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.
data(diamonds)
head(diamonds)
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)