Description Usage Arguments Details Value Warning References See Also Examples
Generate distance measures to ascertain a mean distance measure between codes.
1 2 3 4 5 6 7 8 9 10 11 12 13  cm_distance(
dataframe,
pvals = c(TRUE, FALSE),
replications = 1000,
parallel = TRUE,
extended.output = TRUE,
time.var = TRUE,
code.var = "code",
causal = FALSE,
start.var = "start",
end.var = "end",
cores = detectCores()/2
)

dataframe 
A data frame from the cm_x2long family
( 
pvals 
A logical vector of length 1 or 2. If element 2 is blank
element 1 will be recycled. If the first element is 
replications 
An integer value for the number of replications used in
resampling the data if any 
parallel 
logical. If 
extended.output 
logical. If 
time.var 
An optional variable to split the dataframe by (if you have data that is by various times this must be supplied). 
code.var 
The name of the code variable column. Defaults to "codes" as out putted by x2long family. 
causal 
logical. If 
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. 
cores 
An integer value describing the number of cores to use if

Note that row names are the first code and column names are the
second comparison code. The values for Code A compared to Code B will not be
the same as Code B compared to Code A. This is because, unlike a true
distance measure, cm_distance's matrix is asymmetrical. cm_distance
computes the distance by taking each span (start and end) for Code A and
comparing it to the nearest start or end for Code B.
An object of the class "cm_distance"
. This is a list with the
following components:
pvals 
A logical indication of whether pvalues were calculated 
replications 
Integer value of number of replications used 
extended.output 
An optional list of individual repeated measures information 
main.output 
A list of aggregated repeated measures information 
adj.alpha 
An adjusted alpha level (based on α = .05) for the estimated pvalues using the upper end of the confidence interval around the pvalues 
Within the lists of extended.output and list of the main.output are the following items:
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 
stan.mean 
A matrix of standardized values of distances between codes. The closer a value is to zero the closer two codes relate. 
pvalue 
A n optional matrix of simulated pvalues associated with the mean distances 
pvalues are estimated and thus subject to error. More replications decreases the error. Use:
p +/ (1.96 * √[α * (1α)/n])
to adjust the confidence in the estimated pvalues based on the number of replications.
https://stats.stackexchange.com/a/22333/7482
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67  ## Not run:
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_2long(foo, foo2, v.name = "time"))
plot(dat)
(out < cm_distance(dat, replications=100))
names(out)
names(out$main.output)
out$main.output
out$extended.output
print(out, new.order = c(3, 2, 1))
print(out, new.order = 3:2)
#========================================
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_2long(x))
plot(dat)
(a < cm_distance(dat, causal=TRUE, replications=100))
## Plotting as a network graph
datA < list(
A = qcv(terms="02:03, 05"),
B = qcv(terms="1:2, 3:10, 45, 60, 200:206, 250, 289:299, 330"),
C = qcv(terms="1:9, 47, 62, 100:150, 202, 260, 292:299, 332"),
D = qcv(terms="10:20, 30, 38:44, 138:145"),
E = qcv(terms="10:15, 32, 36:43, 132:140"),
F = qcv(terms="1:2, 3:9, 10:15, 32, 36:43, 45, 60, 132:140, 250, 289:299"),
G = qcv(terms="1:2, 3:9, 10:15, 32, 36:43, 45, 60, 132:140, 250, 289:299"),
H = qcv(terms="20, 40, 60, 150, 190, 222, 255, 277"),
I = qcv(terms="20, 40, 60, 150, 190, 222, 255, 277")
)
datB < list(
A = qcv(terms="40"),
B = qcv(terms="50:90, 110, 148, 177, 200:206, 250, 289:299"),
C = qcv(terms="60:90, 100:120, 150, 201, 244, 292"),
D = qcv(terms="10:20, 30, 38:44, 138:145"),
E = qcv(terms="10:15, 32, 36:43, 132:140"),
F = qcv(terms="10:15, 32, 36:43, 132:140, 148, 177, 200:206, 250, 289:299"),
G = qcv(terms="10:15, 32, 36:43, 132:140, 148, 177, 200:206, 250, 289:299"),
I = qcv(terms="20, 40, 60, 150, 190, 222, 255, 277")
)
(datC < cm_2long(datA, datB, v.name = "time"))
plot(datC)
(out2 < cm_distance(datC, replications=1250))
plot(out2)
plot(out2, label.cex=2, label.dist=TRUE, digits=5)
## End(Not run)

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