plotConf: Plot regression lines

In kkholst/lava: Latent Variable Models

Plot regression lines

Description

Plot regression line (with interactions) and partial residuals.

Usage

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

Value

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

Author(s)

Klaus K. Holst

See Also

termplot

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)

kkholst/lava documentation built on Feb. 22, 2024, 4:07 p.m.