qdap (version 2.2.1)

cm_distance: Distance Matrix Between Codes

Description

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

Usage

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)

Arguments

dataframe

A data frame from the cm_x2long family (cm_range2long; cm_df2long; cm_time2long).

pvals

A logical vector of length 1 or 2. If element 2 is blank element 1 will be recycled. If the first element is TRUE pvalues will be calculated for the combined (main) output for all repeated measures from simulated resampling of the data.

replications

An integer value for the number of replications used in resampling the data if any pvals is TRUE. It is recommended that this value be no lower than 1000. Failure to use enough replications may result in unreliable pvalues.

parallel

logical. If TRUE runs the cm_distance on multiple cores (if available). This will generally be effective with most data sets, given there are repeated measures, because of the large number of simulations. Default uses 1/2 o

extended.output

logical. If TRUE the information on individual repeated measures is calculated in addition to the aggregated repeated measures results for the main output.

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 TRUE measures the distance between x and y given that x must precede y. That is, only those $y_i$ that begin after the $x_i$ has begun will be considered, as it is assumed that x precedes y. If FALSE x is not as

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 parallel = TRUE. Default is to use half of the available cores.

Value

• 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 $\alpha = .05$) for the estimated p-values using the upper end of the confidence interval around the p-values
• 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

Warning

p-values are estimated and thus subject to error. More replications decreases the error. Use: $$p \pm \left ( 1.96 \cdot \sqrt{\frac{\alpha(1-\alpha)}{n}}\right )$$ to adjust the confidence in the estimated p-values based on the number of replications.

Details

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.

References

http://stats.stackexchange.com/a/22333/7482

See Also

print.cm_distance

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_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)