broom (version 0.4.2)

lsmeans_tidiers: Tidy least-squares means objects from the lsmeans packages

Description

Tidiers for least-squares means objects, which report the predicted means for factors or factor combinations in a linear model. This covers two classes: lsmobj and ref.grid. (The two objects have slightly different purposes within the package but have similar output).

Usage

# S3 method for lsmobj
tidy(x, conf.level = 0.95, ...)
# S3 method for ref.grid
tidy(x, ...)

Arguments

x

"lsmobj" or "ref.grid" object

conf.level

Level of confidence interval, used only for lsmobj objects

...

Extra arguments, passed on to summary.ref.grid

Value

A data frame with one observation for each estimated mean, and one column for each combination of factors, along with the following variables:

estimate

Estimated least-squares mean

std.error

Standard error of estimate

df

Degrees of freedom

conf.low

Lower bound of confidence interval

conf.high

Upper bound of confidence interval

When the input is a contrast, each row will contain one estimated contrast, along with some of the following columns:

level1

One level of the factor being contrasted

level2

Second level

contrast

In cases where the contrast is not made up of two levels, describes each

statistic

T-ratio statistic

p.value

P-value

Details

There are a large number of arguments that can be passed on to summary.ref.grid. By broom convention, we use conf.level to pass the level argument.

Examples

# NOT RUN {
if (require("lsmeans", quietly = TRUE)) {
  # linear model for sales of oranges per day
  oranges_lm1 <- lm(sales1 ~ price1 + price2 + day + store, data = oranges)
  
  # reference grid (see lsmeans vignette)
  oranges_rg1 <- ref.grid(oranges_lm1)
  td <- tidy(oranges_rg1)
  head(td)
  
  # marginal averages
  marginal <- lsmeans(oranges_rg1, "day")
  tidy(marginal)
  
  # contrasts
  tidy(contrast(marginal))
  tidy(contrast(marginal, method = "pairwise"))
  
  # plot confidence intervals
  library(ggplot2)
  ggplot(tidy(marginal), aes(day, estimate)) +
    geom_point() +
    geom_errorbar(aes(ymin = conf.low, ymax = conf.high))
  
  # by multiple prices
  by_price <- lsmeans(oranges_lm1, "day", by = "price2",
                      at = list(price1 = 50, price2 = c(40, 60, 80),
                      day = c("2", "3", "4")) )
  by_price
  tidy(by_price)
  
  ggplot(tidy(by_price), aes(price2, estimate, color = day)) +
    geom_line() +
    geom_errorbar(aes(ymin = conf.low, ymax = conf.high))
}

# }

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