View connectedness of two factors in a dataframe using a levelplot

If there is replication for the treatment combination cells in a two-way table, the replications are averaged together (or counted) before constructing the heatmap.

By default, rows and columns are clustered using the 'incidence' matrix of 0s and 1s.

The function checks to see if the cells in the heatmap form a connected set. If not, the data is divided into connected subsets and the subset group number is shown within each cell.

By default, missing values in the response are deleted.

Factor levels are shown along the left and bottom sides.

The number of cells in each column/row is shown along the top/right sides.

If the 2 factors are disconnected, the group membership ID is shown in each cell.

Usage

con_view(
  data,
  formula,
  fun.aggregate = mean,
  xlab = "",
  ylab = "",
  cex.num = 0.75,
  cex.x = 0.7,
  cex.y = 0.7,
  col.regions = RedGrayBlue,
  cluster = "incidence",
  dropNA = TRUE,
  ...
)

Arguments

data

A dataframe

formula

A formula with two (or more) factor names in the dataframe like yield ~ f1 *f2

fun.aggregate

The function to use for aggregating data in cells. Default is mean.

xlab

Label for x axis

ylab

Label for y axis

cex.num

Disjoint group number.

cex.x

Scale factor for x axis tick labels. Default 0.7.

cex.y

Scale factor for y axis tick labels Default 0.7.

col.regions

Function for color regions. Default RedGrayBlue.

cluster

If "incidence", cluster rows and columns by the incidence matrix. If FALSE, no clustering is performed.

dropNA

If TRUE, observed data that are NA will be dropped.

...

Other parameters passed to the levelplot() function.

Value

A lattice graphics object

Author

Kevin Wright

Examples

require(lattice)
bar = transform(lattice::barley, env=factor(paste(site,year)))
set.seed(123)
bar <- bar[sample(1:nrow(bar), 70, replace=TRUE),]
con_view(bar, yield ~ variety * env, cex.x=1, cex.y=.3, cluster=FALSE)


# Create a heatmap of cell counts
w2b = colorRampPalette(c('wheat','black'))
con_view(bar, yield ~ variety * env, fun.aggregate=length,
  cex.x=1, cex.y=.3, col.regions=w2b, cluster=FALSE)


# Example from paper by Fernando et al. (1983).
set.seed(42)
data_fernando = transform(data_fernando,
  y=stats::rnorm(9, mean=100))
con_view(data_fernando, y ~ gen*herd, cluster=FALSE,
     main = "Fernando unsorted")
#> Warning: There are 2 groups

con_view(data_fernando, y ~ gen*herd, cluster=TRUE,
     main = "Fernando unsorted")
#> Warning: There are 2 groups


# Example from Searle (1971), Linear Models, p. 325
dat2 = transform(data_searle,
  y=stats::rnorm(nrow(data_searle)) + 100)

con_view(dat2, y ~ f1*f2, cluster=FALSE, main="data_searle unsorted") 
#> Warning: There are 3 groups

con_view(dat2, y ~ f1*f2, main="data_searle clustered")
#> Warning: There are 3 groups