growthPlot {pcvr} | R Documentation |
Function to visualize models made by fitGrowth.
Description
Models fit using growthSS inputs by fitGrowth (and similar models made through other means) can be visualized easily using this function.
Usage
growthPlot(
fit,
form,
groups = NULL,
df = NULL,
timeRange = NULL,
facetGroups = TRUE,
groupFill = !facetGroups,
hierarchy_value = NULL
)
Arguments
fit |
A model fit object (or a list of |
form |
A formula similar to that in |
groups |
An optional set of groups to keep in the plot. Defaults to NULL in which case all groups in the model are plotted. |
df |
A dataframe to use in plotting observed growth curves on top of the model and for making predictions. |
timeRange |
An optional range of times to use. This can be used to view predictions for future data if the avaiable data has not reached some point (such as asymptotic size). |
facetGroups |
logical, should groups be separated in facets? Defaults to TRUE. |
groupFill |
logical, should groups have different colors? Defaults to the opposite of facetGroups. If TRUE then viridis colormaps are used in the order c('plasma', 'mako', 'viridis', 'inferno', 'cividis', 'magma', 'turbo', 'rocket'). Alternatively this can be given as a vector of viridis colormap names to use in a different order than above. Note that for brms models this is ignored except if used to specify a different viridis color map to use. |
hierarchy_value |
If a hierarchical model is being plotted, what value should the hiearchical predictor be? If left NULL (the default) the mean value is used. |
Value
Returns a ggplot showing a brms model's credible intervals and optionally the individual growth lines.
See Also
growthSS and fitGrowth for making compatible models, testGrowth for hypothesis testing on compatible models.
Examples
simdf <- growthSim("logistic",
n = 20, t = 25,
params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
)
ss <- growthSS(
model = "logistic", form = y ~ time | id / group,
df = simdf, type = "nls"
)
fit <- fitGrowth(ss)
growthPlot(fit, form = ss$pcvrForm, df = ss$df)