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,
...
)model |
Model object (e.g. |
var1 |
predictor (Continuous or factor) |
var2 |
Factor that interacts with |
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 |
colpt |
Color of partial residual points |
alpha |
Alpha level |
cex |
Point size |
delta |
For categorical |
centermark |
For categorical |
jitter |
For categorical |
cidiff |
For categorical |
mean |
For categorical |
legend |
Boolean (add legend) |
trans |
Transform estimates (e.g. exponential) |
partres |
Boolean indicating whether to plot partial residuals |
partse |
. |
labels |
Optional labels of |
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 |
list with following members:
x |
Variable on the x-axis ( |
y |
Variable on the y-axis (partial residuals) |
predict |
Matrix with confidence limits and predicted values |
Klaus K. Holst
termplot
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.