coefplot {arm} | R Documentation |
Functions that plot the coefficients $pm$ 1 and 2 sd from a lm, glm, bugs, and polr fits.
coefplot(object,...) ## Default S3 method: coefplot(coefs, sds, varnames=NULL, CI=2, vertical=TRUE, v.axis=TRUE, h.axis=TRUE, cex.var=0.8, cex.pts=0.9, col.pts=1, var.las=2,...) ## 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, var.las=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, ...)
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. |
varnames |
a vector of variable names, default is NULL, which will use the names of variables. |
CI |
confidence interval, default is 2, which will plot $pm 2$ sds or 95% CI. If CI=1, plot $pm 1$ sds or 50% CI instead. |
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) and the top axis–axis(3). |
h.axis |
default is TRUE, which shows the left axis–axis(2) and the right axis–axis(4). |
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. |
var.las |
the orientation of variable names against the axis, default is 2.
see the usage of las in par . |
var.idx |
the index of the variables of a bugs object, default is TRUE which will plot all the variables. |
intercept |
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 ys463@columbia.edu
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006.
display
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par
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lm
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glm
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bayesglm
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plot
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), mar=c(3,3,5,1), mgp=c(2,0.25,0), tcl=-0.2) coefplot(fit1, xlab="", ylab="", main="Regression Estimates") coefplot(fit2, col.pts="blue", xlab="", ylab="", main="Regression Estimates") # plot 2 par (mar=c(2,8,5,0.5)) longnames <- c("(Intercept)", longnames) coefplot(fit1, longnames, intercept=TRUE, CI=1, xlab="", ylab="", main="Regression Estimates") # plot 3 par (mar=c(2,2,5,2)) coefplot(fit2, vertical=FALSE, var.las=1, xlab="", ylab="", main="Regression Estimates") # 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, xlab="", ylab="") 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, xlab="", ylab="") coefplot(M2, xlim=x.scale, main="bayesglm", intercept=TRUE, xlab="", ylab="") # plot 5: the ordered logit model from polr par (mar=c(3,8,4,1), mgp=c(2,0.25,0), tcl=-0.2) M3 <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) coefplot(M3, xlab="", ylab="", main="polr") M4 <- bayespolr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) coefplot(M4, xlab="", ylab="", main="bayespolr") # plot 6: plot bugs & lmer par (mar=c(3,8,4,1), mgp=c(2,0.25,0), tcl=-0.2) M5 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy) M5.sim <- mcsamp(M5) coefplot(M5.sim, var.idx=5:22, CI=1, xlab="", ylab="", ylim=c(18,1), main="lmer model") # plot 7: plot coefficients & sds vectors par (mar=c(3,4,4,4), mgp=c(2,0.25,0), tcl=-0.2) 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, longnames, xlab="", ylab="", main="Regression Estimates") coefplot (coef.vect, sd.vect, longnames, vertical=FALSE, var.las=1, las=2, xlab="", ylab="", main="Regression Estimates")