plotConf function

Plot regression lines

Plot regression line (with interactions) and partial residuals.

plotConf(
  model,
  var1 = NULL,
  var2 = NULL,
  data = NULL,
  ci.lty = 0,
  ci = TRUE,
  level = 0.95,
  pch = 16,
  lty = 1,
  lwd = 2,
  npoints = 100,
  xlim,
  col = NULL,
  colpt,
  alpha = 0.5,
  cex = 1,
  delta = 0.07,
  centermark = 0.03,
  jitter = 0.2,
  cidiff = FALSE,
  mean = TRUE,
  legend = ifelse(is.null(var1), FALSE, "topright"),
  trans = function(x) {
     x
 },
  partres = inherits(model, "lm"),
  partse = FALSE,
  labels,
  vcov,
  predictfun,
  plot = TRUE,
  new = TRUE,
  ...
)

Arguments

• model: Model object (e.g. lm)
• var1: predictor (Continuous or factor)
• var2: Factor that interacts with var1
• data: data.frame to use for prediction (model.frame is used as default)
• ci.lty: Line type for confidence limits
• ci: Boolean indicating wether to draw pointwise 95% confidence limits
• level: Level of confidence limits (default 95%)
• pch: Point type for partial residuals
• lty: Line type for estimated regression lines
• lwd: Line width for regression lines
• npoints: Number of points used to plot curves
• xlim: Range of x axis
• col: Color (for each level in var2)
• colpt: Color of partial residual points
• alpha: Alpha level
• cex: Point size
• delta: For categorical var1
• centermark: For categorical var1
• jitter: For categorical var1
• cidiff: For categorical var1
• mean: For categorical var1
• legend: Boolean (add legend)
• trans: Transform estimates (e.g. exponential)
• partres: Boolean indicating whether to plot partial residuals
• partse: .
• labels: Optional labels of var2
• vcov: Optional variance estimates
• predictfun: Optional predict-function used to calculate confidence limits and predictions
• plot: If FALSE return only predictions and confidence bands
• new: If FALSE add to current plot
• ...: additional arguments to lower level functions

Returns

list with following members: - x: Variable on the x-axis (var1)

• y: Variable on the y-axis (partial residuals)

• predict: Matrix with confidence limits and predicted values

Examples

n <- 100
x0 <- rnorm(n)
x1 <- seq(-3,3, length.out=n)
x2 <- factor(rep(c(1,2),each=n/2), labels=c("A","B"))
y <- 5 + 2*x0 + 0.5*x1 + -1*(x2=="B")*x1 + 0.5*(x2=="B") + rnorm(n, sd=0.25)
dd <- data.frame(y=y, x1=x1, x2=x2)
lm0 <- lm(y ~ x0 + x1*x2, dd)
plotConf(lm0, var1="x1", var2="x2")
abline(a=5,b=0.5,col="red")
abline(a=5.5,b=-0.5,col="red")
### points(5+0.5*x1 -1*(x2=="B")*x1 + 0.5*(x2=="B") ~ x1, cex=2)

data(iris)
l <- lm(Sepal.Length ~ Sepal.Width*Species,iris)
plotConf(l,var2="Species")
plotConf(l,var1="Sepal.Width",var2="Species")

## Not run:

## lme4 model
dd$Id <- rbinom(n, size = 3, prob = 0.3)
lmer0 <- lme4::lmer(y ~ x0 + x1*x2 + (1|Id), dd)
plotConf(lmer0, var1="x1", var2="x2")
## End(Not run)

See Also

termplot

Author(s)

Klaus K. Holst