coefplot: Generic Function for Making Coefficient Plot

In arm: Data Analysis Using Regression and Multilevel/Hierarchical Models

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 las in par.
main	The main title (on top) using font and size (character expansion) par("font.main") and color par("col.main").
xlab	X axis label using font and character expansion par("font.lab") and color par("col.lab").
ylab	Y axis label, same font attributes as xlab.
mar	A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. The default is c(1,3,5.1,2).
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 [e-mail]

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)

arm documentation built on June 25, 2024, 1:14 a.m.