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

In gmonette/spida: Collection of miscellaneous functions for mixed models etc. prepared for SPIDA 2009+ (development version)

Description

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

Usage

panel.fit(x, y, fit,
           subscripts, ..., type = 'l', font, fontface)

panel.fit(...) # when called within 'layer' or 'glayer' in 'latticeExtra'

panel.band(x, y, lower, upper, subscripts, ..., 
           col, group.number, alpha, 
           col.symbol, border = F, font, fontface) # see examples belor

panel.band(...) # when called within 'layer' or 'glayer' in 'latticeExtra'

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

panel.labels(...) # when called within 'layer' or 'glayer' in 'latticeExtra'

fillin(data, form, n = 200, xpd = 1.0)

Arguments

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.
y
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)
lower
upper
subscripts
...
col
group.number
alpha	to deal with the problem that 'trellis.par.get' will pass a value of alpha = 1, 'panel.band' will set alpha = .3 if alpha = 1. To get a nearly opaque band, use alpha = .99.
col.symbol	is used to control color when using 'groups'
border	default = FALSE for panel.band.
font
fontface

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.

Author(s)

Georges Monette <[e-mail]>

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

  library(spida)
  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.band(...))
  p + glayer(gpanel.band(...)) + glayer(gpanel.fit(...))
  
  # Another example 
  # to illustrate 'fillin'
  
  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(...)) + glayer(gpanel.band(...))  
  p + glayer( gpanel.band(...,  alpha = .1))
  gd(3,lty=1,lwd=2)
  p + glayer( gpanel.band(...,alpha = .1)) + glayer(gpanel.fit(...))
  
  # with panels and 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( gpanel.fit(...)) + layer( gpanel.band(...))
  gd(basecol = 'tomato4')
  p + layer( gpanel.band(...,  col = 'grey10')) + layer(gpanel.fit(...))
  p + layer( gpanel.band(...)) + layer(gpanel.fit(...))
  
  # 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.band(...)) + 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(...))
  p + glayer( gpanel.band(...))
  p + glayer( gpanel.band(...)) + 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(...)) + layer( gpanel.band(...))
  p + layer( gpanel.band(..., col = 'black', alpha = .1)) + layer(gpanel.fit(...))  +
    layer(gpanel.text(...))
  

gmonette/spida documentation built on May 17, 2019, 7:25 a.m.