-
Notifications
You must be signed in to change notification settings - Fork 27
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add names of random effects in tidy() #337
Comments
@sschooler This is now a separate issue and we'll get to this eventually. At the same time, we should add better names for the ranges (@ecophilina). In the meantime, here's an example: library(sdmTMB)
x <- runif(500, -1, 1)
y <- runif(500, -1, 1)
loc <- data.frame(x = x, y = y)
mesh <- make_mesh(loc, c("x", "y"), n_knots = 50, type = "kmeans")
s <- sdmTMB_simulate(
~1,
data = loc,
mesh = mesh,
range = 1.4,
phi = 0.1,
sigma_O = 0.2,
seed = 1,
B = 0
)
g <- rep(gl(30, 10), 999)
set.seed(134)
RE_vals <- rnorm(30, 0, 0.4)
h <- rep(gl(40, 10), 999)
set.seed(1283)
RE_vals2 <- rnorm(40, 0, 0.2)
s$g <- g[seq_len(nrow(s))]
s$h <- h[seq_len(nrow(s))]
s$observed <- s$observed + RE_vals[s$g] + RE_vals2[s$h]
fit <- sdmTMB(
data = s, time = NULL,
formula = observed ~ 1 + (1 | g) + (1 | h), mesh = mesh
)
# you can see them here with names to match up:
fit
#> Spatial model fit by ML ['sdmTMB']
#> Formula: observed ~ 1 + (1 | g) + (1 | h)
#> Mesh: mesh (isotropic covariance)
#> Data: s
#> Family: gaussian(link = 'identity')
#>
#> coef.est coef.se
#> (Intercept) 0.22 0.15
#>
#> Random intercepts:
#> Std. Dev.
#> g 0.44
#> h 0.17
#>
#> Dispersion parameter: 0.10
#> Matérn range: 1.25
#> Spatial SD: 0.18
#> ML criterion at convergence: -253.583
#>
#> See ?tidy.sdmTMB to extract these values as a data frame.
# yes, it would be helpful if these were named better:
tidy(fit, 'ran_pars')
#> # A tibble: 5 × 3
#> term estimate std.error
#> <chr> <dbl> <dbl>
#> 1 range 1.25 0.364
#> 2 phi 0.103 0.00369
#> 3 sigma_O 0.180 0.0349
#> 4 sigma_G 0.436 0.0596
#> 5 sigma_G 0.175 0.0275
# you can currently grab them from:
fit$split_formula[[1]]$barnames # this internal structure may someday change
#> [1] "g" "h"
# or access just the relevant printing function (which uses the above):
sdmTMB:::print_iid_re(fit)
#> Std. Dev.
#> g 0.44
#> h 0.17
# here (the values themselves) they are named:
tidy(fit, 'ran_vals')
#> # A tibble: 70 × 3
#> term estimate std.error
#> <chr> <dbl> <dbl>
#> 1 g_1 -0.295 0.126
#> 2 g_2 0.691 0.126
#> 3 g_3 0.0805 0.126
#> 4 g_4 -0.307 0.126
#> 5 g_5 -0.604 0.126
#> 6 g_6 0.0498 0.126
#> 7 g_7 0.556 0.126
#> 8 g_8 -0.0244 0.126
#> 9 g_9 0.0985 0.126
#> 10 g_10 0.660 0.126
#> # ℹ 60 more rows Created on 2024-04-22 with reprex v2.1.0 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Originally posted by @sschooler in #41 (comment)
The text was updated successfully, but these errors were encountered: