How to plot correlation coefficients from multiple regressions while leaving out some of the variables from the plot? Lets say that I have "y" that I want to model with linear regression. "x" and "z" are the things I'm interrested in showing folks, but I also have things that I want to adjust for, but not really show in my plot.
Now, I would like to show my coefficients in a plot, but I would like to keep the things I adjusted for out of it. So perhaps this could be coronary artery calcification modeled as "CAC ~ SomeBloodStuff + Bloodpressure + BMI + Smoking_status". The BMI and Smoking_status would be something that I would want to take into account, but just note that I have adjusted for them.
I gather this is how to do the model:
MyModel <- lm( CAC ~ SomeBloodstuff + BP + BMI + Smoking_status, data=MyData)
summary(MyModel) 
coefficients(MyModel)
confint(MyModel, level=0.95) # Seems like a legit model.

The coefplot function in arm library gives just the sort of presentation that I want, but shows all the dimensions.
library(arm)
coefplot(MyModel)

How could I leave those few variables out of the plot while keeping them in the regression and get a plot that looks like what the coefplot produces?
 A: You can do what you're looking to do if you make fuller use of the coefplot package's optional arguments.  In documentation, three relevant arguments are "predictors," "coefficients," and "strict":


predictors -  A character vector specifying which variables to keep. Each individual variable has to be specfied [sic], so individual
    levels of factors must be specified. We are working on making this
    easier to implement, but this is the only option for now.
coefficients -  A character vector specifying which factor variables
    to keep. It will keep all levels and any interactions, even if those
    are not listed.
strict - If TRUE then predictors will only be matched to its own coefficients, not its interactions [sic]


The following examples are selected from p. 8.
model1 <- lm(price ~ carat + cut*color, data=diamonds)

model2 <- lm(price ~ carat*color, data=diamonds)

model3 <- glm(price > 10000 ~ carat*color, data=diamonds)

coefplot(model1, predictors="color")

coefplot(model1, predictors="color", strict=TRUE)

coefplot(model1, coefficients=c("(Intercept)", "color.Q"))

coefplot(model1, predictors="cut", coefficients=c("(Intercept)", "color.Q"), strict=TRUE)

