Posterior means (and optional quantiles) of
eta1 = beta0 + a11 w1, the log cell intensity per unit area;a11w1 = a11 w1, the density latent field;eta2 = beta1 + a21 w1 + a22 w2, the log expression per cell;a21w1 = a21 w1, the density-shared part of expression;a22w2 = a22 w2, the expression-specific part.
Value
Data frame with one row per grid cell: grid_index, i, j,
x, y, Y, M, the five fields, log_lambda1 and log_lambda2.
Details
The full linear predictors are log_lambda1 = log|g| + eta1 and
log_lambda2 = log(Y + 0.05) + eta2.
Examples
toy <- denmark_toy_data()
tissue <- denmark_alpha_shape(toy$x, toy$y)
tumor <- subset(toy, celltype == "Tumor", select = -celltype)
fit <- DenMark(tumor, gene = "geneA", boundary = tissue,
grid_size = 10, basis = 10,
chains = 2, iter_warmup = 300, iter_sampling = 300,
seed = 2024, quiet = TRUE)
fields <- denmark_fields(fit)
head(fields)
#> grid_index i j x y Y M eta1 a11w1 eta2
#> 1 3 3 1 1.891507 0.7530424 12 18 6.061543 0.4602119 0.5019067
#> 2 4 4 1 2.401581 0.7530424 29 63 5.720620 0.1192884 0.8170485
#> 3 5 5 1 2.911655 0.7530424 31 78 5.415823 -0.1855085 0.9240433
#> 4 6 6 1 3.421729 0.7530424 14 13 5.029744 -0.5715874 0.0353098
#> 5 7 7 1 3.931803 0.7530424 1 1 5.444391 -0.1569405 0.0186064
#> 6 12 2 2 1.381434 1.0078334 8 15 6.268291 0.6669597 0.7402424
#> a21w1 a22w2 log_lambda1 log_lambda2
#> 1 0.3874448 0.1213282 2.4511213 2.99097136
#> 2 0.1017701 0.7221446 3.3396454 4.18606696
#> 3 -0.1558314 1.0867410 3.2502438 4.35964207
#> 4 -0.4759146 0.5180906 2.6924697 2.67793219
#> 5 -0.1278886 0.1533612 0.7622484 0.06739657
#> 6 0.5620494 0.1850593 1.8897101 2.82591452