R: Plot regression lines

plotConf {lava} | R Documentation

## 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)

[Package lava version 1.5.1 Index]