pcvr (version 1.2.0)

nlsPlot: Function to visualize common stats::nls growth models.

Description

Models fit using growthSS inputs by fitGrowth (and similar models made through other means) can be visualized easily using this function. This will generally be called by growthPlot.

Usage

nlsPlot(
  fit,
  form,
  df = NULL,
  groups = NULL,
  timeRange = NULL,
  facetGroups = TRUE,
  groupFill = FALSE,
  virMaps = c("plasma")
)
gamPlot(
  fit,
  form,
  df = NULL,
  groups = NULL,
  timeRange = NULL,
  facetGroups = TRUE,
  groupFill = FALSE,
  virMaps = c("plasma")
)
lmPlot(
  fit,
  form,
  df = NULL,
  groups = NULL,
  timeRange = NULL,
  facetGroups = TRUE,
  groupFill = FALSE,
  virMaps = c("plasma")
)

Value

Returns a ggplot showing an nls model's predictions.

Arguments

fit

A model fit returned by fitGrowth with type="nls".

form

A formula similar to that in growthSS inputs (or the pcvrForm part of the output) specifying the outcome, predictor, and grouping structure of the data as outcome ~ predictor|individual/group. If the individual and group are specified then the observed growth lines are plotted.

df

A dataframe to use in plotting observed growth curves on top of the model. This must be supplied for nls models.

groups

An optional set of groups to keep in the plot. Defaults to NULL in which case all groups in the model are plotted.

timeRange

An optional range of times to use. This can be used to view predictions for future data if the available 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 FALSE. If TRUE then viridis colormaps are used in the order of virMaps

virMaps

order of viridis maps to use. Will be recycled to necessary length. Defaults to "plasma", but will generally be informed by growthPlot's default.

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, start = NULL, type = "nls"
)
fit <- fitGrowth(ss)
nlsPlot(fit, form = ss$pcvrForm, df = ss$df, groupFill = TRUE)
nlsPlot(fit, form = ss$pcvrForm, df = ss$df, groups = "a", timeRange = 1:10)

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 = "gam", form = y ~ time | id / group,
  df = simdf, start = NULL, type = "nls"
)
fit <- fitGrowth(ss)
gamPlot(fit, form = ss$pcvrForm, df = ss$df, groupFill = TRUE)
gamPlot(fit, form = ss$pcvrForm, df = ss$df, groups = "a", timeRange = 1:10)
ss <- growthSS(
  model = "gam", form = y ~ time | group,
  df = simdf, start = NULL, type = "nls"
)
fit <- fitGrowth(ss)
gamPlot(fit, form = ss$pcvrForm, df = ss$df, groupFill = TRUE)

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 = "gam", form = y ~ time | id / group,
  df = simdf, start = NULL, type = "nls"
)
fit <- fitGrowth(ss)
lmPlot(fit, form = ss$pcvrForm, df = ss$df)