gpanel.fit: Panel functions for predicted values and SE bands

In gmonette/yscs: Collection of tools used in York University's Statistical Consulting Service

Description

Panel functions for predicted values and SE bands using 'layer' and 'glayer' in the package latticeExtra

This is an experiment in writing a function that can be called via layer or glayer without further complications e.g. xyplot(......,labels = rownames(data)) + layer(gpanel.labels(...)) or xyplot(....., labels = rownames(data), subscripts = T) + glayer(gpanel.labels(...)). For selected labels see the examples with trellis.focus and panel.identify

Usage

gpanel.fit(x, y, fit, lower, upper, subscripts, ..., type, group.number, alpha,
  col, col.line, col.symbol, border = F, font, fontface)

panel.fit(x, y, fit, lower, upper, subscripts, ..., type, group.number, alpha,
  col, col.line, col.symbol, border = F, font, fontface)

gpanel.band(x, y, fit, lower, upper, subscripts, ..., type, group.number, alpha,
  col, col.line, col.symbol, border = F, font, fontface)

gpanel.labels(x, y, labels, subscripts, ...)

Arguments

x, y	position of labels, usually supplied through panel call
fit	fitted values of a model, generally passed through 'layer' from a call to 'xyplot': e.g. xyplot( y ~ x, data, groups = g, fit = data$yhat, lower = with(data, yhat - 2*se), upper = with(data, yhat + 2*se), subscripts = T)
...	NOTE: may specify anything you don't want passed through ...
col.symbol	is used to control color when using 'groups'
border	default = FALSE for panel.band.
labels	default is rownames of data
data	data frame to be used to add additional values of numeric variable
form	formula evaluated in data. The first term defines the variable with values to be filled in and the remaining terms define the variables to be used for grouping determining the minima and maxima within which values are added.
n	the number of values to be added between the global mininum and maximum. Values falling outside conditional minima and maxima are culled. Default 200.
xpd	expansion factor to add points beyond minima and maxima. Default 1.0.
labels	to display

Details

With 'layer' and 'glayer' in 'latticeExtra', these functions can be used to easily generate fitted values and confidence or prediction bands that have a reasonable appearance whether a plot uses 'groups' or not.

Value

The 'panel.bands', 'panel.fit', and 'panel.labels' functions are invoked for their graphical effect.

Functions

• panel.fit: to be used with 'layer' – but, actually, identical to 'glayer'

• gpanel.band: identical to gpanel.fit but kept for backward compatibility

• gpanel.labels:

Author(s)

Georges Monette <[e-mail]>

Examples

## Not run: 
  library(yscs)
  library(latticeExtra)
  library(car)

  fit <- lm(prestige ~ (income + I(income^2)) * type, Prestige,
      na.action = na.exclude)
  pred <- cbind(Prestige, predict(fit, newdata = Prestige, se = TRUE))
  head(pred)
  (p <- xyplot( prestige ~ income , pred,  groups = type,
                subscripts = T,
                fit = pred$fit,
                lower = with(pred, fit - 2*se.fit),
                upper = with(pred, fit + 2*se.fit)))
  p + glayer(gpanel.fit(...))

  ###
  ### Use 'fillin' to add points in sparse regions of the predictor
  ### to produce smoother bands
  ###

  fit <- lm( income ~
        (education+I(education^2)+I(education^3) +I(education^4))* type,
        Prestige, na.action = na.exclude)    # overfitting!
  # adding extra values of predictor to get smooth line
  Prestige$occupation <- rownames(Prestige)
  z <- fillin(Prestige, ~education + type, xpd = 1.1)

  dim(z)
  dim(Prestige)
  z <- cbind(z, predict(fit, newdata = z, se = TRUE))
  head(z)
  gd(3,cex=2,lwd=2, alpha = .7)
  (p <-  xyplot( income ~ education, z, groups = type,
                 subscripts = T,
                 fit = z$fit,
                 lower = z$fit - z$se,
                 upper = z$fit + z$se,
                 auto.key = list(space='right', lines= T)))
  p + glayer( gpanel.fit(...))
  p + glayer( gpanel.fit(...,  alpha = .1))
  # Using spida:gd() to get a ggplot-like appearance
  gd(3,lty=1,lwd=2)
  p + glayer( gpanel.fit(...,alpha = .1))

  ###
  ###  Using gpanel.fit with no groups
  ###

  (p <-  xyplot( income ~ education| type, z,
                 subscripts = T,
                 fit = z$fit,
                 lower = z$fit - z$se,
                 upper = z$fit + z$se,
                 auto.key = list(space='right', lines= T)))
  p + layer( panel.fit(...))

  # gd_(basecol = 'tomato4')  # Use 'gd_' to set parameters without groups
  gd_(base = 'tomato4')  # Use 'gd_' to set parameters without groups
  p + layer( panel.fit(...))
  p + layer( panel.fit(...,  col = 'grey10'))

  ###
  ### With panels and groups
  ###

  z <- Prestige
  z$gender <- with(z, cut( women, c(-1,15,50,101),labels = c("Male","Mixed","Female")))
  tab(z, ~ gender + type)
  z <- fillin( z, ~ education + type + gender, xpd = 1.1)
  fit <- lm( income ~ (education+I(education^2)+I(education^3) )* type * gender,
             z, na.action = na.exclude)    # overfitting!
  summary(fit)
  z <- cbind( z, predict(fit, newdata = z, se = TRUE))
  head(z)
  (p <-  xyplot( income ~ education| gender, z, groups = type,
                 subscripts = T,
                 fit = z$fit,
                 lower = z$fit - z$se,
                 upper = z$fit + z$se,
                 layout = c(1,3),
                 auto.key = list(space='right', lines= T, cex = 1.5)))

  p + glayer(gpanel.fit(...))
  trellis.focus()
  panel.identify(labels= z$occupation)
  trellis.unfocus()
  z$type2 <- with( z, reorder(type,education, mean, na.rm=T))
  gd(3)
  (p <-  xyplot( income ~ education| type2, z, groups = gender,
                 subscripts = T,
                 fit = z$fit,
                 lower = z$fit - z$se,
                 upper = z$fit + z$se,
                 layout = c(1,3),
                 par.strip.text = list(cex = 2),
                 auto.key = list(space='right', lines= T, cex = 1.5)))

  p + glayer( gpanel.fit(...))
  trellis.focus()
  panel.identify(labels= z$occupation)
  trellis.unfocus()

  ###
  ### With panels^2
  ### need to remove 'col = col.line'
  ###

  z <- Prestige
  z$occ <- rownames(Prestige)
  z$gender <- with(z, cut( women, c(-1,15,50,101),labels = c("Male","Mixed","Female")))
  z$type2 <- with( z, reorder(type,education, mean, na.rm=T))
  tab(z, ~ gender + type2)
  z <- fillin( z, ~ education + type + gender, xpd = 1.1)
  fit <- lm( income ~ (education+I(education^2)+I(education^3) )* type * gender,
             z, na.action = na.exclude)    # overfitting!
  summary(fit)
  z <- cbind( z, predict(fit, newdata = z, se = TRUE))
  head(z)
  (p <-  xyplot( income ~ education| gender*type, z,
                 subscripts = T,
                 fit = z$fit,
                 labels = z$occ,
                 lower = z$fit - z$se,
                 upper = z$fit + z$se,
                 auto.key = list(space='right', lines= T, cex = 1.5)))

  p + layer( gpanel.fit(...))
  p + layer( gpanel.fit(..., col = 'black', alpha = .1)) + layer(gpanel.labels(...))

## End(Not run)
## Not run: 
trellis.focus()
panel.identify(labels = rownames(data),rot=-15,col = col.symbol, etc.)
trellis.unfocus()

## End(Not run)

gmonette/yscs documentation built on May 17, 2019, 7:28 a.m.