Print details of the fitted DI models supplied
Usage
# S3 method for class 'DImulti'
print(x, ...)Details
The appearance of the printed information will differ depending on whether a user has installed some combination of the suggested packages "crayon", "cli", and "fansi". These changes are mainly cosmetic, with crayon making the output easier to read, cli providing links to help files, and fansi enabling the reading of special characters in R markdown (Rmd) files. See 'Examples' below for suggested code to include in Rmd files.
See also
print which this function wraps.
Examples
## Set up for R markdown for crayon and cli output if user has packages installed
if(requireNamespace("fansi", quietly = TRUE) &
requireNamespace("crayon", quietly = TRUE) &
requireNamespace("cli", quietly = TRUE))
{
options(crayon.enabled = TRUE)
ansi_aware_handler <- function(x, options)
{
paste0(
"<pre class=\"r-output\"><code>",
fansi::sgr_to_html(x = x, warn = FALSE, term.cap = "256"),
"</code></pre>"
)
}
old_hooks <- fansi::set_knit_hooks(knitr::knit_hooks,
which = c("output", "message", "error", "warning"))
knitr::knit_hooks$set(
output = ansi_aware_handler,
message = ansi_aware_handler,
warning = ansi_aware_handler,
error = ansi_aware_handler
)
}
#> <STYLE type='text/css' scoped>
#> PRE.fansi SPAN {padding-top: .25em; padding-bottom: .25em};
#> </STYLE>
#################################################################################################
## Usage
model <- DImulti(prop = c("G1", "G2", "L1", "L2"), y = "Y", eco_func = c("Var", "un"),
unit_IDs = "Plot", theta = c(0.5, 1, 1.2), DImodel = "FG",
FG = c("Grass", "Grass", "Legume", "Legume"), extra_fixed = ~ Density,
method = "REML", data = dataBEL)
print(model)
#> Note:
#> Method Used = REML
#> Correlation Structure Used = un
#> Functional Group Model
#> Theta value(s) = N:0.5, Sown:1, Weed:1.2
#>
#> Generalized least squares fit by REML
#> Model: Y ~ 0 + Var:((G1_ID + G2_ID + L1_ID + L2_ID + FG.bfg_Grass_Legume + FG.wfg_Grass + FG.wfg_Legume) + Density)
#> AIC BIC logLik
#> 488.8132 554.5029 -214.4066
#>
#> Multivariate Correlation Structure: General
#> Formula: ~0 | Plot
#> Parameter estimate(s):
#> Correlation:
#> 1 2
#> 2 0.797
#> 3 0.065 0.523
#>
#>
#> Table: Fixed Effect Coefficients
#>
#> Beta Std. Error t-value p-value Signif
#> ---------------------------- --------- ----------- -------- ---------- -------
#> VarN:G1_ID +44.867 4.209 10.661 5.433e-16 ***
#> VarSown:G1_ID +65.337 4.656 14.033 1.745e-21 ***
#> VarWeed:G1_ID +79.372 9.039 8.781 1.065e-12 ***
#> VarN:G2_ID +28.926 4.209 6.873 2.722e-09 ***
#> VarSown:G2_ID +46.808 4.656 10.053 6.096e-15 ***
#> VarWeed:G2_ID +90.654 9.039 10.029 6.706e-15 ***
#> VarN:L1_ID +98.246 4.209 23.344 1.322e-33 ***
#> VarSown:L1_ID +76.396 4.656 16.408 5.561e-25 ***
#> VarWeed:L1_ID +50.032 9.039 5.535 5.782e-07 ***
#> VarN:L2_ID +77.067 4.209 18.312 1.438e-27 ***
#> VarSown:L2_ID +51.062 4.656 10.967 1.629e-16 ***
#> VarWeed:L2_ID +33.951 9.039 3.756 0.0003674 ***
#> VarN:FG.bfg_Grass_Legume +14.538 13.281 1.095 0.2777
#> VarSown:FG.bfg_Grass_Legume +86.492 18.699 4.625 1.796e-05 ***
#> VarWeed:FG.bfg_Grass_Legume +152.157 49.542 3.071 0.003095 **
#> VarN:FG.wfg_Grass +58.483 28.072 2.083 0.0411 *
#> VarSown:FG.wfg_Grass +107.670 41.360 2.603 0.0114 *
#> VarWeed:FG.wfg_Grass -9.702 108.079 -0.090 0.9287
#> VarN:FG.wfg_Legume -16.215 28.072 -0.578 0.5655
#> VarSown:FG.wfg_Legume +60.847 41.360 1.471 0.146
#> VarWeed:FG.wfg_Legume +242.472 108.079 2.243 0.02823 *
#> VarN:Density1 -1.261 2.348 -0.537 0.593
#> VarSown:Density1 +2.290 2.618 0.875 0.3849
#> VarWeed:Density1 +0.991 5.083 0.195 0.846
#>
#> Signif codes: 0-0.001 '***', 0.001-0.01 '**', 0.01-0.05 '*', 0.05-0.1 '+', 0.1-1.0 ' '
#>
#> Degrees of freedom: 90 total; 66 residual
#> Residual standard error: 6.428977
#>
#> Marginal variance covariance matrix
#> N Sown Weed
#> N 41.3320 36.716 5.8403
#> Sown 36.7160 51.407 52.2200
#> Weed 5.8403 52.220 193.7900
#> Standard Deviations: 6.429 7.1699 13.921