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.

Value

Produces a plot. Returns nothing.

Details

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

References

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

Author

Salvatore Mangiafico, mangiafico@njaes.rutgers.edu

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")