DAAG (version 1.22)

plotSimScat: Simulate scatterplots, from lm object with a single explanatory variable.

Description

This plots simulated y-values, or residuals from such simulations, against x-values .

Usage

plotSimScat(obj, sigma = NULL, layout = c(4, 1), type = c("p", "r"),
show = c("points", "residuals"), ...)

Arguments

obj

An lm object with a single explanatory variable.

sigma

Standard deviation, if different from that for the supplied lm object.

layout

Columns by Rows layout for plots from the simulations.

type

See type as in plot.lm.

show

Specify points or residuals.

...

Other parameters to be passed to plotting functions

Value

A lattice graphics object is returned.

See Also

plotSimDiags

Examples

# NOT RUN {
nihills.lm <- lm(timef~time, data=nihills)
plotSimDiags(nihills.lm)

## The function is currently defined as
function (obj, sigma = NULL, layout = c(4, 1), type = c("p",
    "r"), show = c("points", "residuals"))
{
    nsim <- prod(layout)
    if (is.null(sigma))
        sigma <- summary(obj)[["sigma"]]
    hat <- fitted(obj)
    xnam <- all.vars(formula(obj))[2]
    ynam <- all.vars(formula(obj))[1]
    df <- data.frame(sapply(1:nsim, function(x) rnorm(length(hat),
        sd = sigma)))
    if (show[1] == "points")
        df <- df + hat
    simnam <- names(df) <- paste("Simulation", 1:nsim, sep = "")
    df[, c(xnam, ynam)] <- model.frame(obj)[, c(xnam, ynam)]
    if (show[1] != "points") {
        df[, "Residuals"] <- df[, ynam] - hat
        ynam <- "Residuals"
        legadd <- "residuals"
    }
    else legadd <- "data"
    leg <- list(text = paste(c("Simulated", "Actual"), legadd),
        columns = 2)
    formula <- formula(paste(paste(simnam, collapse = "+"), "~",
        xnam))
    parset <- simpleTheme(pch = c(16, 16), lty = 2, col = c("black",
        "gray"))
    gph <- xyplot(formula, data = df, outer = TRUE, par.settings = parset,
        auto.key = leg, lty = 2, layout = layout, type = type)
    formxy <- formula(paste(ynam, "~", xnam))
    addgph <- xyplot(formxy, data = df, pch = 16, col = "gray")
    gph + as.layer(addgph, under = TRUE)
  }
# }