cm_distance: Distance Matrix Between Codes

In trinker/qdap2: Bridging the gap between qualitative data and quantitative analysis

Description

Generate distance measures to ascertain a mean distance measure between codes.

Usage

  cm_distance(dataframe, time.var = NULL, parallel = FALSE,
    code.var = "code", causal = FALSE, start.var = "start",
    end.var = "end", mean.digits = 2, sd.digits = 2,
    stan.digits = 2)

Arguments

dataframe	A data frame from the cm_x2long family (cm_range2long; cm_df2long; cm_time2long).
time.var	An optional variable to split the dataframe by (if you have data that is by various times this must be supplied).
parallel	logical. If TRUE runs the cm_distance on multiple cores. This is effective with larger data sets but may actually be slower with smaller data sets.
code.var	The name of the code variable column. Defaults to "codes" as out putted by x2long family.
causal	logical. If TRUE measures the distance between x and y given that x must proceed y.
start.var	The name of the start variable column. Defaults to "start" as out putted by x2long family.
end.var	The name of the end variable column. Defaults to "end" as out putted by x2long family.
mean.digits	The number of digits to be displayed in the mean matrix.
sd.digits	The number of digits to be displayed in the sd (standard deviation) matrix.
stan.digits	The number of digits to use in the standardized mean difference matrix.

Value

An object of the class cm.dist. This is a list of n lists with the following components per each list (time.var):

mean	A distance matrix of average distances between codes
sd	A matrix of standard deviations of distances between codes
n	A matrix of counts of distances between codes
combined	A matrix of combined mean, sd and n of distances between codes
standardized	A matrix of standardized values of distances between codes. The closer a value is to zero the closer two codes relate.

Examples

foo <- list(
    AA = qcv(terms="02:03, 05"),
    BB = qcv(terms="1:2, 3:10"),
    CC = qcv(terms="1:9, 100:150")
)

foo2  <- list(
    AA = qcv(terms="40"),
    BB = qcv(terms="50:90"),
    CC = qcv(terms="60:90, 100:120, 150"),
    DD = qcv(terms="")
)

(dat <- cm_range2long(foo, foo2, v.name = "time"))
(out <- cm_distance(dat, time.var = "time", causal=TRUE))
names(out)
names(out$foo2)
out$foo2
#========================================
x <- list(
    transcript_time_span = qcv(00:00 - 1:12:00),
    A = qcv(terms = "2.40:3.00, 6.32:7.00, 9.00,
        10.00:11.00, 59.56"),
    B = qcv(terms = "3.01:3.02, 5.01,  19.00, 1.12.00:1.19.01"),
    C = qcv(terms = "2.40:3.00, 5.01, 6.32:7.00, 9.00, 17.01")
)
(dat <- cm_time2long(x))
gantt_wrap(dat, "code", border.color = "black", border.size = 5,
    sig.dig.line.freq = -2)
(a <- cm_distance(dat))
names(a)
names(a$dat)
a$dat

trinker/qdap2 documentation built on May 31, 2019, 9:47 p.m.