plot: Plot AMCE estimates, MM descriptives, and frequency plots
In cregg: Simple Conjoint Tidying, Analysis, and Visualization

Description Usage Arguments Details Value See Also Examples

ggplot2-based plotting of conjoint AMCEs estimates and MMs, and differences

## S3 method for class 'cj_amce'
plot(
  x,
  group = attr(x, "by"),
  feature_headers = TRUE,
  header_fmt = "(%s)",
  size = 1,
  xlab = "Estimated AMCE",
  ylab = "",
  legend_title = if (is.null(group)) "Feature" else group,
  legend_pos = "bottom",
  xlim = NULL,
  vline = 0,
  vline_color = "gray",
  theme = ggplot2::theme_bw(),
  ...
)

## S3 method for class 'cj_diffs'
plot(
  x,
  group = attr(x, "by"),
  feature_headers = TRUE,
  header_fmt = "(%s)",
  size = 1,
  xlab = "Estimated Difference",
  ylab = "",
  legend_title = if (is.null(group)) "Feature" else group,
  legend_pos = "bottom",
  xlim = NULL,
  vline = 0,
  vline_color = "gray",
  theme = ggplot2::theme_bw(),
  ...
)

## S3 method for class 'cj_freqs'
plot(
  x,
  group = attr(x, "by"),
  feature_headers = TRUE,
  header_fmt = "(%s)",
  xlab = "",
  ylab = "Frequency",
  legend_title = if (is.null(group)) "Feature" else group,
  legend_pos = "bottom",
  theme = ggplot2::theme_bw(),
  ...
)

## S3 method for class 'cj_mm'
plot(
  x,
  group = attr(x, "by"),
  feature_headers = TRUE,
  header_fmt = "(%s)",
  size = 1,
  xlab = "Marginal Mean",
  ylab = "",
  legend_title = if (is.null(group)) "Feature" else group,
  legend_pos = "bottom",
  xlim = NULL,
  vline = 0,
  vline_color = "gray",
  theme = ggplot2::theme_bw(),
  ...
)

`x`	A data frame returned from `cj` or `mm`.
`group`	Optionally a character string specifying a grouping factor. This is useful when, for example, subgroup analyses or comparing AMCEs for different outcomes. An alternative is to use `facet_wrap` for faceted graphics.
`feature_headers`	A logical indicating whether to include headers for each feature to visually separate levels for each feature (beyond the color palette).
`header_fmt`	A character string specifying a `fmt` argument to `sprintf`, which will be used when generating the feature headers (if `feature_headers = TRUE`).
`size`	A numeric value specifying point size in `geom_point`.
`xlab`	A label for the x-axis
`ylab`	A label for the y-axis
`legend_title`	A character string specifying a label for the legend.
`legend_pos`	An argument forwarded to the `legend.position` argument in `theme`.
`xlim`	A two-element number vector specifying limits for the x-axis. If `NULL`, a default value is calculated from the data.
`vline`	Optionally, a numeric value specifying an x-intercept for a vertical line. This can be useful in distinguishing the midpoint of the estimates (e.g., a zero line for AMCEs).
`vline_color`	A character string specifying a color for the `vline`.
`theme`	A ggplot2 theme object
`...`	Ignored.

These are convenience functions for quickly plotting results from cregg. Because plot returns ggplot2 objects, these are easily manipulated using standard ggplot2 operations.

Note that ggplot2, by default, sorts factors (like feature names here) in what might be the opposite order of what you would expect and in the opposite order that cregg functions sort their output.

A ggplot2 object

amce, mm

require("ggplot2")
# load data
data("immigration")
immigration$contest_no <- factor(immigration$contest_no)

# calculate MMs
d1 <- mm(immigration, ChosenImmigrant ~ Gender + Education + 
         LanguageSkills + CountryOfOrigin + Job + JobExperience + 
         JobPlans + ReasonForApplication + PriorEntry, id = ~ CaseID)

# plot MMs
## simple plot
(p <- plot(d1, vline = 0.5))

## gridlines to aid interpretation
p + ggplot2::theme_grey()

## monochrome bars
p + scale_color_manual(values = rep("black", 9))

## plot with estimates shown as text labels
p + ggplot2::geom_text(
  aes(label = sprintf("%0.2f (%0.2f)", estimate, std.error)),
  colour = "black", position = position_nudge(y = .5)
)

## plot with facetting by feature
plot(d1, feature_headers = FALSE) + 
  ggplot2::facet_wrap(~feature, ncol = 1L, 
                      scales = "free_y", strip.position = "right")

# MMs split by profile number
stacked <- cj(immigration, ChosenImmigrant ~ Gender + 
              Education + LanguageSkills + CountryOfOrigin + Job + JobExperience + 
              JobPlans + ReasonForApplication + PriorEntry, id = ~ CaseID,
              estimate = "mm", by = ~ contest_no)

## plot with grouping
plot(stacked, group = "contest_no", feature_headers = FALSE)

## plot with facetting
plot(stacked) + ggplot2::facet_wrap(~contest_no, nrow = 1L)

## plot with shapes instead of colors for groups
plot(stacked, group = "contest_no", vline = 0.5) + 
 aes(shape = contest_no) + # map group to `shape` aesthetic
 scale_shape_manual(values=c(1, 2, 3, 4, 5)) +
 scale_colour_manual(values=rep("black", 5)) 

# estimate AMCEs over different subsets of data
reasons12 <- subset(
  immigration, ReasonForApplication %in% levels(ReasonForApplication)[1:2]
)
d2_1 <- cj(immigration, ChosenImmigrant ~ CountryOfOrigin, id = ~ CaseID)
d2_2 <- cj(reasons12, ChosenImmigrant ~ CountryOfOrigin, id = ~ CaseID,
           feature_labels = list(CountryOfOrigin = "Country Of Origin"))
d2_1$reasons <- "1,2,3"
d2_2$reasons <- "1,2"
plot(rbind(d2_1, d2_2), group = "reasons")