dots_bars: Plot Points +/- Error Bars
In dvmisc: Faster Computation of Common Statistics and Miscellaneous Functions

Description Usage Arguments Value Examples

Creates plot showing user-specified points (e.g. means, medians, regression coefficients) along with user-specified error bars (e.g. standard deviations, min/max, 95% confidence intervals).

dots_bars(y = NULL, bars = NULL, bars.lower = y - bars, bars.upper = y +
  bars, group.labels = NULL, subgroup.labels = NULL, subgroup.pch = NULL,
  subgroup.col = NULL, points.list = NULL, arrows.list = NULL,
  axis.list = NULL, legend.list = NULL, ...)

`y`	Numeric vector of y-values for different groups, or numeric matrix where each column contains y-values for clustered subgroups within a group.
`bars`	Numeric vector or matrix (matching whichever type `y` is) specifying the length of the error bar for each group/subgroup (i.e. distance from point to one end of error bar).
`bars.lower`	Numeric vector or matrix (matching whichever type `y` is) specifying the position of the lower end of the error bar for each group/subgroup.
`bars.upper`	Numeric vector or matrix (matching whichever type `y` is) specifying the position of the upper end of the error bar for each group/subgroup.
`group.labels`	Character vector giving labels for the groups.
`subgroup.labels`	Character vector giving labels for the subgroups.
`subgroup.pch`	Plotting symbol for different subgroups within each group.
`subgroup.col`	Plotting color for different subgroups within each group.
`points.list`	Optional list of inputs to pass to `points` function.
`arrows.list`	Optional list of inputs to pass to `arrows` function.
`axis.list`	Optional list of inputs to pass to `axis` function.
`legend.list`	Optional list of inputs to pass to `legend` function.
`...`	Additional arguments to pass to `plot` function.

Plot showing points +/- error bars across groups/subgroups.

# Generate 100 values from normal distributions with different means, and 
# graph mean +/- standard deviation across groups 
dat <- cbind(rnorm(100, 2), rnorm(100, 2.5), rnorm(100, 1.75))
means <- apply(dat, 2, mean)
sds <- apply(dat, 2, sd)
fig1 <- dots_bars(y = means, bars = sds, main = "Mean +/- SD by Group",
                  ylab = "Mean +/- SD")
                  
# Simulate BMI values for males and females in 3 different age groups, and 
# graph mean +/- 95\% CI
sex <- as.factor(c(rep("Male", 300), rep("Female", 300)))
age <- as.factor(rep(c("Young", "Middle", "Old"), 2))
bmi <- c(rnorm(100, 25, 4), rnorm(100, 26, 4.25), rnorm(100, 27, 4.5),
         rnorm(100, 26.5, 4.5), rnorm(100, 27.25, 4.75), rnorm(100, 28, 5))
dat <- data.frame(sex = sex, age = age, bmi = bmi)
means <- tapply(dat$bmi, dat[, c("sex", "age")], mean)
ci.lower <- tapply(dat$bmi, dat[, c("sex", "age")],
                   function(x) t.test(x)$conf.int[1])
ci.upper <- tapply(dat$bmi, dat[, c("sex", "age")],
                   function(x) t.test(x)$conf.int[2])
fig2 <- dots_bars(y = means, bars.lower = ci.lower, bars.upper = ci.upper,
                  main = "BMI by Sex and Age",
                  ylab = "BMI (mean +/- CI)",
                  xlab = "Age group")