Select 2d spaces with variable associated measures displayed in scatterplot matrix

Measures object is a matrix or data.frame with measures (columns) for variable pairs (rows) and rownames of the two variates separated by separator

# S3 method for default
l_ng_plots(measures, data, separator = ":", ...)

Arguments

measures	matrix or data.frame with measures (columns) for variable pairs (rows) and rownames of the two variates separated by separator
data	data frame for scatterplot
separator	a string that separates the variable pair string into the individual variables
...	arguments passed on to configure the scatterplot

Value

named list with plots-, graph-, plot-, navigator-, and context handle. The list also contains the environment of the the function call in env.

Details

For more information run: l_help("learn_R_display_graph.html#l_ng_plots")

See also

l_ng_plots, l_ng_plots.measures, l_ng_plots.scagnostics, measures1d, measures2d, scagnostics2d, l_ng_ranges

Examples

if (FALSE) {
n <- 100
dat <- data.frame(
   A = rnorm(n), B = rnorm(n), C = rnorm(n),
   D = rnorm(n), E = rnorm(n)
)
m2d <- data.frame(
    cov = with(dat, c(cov(A,B), cov(A,C), cov(B,D), cov(D,E), cov(A,E))),
    measure_1 = c(1, 3, 2, 1, 4),
    row.names = c('A:B', 'A:C', 'B:D', 'D:E', 'A:E')
)

# or m2d <- as.matrix(m2d)

nav <- l_ng_plots(measures=m2d, data=dat)

# only one measure
m <- m2d[,1]
names(m) <- row.names(m2d)
nav <- l_ng_plots(measures=m, data=dat)

m2d[c(1,2),1]

# one d measures
m1d <- data.frame(
     mean = sapply(dat, mean),
     median =  sapply(dat, median),
     sd =  sapply(dat, sd),
     q1 = sapply(dat, function(x)quantile(x, probs=0.25)),
     q3 = sapply(dat, function(x)quantile(x, probs=0.75)),
     row.names = names(dat)
)

nav <- l_ng_plots(m1d, dat)

## more involved
q1 <- function(x)as.vector(quantile(x, probs=0.25))

# be carful that the vector names are correct
nav <- l_ng_plots(sapply(oliveAcids, q1), oliveAcids)
}