plot.gcFitLinear {QurvE} | R Documentation |
Generic plot function for gcFittedLinear
objects. Plot the results of a linear regression on ln-transformed data
Description
plot.gcFitLinear
shows the results of a linear regression on log-transformed data and visualizes raw data, data points included in the fit, the tangent obtained by linear regression, and the lag time.
Usage
## S3 method for class 'gcFitLinear'
plot(
x,
log = "y",
which = c("fit", "diagnostics", "fit_diagnostics"),
pch = 21,
cex.point = 1,
cex.lab = 1.5,
cex.axis = 1.3,
lwd = 2,
color = "firebrick3",
y.lim = NULL,
x.lim = NULL,
plot = TRUE,
export = FALSE,
height = ifelse(which == "fit", 7, 5),
width = ifelse(which == "fit", 9, 9),
out.dir = NULL,
...
)
Arguments
x |
A |
log |
("x" or "y") Display the x- or y-axis on a logarithmic scale. |
which |
("fit" or "diagnostics") Display either the results of the linear fit on the raw data or statistical evaluation of the linear regression. |
pch |
(Numeric) Shape of the raw data symbols. |
cex.point |
(Numeric) Size of the raw data points. |
cex.lab |
(Numeric) Font size of axis titles. |
cex.axis |
(Numeric) Font size of axis annotations. |
lwd |
(Numeric) Line width. |
color |
(Character string) Enter color either by name (e.g., red, blue, coral3) or via their hexadecimal code (e.g., #AE4371, #CCFF00FF, #0066FFFF). A full list of colors available by name can be found at http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf |
y.lim |
(Numeric vector with two elements) Optional: Provide the lower ( |
x.lim |
(Numeric vector with two elements) Optional: Provide the lower ( |
plot |
(Logical) Show the generated plot in the |
export |
(Logical) Export the generated plot as PDF and PNG files ( |
height |
(Numeric) Height of the exported image in inches. |
width |
(Numeric) Width of the exported image in inches. |
out.dir |
(Character) Name or path to a folder in which the exported files are stored. If |
... |
Further arguments to refine the generated base R plot. |
Value
A plot with the linear fit.
Examples
# Create random growth dataset
rnd.dataset <- rdm.data(d = 35, mu = 0.8, A = 5, label = "Test1")
# Extract time and growth data for single sample
time <- rnd.dataset$time[1,]
data <- rnd.dataset$data[1,-(1:3)] # Remove identifier columns
# Perform linear fit
TestFit <- growth.gcFitLinear(time, data, gcID = "TestFit",
control = growth.control(fit.opt = "l"))
plot(TestFit)