arm (version 1.0-9)

coefplot: Generic Function for Making Coefficient Plot

Description

Functions that plot the coefficients from a lm, glm, bugs, and polr fits.

Usage

coefplot(object, ...)

# methods for coefplot()
coefplot.default (object, longnames=NULL,
            xlim=NULL, ylim=NULL, 
            xlab="", ylab="", main="", 
            intercept=FALSE, varnames=TRUE, 
            cex.var=0.8, cex.pts=0.9, col.pts=1)

coefplot.polr (object, longnames=NULL, 
               xlim=NULL, ylim=NULL, 
               xlab="", ylab="", main="",   
               varnames=TRUE, cex.var=0.8, cex.pts=0.9, col.pts=1)

coefplot.bugs (object, longnames=NULL,
            xlim=NULL, ylim=NULL, 
            xlab="", ylab="", main="", 
            varnames=TRUE, cex.var=0.8, cex.pts=0.9, col.pts=1)

Arguments

object

fitted objects-lm, glm, bugs and polr.

...

further arguments passed to or from other methods.

longnames

long variable names (vector). If not provided, use default variable names.

xlim

the x limits (x1, x2) of the plot. Note that 'x1 > x2' is allowed and leads to a "reversed axis".

ylim

the y limits of the plot

xlab

a label for the x axis, default is "".

ylab

a label for the y axis, default is "".

main

a main title for the plot, default is "". See also title.

intercept

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

varnames

If FALSE will not plot variable names, default=TRUE.

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

Value

• Plot of the coefficients from a lm or glm fit. You can add the intercept, the variable names and the display the result of the fitted model.

Details

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

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

Examples

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"))
 
 # plot 1
 par (mfrow=c(2,2))
 coefplot(fit1)
 coefplot(fit2, col.pts="blue")
 
 
 # plot 2
 par (mar=c(2,8,2,0.5))
 coefplot(fit1, longnames=longnames, intercept=TRUE)
 
 # plot 3
 par (mar=c(2,2,2,2))
 coefplot(fit2, longnames=longnames, varnames=FALSE)
 
 # 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
    par(mar=c(2,5,3,1), mgp=c(2,0.25,0), oma=c(0,0,2,0), tcl=-0.2)
 
    coefplot(M2, xlim=c(-1,5), intercept=TRUE)
    points(coef(M1), c(3:1)-0.1, col="red", pch=19)
    segments(coef(M1) + se.coef(M1), c(3:1)-0.1,
        coef(M1) - se.coef(M1), c(3:1)-0.1, lwd=2, col="red")
    segments(coef(M1) + 2*se.coef(M1), c(3:1)-0.1,
        coef(M1) - 2*se.coef(M1), c(3:1)-0.1, col="red")    
    mtext("Coefficients", side=3, at=0.1, outer=TRUE)
    mtext("Estimate", side=3, at=0.6, outer=TRUE)
 
    ## arrayed plot
    par(mfrow=c(1,2), mar=c(2,5,5,1), mgp=c(2,0.25,0), tcl=-0.2)
    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)
 par (mar=c(2,8,2,0.5))
 coefplot(M3)
   
 M4 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
 par (mar=c(2,8,2,0.5))
 coefplot(M4)

# plot 6: plot bugs 
 M5 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy)
 M5.sim <- mcsamp(M5)
 coefplot(M5.sim)