gpanel.fit: Panel functions for predicted values and SE bands
In gmonette/spidanew: New Functions for the spida Package
Description
Usage
Arguments
Details
Value
Functions
Author(s)
Examples
Description
Panel functions for predicted values and SE bands using 'layer' and 'glayer' in the package latticeExtra
gpanel.labels: shows all labels, for selected labels see the examples with
trellis.focus and panel.identify:
trellis.focus()
panel.identify(labels = rownames(data),rot=-15,col = col.symbol, etc.)
trellis.unfocus()
Usage
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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, ...)
fillin(data, form, n = 200, xpd = 1)
Arguments
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
group.number
alpha
col
col.symbol
is used to control color when using 'groups'
border
default = FALSE for panel.band.
font
fontface
labels
to display
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.
dots
data
data frame with values of x that need filling in
n
number of additional points over range of predictor (default 200)
form
formula idenfying variable x to fill in and grouping variables, g1, g2, etc.
using syntax: ~ x + g1 + g2 (the variable to fill in comes first)
xpd
expansion beyond range of predictor (default 1.0, i.e. no expansion)
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:
-
fillin:
Author(s)
Georges Monette <georges@yorku.ca>
Examples
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## Not run:
library(spida)
library(spidanew)
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$se,
auto.key = list(space='right', lines= T)))
p + layer( gpanel.fit(...))
# gd_(basecol = 'tomato4') # Use 'gd_' to set parameters without groups
gd_(base = 'tomato4') # Use 'gd_' to set parameters without groups
p + layer( gpanel.fit(..., col = 'grey10'))
###
### With panels and groups
###
z <- Prestige
z$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.text(...))
## End(Not run)
gmonette/spidanew documentation built on May 17, 2019, 7:27 a.m.
R Package Documentation
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Description Usage Arguments Details Value Functions Author(s) Examples
Description
Panel functions for predicted values and SE bands using 'layer' and 'glayer' in the package latticeExtra
gpanel.labels: shows all labels, for selected labels see the examples with
trellis.focus and panel.identify:
trellis.focus()
panel.identify(labels = rownames(data),rot=-15,col = col.symbol, etc.)
trellis.unfocus()
Usage
1 2 3 4 5 6 7 8 9 10 11 12 | 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, ...)
fillin(data, form, n = 200, xpd = 1)
|
Arguments
y |
|
fit |
fitted values of a model, generally passed through 'layer' from a call to 'xyplot': e.g. |
lower,upper |
|
subscripts |
|
group.number |
|
alpha |
|
col |
|
col.symbol |
is used to control color when using 'groups' |
border |
default = FALSE for panel.band. |
font |
|
fontface |
|
labels |
to display |
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. |
dots |
|
data |
data frame with values of x that need filling in |
n |
number of additional points over range of predictor (default 200) |
form |
formula idenfying variable x to fill in and grouping variables, g1, g2, etc.
using syntax: |
xpd |
expansion beyond range of predictor (default 1.0, i.e. no expansion) |
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: -
fillin:
Author(s)
Georges Monette <georges@yorku.ca>
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | ## Not run:
library(spida)
library(spidanew)
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( gpanel.fit(...))
# gd_(basecol = 'tomato4') # Use 'gd_' to set parameters without groups
gd_(base = 'tomato4') # Use 'gd_' to set parameters without groups
p + layer( gpanel.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.text(...))
## End(Not run)
|
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