| coefplot {arm} | R Documentation |
Generic Function for Making Coefficient Plot
Description
Functions that plot the coefficients plus and minus 1 and 2 sd from a lm, glm, bugs, and polr fits.
Usage
coefplot(object,...)
## Default S3 method:
coefplot(coefs, sds, CI=2,
lower.conf.bounds, upper.conf.bounds,
varnames=NULL, vertical=TRUE,
v.axis=TRUE, h.axis=TRUE,
cex.var=0.8, cex.pts=0.9,
col.pts=1, pch.pts=20, var.las=2,
main=NULL, xlab=NULL, ylab=NULL, mar=c(1,3,5.1,2),
plot=TRUE, add=FALSE, offset=.1, ...)
## S4 method for signature 'bugs'
coefplot(object, var.idx=NULL, varnames=NULL,
CI=1, vertical=TRUE,
v.axis=TRUE, h.axis=TRUE,
cex.var=0.8, cex.pts=0.9,
col.pts=1, pch.pts=20, var.las=2,
main=NULL, xlab=NULL, ylab=NULL,
plot=TRUE, add=FALSE, offset=.1,
mar=c(1,3,5.1,2), ...)
## S4 method for signature 'numeric'
coefplot(object, ...)
## S4 method for signature 'lm'
coefplot(object, varnames=NULL, intercept=FALSE, ...)
## S4 method for signature 'glm'
coefplot(object, varnames=NULL, intercept=FALSE, ...)
## S4 method for signature 'polr'
coefplot(object, varnames=NULL, ...)
Arguments
object |
fitted objects-lm, glm, bugs and polr, or a vector of coefficients. |
... |
further arguments passed to or from other methods. |
coefs |
a vector of coefficients. |
sds |
a vector of sds of coefficients. |
CI |
confidence interval, default is 2, which will plot plus and minus 2 sds or 95% CI. If CI=1, plot plus and minus 1 sds or 50% CI instead. |
lower.conf.bounds |
lower bounds of confidence intervals. |
upper.conf.bounds |
upper bounds of confidence intervals. |
varnames |
a vector of variable names, default is NULL, which will use the names of variables; if specified, the length of varnames must be equal to the length of predictors, including the intercept. |
vertical |
orientation of the plot, default is TRUE which will plot variable names in the 2nd axis. If FALSE, plot variable names in the first axis instead. |
v.axis |
default is TRUE, which shows the bottom axis–axis(1). |
h.axis |
default is TRUE, which shows the left axis–axis(2). |
cex.var |
The fontsize of the varible names, default=0.8. |
cex.pts |
The size of data points, default=0.9. |
col.pts |
color of points and segments, default is black. |
pch.pts |
symbol of points, default is solid dot. |
var.las |
the orientation of variable names against the axis, default is 2.
see the usage of |
main |
The main title (on top) using font and size (character
expansion) |
xlab |
X axis label using font and character expansion
|
ylab |
Y axis label, same font attributes as |
mar |
A numerical vector of the form |
plot |
default is TRUE, plot the estimates. |
add |
if add=TRUE, plot over the existing plot. default is FALSE. |
offset |
add extra spaces to separate from the existing dots. default is 0.1. |
var.idx |
the index of the variables of a bugs object, default is NULL which will plot all the variables. |
intercept |
If TRUE will plot intercept, default=FALSE to get better presentation. |
Details
This function plots coefficients from bugs, lm, glm and polr with 1 sd and 2 sd interval bars.
Value
Plot of the coefficients from a bugs, lm or glm fit. You can add the intercept, the variable names and the display the result of the fitted model.
Author(s)
Yu-Sung Su suyusung@tsinghua.edu.cn
References
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006.
See Also
display,
par,
lm,
glm,
bayesglm,
plot
Examples
old.par <- par(no.readonly = TRUE)
y1 <- rnorm(1000,50,23)
y2 <- rbinom(1000,1,prob=0.72)
x1 <- rnorm(1000,50,2)
x2 <- rbinom(1000,1,prob=0.63)
x3 <- rpois(1000, 2)
x4 <- runif(1000,40,100)
x5 <- rbeta(1000,2,2)
longnames <- c("a long name01","a long name02","a long name03",
"a long name04","a long name05")
fit1 <- lm(y1 ~ x1 + x2 + x3 + x4 + x5)
fit2 <- glm(y2 ~ x1 + x2 + x3 + x4 + x5,
family=binomial(link="logit"))
op <- par()
# plot 1
par (mfrow=c(2,2))
coefplot(fit1)
coefplot(fit2, col.pts="blue")
# plot 2
longnames <- c("(Intercept)", longnames)
coefplot(fit1, longnames, intercept=TRUE, CI=1)
# plot 3
coefplot(fit2, vertical=FALSE, var.las=1, frame.plot=TRUE)
# plot 4: comparison to show bayesglm works better than glm
n <- 100
x1 <- rnorm (n)
x2 <- rbinom (n, 1, .5)
b0 <- 1
b1 <- 1.5
b2 <- 2
y <- rbinom (n, 1, invlogit(b0+b1*x1+b2*x2))
y <- ifelse (x2==1, 1, y)
x1 <- rescale(x1)
x2 <- rescale(x2, "center")
M1 <- glm (y ~ x1 + x2, family=binomial(link="logit"))
display (M1)
M2 <- bayesglm (y ~ x1 + x2, family=binomial(link="logit"))
display (M2)
#===================
# stacked plot
#===================
coefplot(M2, xlim=c(-1,5), intercept=TRUE)
coefplot(M1, add=TRUE, col.pts="red")
#====================
# arrayed plot
#====================
par(mfrow=c(1,2))
x.scale <- c(0, 7.5) # fix x.scale for comparison
coefplot(M1, xlim=x.scale, main="glm", intercept=TRUE)
coefplot(M2, xlim=x.scale, main="bayesglm", intercept=TRUE)
# plot 5: the ordered logit model from polr
M3 <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
coefplot(M3, main="polr")
M4 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
coefplot(M4, main="bayespolr", add=TRUE, col.pts="red")
## plot 6: plot bugs & lmer
# par <- op
# M5 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy)
# M5.sim <- mcsamp(M5)
# coefplot(M5.sim, var.idx=5:22, CI=1, ylim=c(18,1), main="lmer model")
# plot 7: plot coefficients & sds vectors
coef.vect <- c(0.2, 1.4, 2.3, 0.5)
sd.vect <- c(0.12, 0.24, 0.23, 0.15)
longnames <- c("var1", "var2", "var3", "var4")
coefplot (coef.vect, sd.vect, varnames=longnames, main="Regression Estimates")
coefplot (coef.vect, sd.vect, varnames=longnames, vertical=FALSE,
var.las=1, main="Regression Estimates")
par(old.par)