View source: R/multicomp.plot.R
multicomp.plot | R Documentation |
Plots significant difference of simulated array.
multicomp.plot(object, alpha = 0.05, main = "Multiple Comparison Plot",
label = NULL, shortlabel = NULL, show.pvalue = FALSE,
label.as.shortlabel = FALSE, label.on.which.axis = 3,
col.low = "lightsteelblue", col.same = "white", col.high = "lightslateblue",
vertical.line = TRUE, horizontal.line = FALSE,
vertical.line.lty = 1, horizontal.line.lty = 1, mar=c(3.5,3.5,3.5,3.5))
object |
Simulated array of coefficients, columns being different variables and rows being simulated result. |
alpha |
Level of significance to compare. |
main |
Main label. |
label |
Labels for simulated parameters. |
shortlabel |
Short labels to put into the plot. |
show.pvalue |
Default is FALSE, if set to TRUE replaces short label with Bayesian p value. |
label.as.shortlabel |
Default is FALSE, if set to TRUE takes first 2 character of label and use it as short label. |
label.on.which.axis |
default is the 3rd (top) axis. |
col.low |
Color of significantly low coefficients. |
col.same |
Color of not significant difference. |
col.high |
Color of significantly high coefficients. |
vertical.line |
Default is TRUE, if set to FALSE does not draw vertical line. |
horizontal.line |
Default is FALSE, if set to TRUE draws horizontal line. |
vertical.line.lty |
Line type of vertical line. |
horizontal.line.lty |
Line type of horizontal line. |
mar |
A numerical vector of the form |
pvalue |
Array of Bayesian p value. |
significant |
Array of significance. |
Masanao Yajima yajima@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu
Andrew Gelman and Jennifer Hill. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
coefplot
old.par <- par(no.readonly = TRUE)
# example 1
simulation.array <- data.frame(coef1=rnorm(100,10,2), coef2=rnorm(100,5,2),
coef3=rnorm(100,0,1), coef4=rnorm(100,-5,3),
coef5=rnorm(100,-2,1))
short.lab <- c("c01", "c02", "c03", "c04", "c05")
multicomp.plot(simulation.array[,1:4], label.as.shortlabel=TRUE)
# wraper for multicomp.plot
mcplot(simulation.array, shortlabel = short.lab)
# example 2
data(lalonde)
M1 <- lm(re78 ~ treat + re74 + re75 + age + educ + u74 + u75, data=lalonde)
M1.sim <- sim(M1)
lm.sim <- coef(M1.sim)[,-1]
multicomp.plot(lm.sim, label.as.shortlabel=TRUE, label.on.which.axis=2)
par(old.par)
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