| coefplot {arm} | R Documentation |
Functions that plot the coefficients $pm$ 1 and 2 sd from a lm, glm, bugs, and polr fits.
coefplot(object, ...)
coefplot.default(coefs, sds,
varnames=NULL, CI=2, vertical=TRUE,
xlim=NULL, ylim=NULL,
xlab="", ylab="", main="",
cex.var=0.8, cex.pts=0.9, col.pts=1,
var.las=2)
# methods for coefplot()
coefplot.lm (object, varnames=NULL, intercept=FALSE, ...)
coefplot.glm (object, varnames=NULL, intercept=FALSE, ...)
coefplot.polr (object, varnames=NULL, ...)
coefplot.bugs (object, varnames=NULL, CI=2,
xlim=NULL, ylim=NULL,
xlab="", ylab="", main="",
cex.var=0.8, cex.pts=0.9, col.pts=1)
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. |
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. |
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. |
intercept |
If TRUE will plot intercept, default=FALSE to get better presentation. |
This function plots coefficients from lm, glm and polr with 1 sd and 2 sd interval bars.
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.
Yu-Sung Su ys463@columbia.edu
Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006.
display,
par,
lm,
glm,
bayesglm
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, intercept=TRUE, CI=1)
# plot 3
par (mar=c(2,2,2,2))
coefplot(fit2, vertical=FALSE, var.las=1)
# 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)
# 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, longnames)
coefplot (coef.vect, sd.vect, longnames, vertical=FALSE, var.las=1)