plotPredy: Plot a predicted line from a bivariate model

In rcompanion: Functions to Support Extension Education Program Evaluation

Plot a predicted line from a bivariate model

Description

Plots the best fit line for a model with one y variable and one x variable, or with one y variable and polynomial x variables.

Usage

plotPredy(
  data,
  x,
  y,
  model,
  order = 1,
  x2 = NULL,
  x3 = NULL,
  x4 = NULL,
  x5 = NULL,
  pch = 16,
  xlab = "X",
  ylab = "Y",
  length = 1000,
  lty = 1,
  lwd = 2,
  col = "blue",
  type = NULL,
  ...
)

Arguments

data	The name of the data frame.
x	The name of the x variable.
y	The name of the y variable.
model	The name of the model object.
order	If plotting a polynomial function, the order of the polynomial. Otherwise can be left as 1.
x2	If applicable, the name of the second order polynomial x variable.
x3	If applicable, the name of the third order polynomial x variable.
x4	If applicable, the name of the fourth order polynomial x variable.
x5	If applicable, the name of the fifth order polynomial x variable.
pch	The shape of the plotted data points.
xlab	The label for the x-axis.
ylab	The label for the y-axis.
length	The number of points used to draw the line.
lty	The style of the plotted line.
lwd	The width of the plotted line.
col	The col of the plotted line.
type	Passed to predict. Required for certain models.
...	Other arguments passed to plot.

Details

Any model for which predict() is defined can be used.

Value

Produces a plot. Returns nothing.

Author(s)

Salvatore Mangiafico, [e-mail]

References

https://rcompanion.org/handbook/I_10.html

Examples

### Plot of linear model fit with lm
data(BrendonSmall)
model = lm(Weight ~ Calories, data = BrendonSmall) 
plotPredy(data  = BrendonSmall,
          y     = Weight,
          x     = Calories,
          model = model,
          xlab  = "Calories per day",
          ylab  = "Weight in kilograms")
           
### Plot of polynomial model fit with lm
data(BrendonSmall)
BrendonSmall$Calories2 = BrendonSmall$Calories * BrendonSmall$Calories
model = lm(Sodium ~ Calories + Calories2, data = BrendonSmall) 
plotPredy(data  = BrendonSmall,
          y     = Sodium,
          x     = Calories,
          x2    = Calories2,
          model = model,
          order = 2,
          xlab  = "Calories per day",
          ylab  = "Sodium intake per day")

### Plot of quadratic plateau model fit with nls
data(BrendonSmall)
quadplat = function(x, a, b, clx) {
          ifelse(x  < clx, a + b * x   + (-0.5*b/clx) * x   * x,
                           a + b * clx + (-0.5*b/clx) * clx * clx)}
model = nls(Sodium ~ quadplat(Calories, a, b, clx),
            data = BrendonSmall,
            start = list(a   = 519,
                         b   = 0.359,
                         clx = 2304))
plotPredy(data  = BrendonSmall,
          y     = Sodium,
          x     = Calories,
          model = model,
          xlab  = "Calories per day",
          ylab  = "Sodium intake per day")

### Logistic regression example requires type option
data(BullyHill)
Trials = cbind(BullyHill$Pass, BullyHill$Fail)
model.log = glm(Trials ~ Grade, data = BullyHill,
                family = binomial(link="logit"))
plotPredy(data  = BullyHill,
          y     = Percent,
          x     = Grade,
          model = model.log,
          type  = "response",
          xlab  = "Grade",
          ylab  = "Proportion passing")

rcompanion documentation built on Sept. 17, 2023, 5:07 p.m.