plotPredy function

Plot a predicted line from a bivariate model

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.

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.

Returns

Produces a plot. Returns nothing.

Details

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

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")

References

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

Author(s)

Salvatore Mangiafico, mangiafico@njaes.rutgers.edu