arm (version 1.0-4)

coefplot: Function for Making Coefficient Plot

Description

A function that plot the coefficients from a lm or glm fit

Usage

coefplot(fit, longnames=NULL,
        xlim=NULL, ylim=NULL, 
        xlab="", ylab="", main="", ...,   
        intercept=FALSE, varnames=TRUE, display.fit=FALSE, 
        cex.var=0.8, cex.pts=0.9)

Arguments

fit

fitted objects-lm or glm.

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'.

...

other plot options may be passed to this function, see plot.default and par.

intercept

If TRUE will plot intercept, default=FALSE.

varnames

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

display.fit

If TRUE will display fitted output, default=FALSE.

cex.var

The fontsize of the varible names, default=0.8.

cex.pts

The size of data points, default=0.9.

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 and glm 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)
 
 
 # plot 2
 par (mar=c(2,8,2,0.5))
 coefplot(fit1, longnames=longnames, display=TRUE)
 
 # plot 3
 par (mar=c(2,2,2,2))
 coefplot(fit2, longnames=longnames, varnames=FALSE)
 
 # plot 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(0,7.5), 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)