log_lik[19] -14.26532200 3180.431391 1.0005772
## log_lik[20] -7.84463965 3150.444914 1.0006360
## log_lik[21] -10.31577233 3155.855224 1.0006284
## log_lik[22] -5.30945082 3141.779762 1.0004325
## log_lik[23] -5.16613622 3051.089016 1.0007235
## log_lik[24] -11.50999784 3180.736872 1.0005761
## log_lik[25] -14.02993711 3180.666427 1.0005763
## log_lik[26] -13.82766538 3181.133307 1.0005745
## log_lik[27] -5.46905399 3116.812230 1.0006731
## log_lik[28] -11.50699747 3182.015151 1.0005709
## rho[1] 0.09166899 3176.945243 1.0005881
## rho[2] 0.09166899 3176.945243 1.0005881
## rho[3] 0.09166899 3176.945243 1.0005881
## rho[4] 0.09166899 3176.945243 1.0005881
## rho[5] 0.09166899 3176.945243 1.0005881
## rho[6] 0.09166899 3176.945243 1.0005881
## rho[7] 0.09166899 3176.945243 1.0005881
## rho[8] 0.09166899 3176.945243 1.0005881
## rho[9] 0.09166899 3176.945243 1.0005881
## rho[10] 0.09166899 3176.945243 1.0005881
## rho[11] 0.09166899 3176.945243 1.0005881
## rho[12] 0.09166899 3176.945243 1.0005881
## rho[13] 0.09166899 3176.945243 1.0005881
## rho[14] 0.09166899 3176.945243 1.0005881
## [ reached getOption("max.print") -- omitted 15 rows ]out <- rstan::extract(temp)
#' Could add truth to this plot and remake in ggplot2
plot(out$y_mis)