gridplot: Plot Multidimensional Sequence Plots in a Grid

In seqHMM: Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

Plot Multidimensional Sequence Plots in a Grid

Description

Function gridplot plots multiple ssp objects to a grid.

Usage

gridplot(
  x,
  nrow = NA,
  ncol = NA,
  byrow = FALSE,
  with.legend = "auto",
  legend.pos = "auto",
  legend.pos2 = "center",
  title.legend = "auto",
  ncol.legend = "auto",
  with.missing.legend = "auto",
  row.prop = "auto",
  col.prop = "auto",
  cex.legend = 1
)

Arguments

x	A list of ssp objects.
nrow, ncol	Optional arguments to arrange plots.
byrow	Controls the order of plotting. Defaults to FALSE, i.e. plots are arranged column-wise.
with.legend	Defines if and how the legends for the states are plotted. The default value "auto" (equivalent to TRUE and "many") creates separate legends for each requested plot. Other possibilities are "combined" (all legends combined) and FALSE (no legend).
legend.pos	Defines the positions of the legend boxes relative to the whole plot. Either one of "bottom" (equivalent to "auto") or "right", or a numerical vector of grid cells (by order) to print the legends to (the cells must be in one row/column).
legend.pos2	Defines the positions of the legend boxes relative to the cell(s). One of "bottomright", "bottom", "bottomleft", "left", "topleft", "top" (the default), "topright", "right" and "center".
title.legend	The titles for the legend boxes. The default "auto" takes the titles from the channel labels provided by the first object in x. NA prints no title.
ncol.legend	(A vector of) the number of columns for the legend(s). The default "auto" creates one column for each legend.
with.missing.legend	If set to "auto" (the default), a legend for the missing state is added automatically if one or more of the sequences in data contain missing states. With the value TRUE a legend for the missing state is added in any case; equivalently FALSE omits the legend for the missing state.
row.prop	Sets the proportions of the row heights of the grid. The default value is "auto" for even row heights. Takes a vector of values from 0 to 1, with values summing to 1.
col.prop	Sets the proportion of the column heights of the grid. The default value is "auto" for even column widths. Takes a vector of values from 0 to 1, with values summing to 1.
cex.legend	Expansion factor for setting the size of the font for the labels in the legend. The default value is 1. Values lesser than 1 will reduce the size of the font, values greater than 1 will increase the size.

See Also

ssp for defining the plot before using gridplot, and plot.ssp for plotting only one ssp object.

Examples

## Not run: 
data("biofam3c")

# Creating sequence objects
child_seq <- seqdef(biofam3c$children, start = 15)
marr_seq <- seqdef(biofam3c$married, start = 15)
left_seq <- seqdef(biofam3c$left, start = 15)

## Choosing colors
attr(child_seq, "cpal") <- c("#66C2A5", "#FC8D62")
attr(marr_seq, "cpal") <- c("#AB82FF", "#E6AB02", "#E7298A")
attr(left_seq, "cpal") <- c("#A6CEE3", "#E31A1C")


# Preparing plot for state distribution plots of observations for women
ssp_f <- ssp(
  list(
    child_seq[biofam3c$covariates$sex == "woman", ],
    marr_seq[biofam3c$covariates$sex == "woman", ],
    left_seq[biofam3c$covariates$sex == "woman", ]
  ),
  type = "d", plots = "obs", title = "Women",
  ylab = c("Children", "Married", "Left home")
)

# Preparing plot for state distribution plots of observations for men
# (Updating the previous plot, only arguments that change values)
ssp_m <- update(ssp_f,
  title = "Men",
  x = list(
    child_seq[biofam3c$covariates$sex == "man", ],
    marr_seq[biofam3c$covariates$sex == "man", ],
    left_seq[biofam3c$covariates$sex == "man", ]
  )
)

# Plotting state distribution plots of observations for women and men in two columns
gridplot(list(ssp_f, ssp_m), ncol = 2, with.legend = FALSE)

# Preparing plots for women's state distributions
ssp_f2 <- ssp(
  list(
    marr_seq[biofam3c$covariates$sex == "woman", ],
    child_seq[biofam3c$covariates$sex == "woman", ],
    left_seq[biofam3c$covariates$sex == "woman", ]
  ),
  type = "d", border = NA, with.legend = FALSE,
  title = "State distributions for women", title.n = FALSE, xtlab = 15:30,
  ylab.pos = c(1, 2, 1), ylab = c("Married", "Children", "Left home")
)

# The same plot with sequences instead of state distributions
ssp_f3 <- update(
  ssp_f2,
  type = "I", sortv = "mds.obs", title = "Sequences for women"
)

# State distributions with men's data
ssp_m2 <- update(
  ssp_f2,
  title = "State distributions for men",
  x = list(
    marr_seq[biofam3c$covariates$sex == "man", ],
    child_seq[biofam3c$covariates$sex == "man", ],
    left_seq[biofam3c$covariates$sex == "man", ]
  )
)

# Men's sequences
ssp_m3 <- update(
  ssp_m2,
  type = "I", sortv = "mds.obs", title = "Sequences for men"
)

# Plotting state distributions and index plots of observations
# for women and men in two columns (+ one column for legends)
gridplot(
  list(ssp_f2, ssp_f3, ssp_m2, ssp_m3),
  ncol = 3, byrow = TRUE,
  with.legend = "combined", legend.pos = "right", col.prop = c(0.35, 0.35, 0.3)
)

# The same with different positioning and fixed cells for legends
gridplot(
  list(ssp_f2, ssp_f3, ssp_m2, ssp_m3),
  ncol = 2, nrow = 3, byrow = TRUE,
  # defining the legend positions by the cell numbers
  legend.pos = 3:4
)

## End(Not run)

seqHMM documentation built on July 9, 2023, 6:35 p.m.