coefplot {arm}R Documentation

Generic Function for Making Coefficient Plot


Functions that plot the coefficients plus and minus 1 and 2 sd from a lm, glm, bugs, and polr fits.



## 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, ...)



fitted objects-lm, glm, bugs and polr, or a vector of coefficients.


further arguments passed to or from other methods.


a vector of coefficients.


a vector of sds of coefficients.


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 bounds of confidence intervals.


upper bounds of confidence intervals.


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.


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.


default is TRUE, which shows the bottom axis–axis(1).


default is TRUE, which shows the left axis–axis(2).


The fontsize of the varible names, default=0.8.


The size of data points, default=0.9.


color of points and segments, default is black.


symbol of points, default is solid dot.


the orientation of variable names against the axis, default is 2. see the usage of las in par.


The main title (on top) using font and size (character expansion) par("font.main") and color par("col.main").


X axis label using font and character expansion par("font.lab") and color par("col.lab").


Y axis label, same font attributes as xlab.


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).


default is TRUE, plot the estimates.


if add=TRUE, plot over the existing plot. default is FALSE.


add extra spaces to separate from the existing dots. default is 0.1.


the index of the variables of a bugs object, default is NULL which will plot all the variables.


If TRUE will plot intercept, default=FALSE to get better presentation.


This function plots coefficients from bugs, lm, glm and polr with 1 sd and 2 sd interval bars.


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.


Yu-Sung Su


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


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, 
 op <- par()
 # plot 1
 par (mfrow=c(2,2))
 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       
  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")

[Package arm version 1.11-2 Index]