library(DImodels)
data(sim2)
# Fit model
mod <- DI(y = "response", prop = 3:6, DImodel = "AV", data = sim2)
#> Fitted model: Average interactions 'AV' DImodel
# Create plot
# Move from p3 monoculture to p4 monoculture
simplex_path(model = mod,
starts = data.frame(p1 = 0, p2 = 0, p3 = 1, p4 = 0),
ends = data.frame(p1 = 0, p2 = 0, p3 = 0, p4 = 1))
#> ✔ Finished data preparation.
#> ✔ Created plot.
# Move from each 70% dominant mixtures to p1 monoculture
simplex_path(model = mod,
starts = sim2[c(1, 5, 9, 13), 3:6],
ends = data.frame(p1 = 1, p2 = 0, p3 = 0, p4 = 0))
#> ✔ Finished data preparation.
#> ✔ Created plot.
# Move from centroid community to each monoculture
simplex_path(model = mod,
starts = sim2[c(18),],
ends = sim2[c(48, 52, 56, 60), ])
#> ✔ Finished data preparation.
#> ✔ Created plot.
# Show change across multiple points simultaneously and show confidence bands
# using `se = TRUE`
simplex_path(model = mod,
starts = sim2[c(1, 17, 22), ],
ends = sim2[c(5, 14, 17), ], se = TRUE)
#> ✔ Finished data preparation.
#> ✔ Created plot.
# Change pie_colours using `pie_colours` and show pie-glyph at different
# points along the curve using `pie_positions`
simplex_path(model = mod,
starts = sim2[c(1, 17, 22), ],
ends = sim2[c(5, 14, 17), ], se = TRUE,
pie_positions = c(0, 0.25, 0.5, 0.75, 1),
pie_colours = c("steelblue1", "steelblue4", "orange1", "orange4"))
#> ✔ Finished data preparation.
#> ✔ Created plot.
# Facet based on existing variables
# \donttest{
simplex_path(model = mod,
starts = sim2[c(1, 17, 22), ],
ends = sim2[c(5, 14, 17), ], se = TRUE, facet_var = "block",
pie_colours = c("steelblue1", "steelblue4", "orange1", "orange4"))
#> ✔ Finished data preparation.
#> ✔ Created plot.
# Add additional variables and create a separate plot for each
simplex_path(model = mod,
starts = sim2[c(1, 17, 22), 3:6],
ends = sim2[c(5, 14, 17), 3:6], se = TRUE,
pie_colours = c("steelblue1", "steelblue4", "orange1", "orange4"),
add_var = list("block" = factor(c(1, 3),
levels = c(1, 2, 3, 4))))
#> ✔ Finished data preparation.
#> ✔ Created all plots.
# }
## Specify `plot = FALSE` to not create the plot but return the prepared data
head(simplex_path(model = mod, plot = FALSE,
starts = sim2[c(1, 17, 22), 3:6],
ends = sim2[c(5, 14, 17), 3:6], se = TRUE,
pie_colours = c("steelblue1", "steelblue4",
"orange1", "orange4"),
add_var = list("block" = factor(c(1, 3),
levels = c(1, 2, 3, 4)))))
#> ✔ Finished data preparation.
#> p1 p2 p3 p4 .InterpConst .Group block .add_str_ID .Pred .Lower
#> 1 0.700 0.100 0.1 0.1 0.00 1 1 block: 1 18.19429 17.45473
#> 2 0.694 0.106 0.1 0.1 0.01 1 1 block: 1 18.30217 17.56890
#> 3 0.688 0.112 0.1 0.1 0.02 1 1 block: 1 18.40775 17.68057
#> 4 0.682 0.118 0.1 0.1 0.03 1 1 block: 1 18.51103 17.78973
#> 5 0.676 0.124 0.1 0.1 0.04 1 1 block: 1 18.61200 17.89639
#> 6 0.670 0.130 0.1 0.1 0.05 1 1 block: 1 18.71068 18.00055
#> .Upper
#> 1 18.93385
#> 2 19.03544
#> 3 19.13494
#> 4 19.23233
#> 5 19.32762
#> 6 19.42080
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