check.data {fechner} | R Documentation |
Check for Required Data Format
Description
check.data
is used to check whether the data are of required
format.
Usage
check.data(X, format = c("probability.different", "percent.same",
"general"))
Arguments
X |
a required square matrix or data frame of numeric
data. No |
format |
an optional character string giving the data format to
be checked. This must be one of |
Details
The data must be a matrix or a data frame, have the same number of
rows and columns, and be numeric consisting of real numbers. In
particular, no infinite, undefined, or missing values are allowed.
This is the general data format. The probability-different and
percent-same formats, in addition, require that the data lie in the
intervals [0, 1]
and [0, 100]
, respectively. If all of
the requirements for a data format are satisfied, the data are
returned as a matrix with rows and columns labeled; otherwise the
function produces respective messages.
The labeling is as follows.
If the data are entered without any labeling of the rows and columns: The function does the labeling automatically, as
a1, b1, \ldots, z1
,a2, b2, \ldots, z2
, ..., etc., up toa9, b9, \ldots, z9
if the data are as large as234\times 234
, or if the data are larger than234\times 234
, the labeling isv1, v2, \ldots, vN
, whereN\times N
is the dimension of the data (andN > 234
).If the data are entered with either row or column labeling: In that case, the row or column labels are assigned to the columns or rows, respectively.
If the data are entered with row and column labeling: Since the labeling of both the rows and columns is now provided by the user manually, the same labeling must be used for both. If this is the case, the labeling is adopted. Otherwise the function produces a respective message.
Value
If the data are of required format, check.data
returns a
matrix of the data with rows and columns labeled.
Author(s)
Thomas Kiefer, Ali Uenlue. Based on original MATLAB source by Ehtibar N. Dzhafarov.
References
Dzhafarov, E. N. and Colonius, H. (2006) Reconstructing distances among objects from their discriminability. Psychometrika, 71, 365–386.
Dzhafarov, E. N. and Colonius, H. (2007) Dissimilarity cumulation theory and subjective metrics. Journal of Mathematical Psychology, 51, 290–304.
Uenlue, A. and Kiefer, T. and Dzhafarov, E. N. (2009) Fechnerian scaling in R: The package fechner. Journal of Statistical Software, 31(6), 1–24. URL http://www.jstatsoft.org/v31/i06/.
See Also
check.regular
for checking regular
minimality/maximality; fechner
, the main function for
Fechnerian scaling. See also fechner-package
for
general information about this package.
Examples
## dataset \link{wish} is of probability-different format
check.data(wish)
## dataset \link{morse} is of percent-same format
check.data(morse, format = "percent.same")
## a matrix without any labeling of rows and columns, of general format
## check.data does the labeling automatically
(X <- ((-1) * matrix(1:16, nrow = 4)))
check.data(X, format = "general")
## examples of data that are not of any of the three formats
## message: data must be matrix or data frame
check.data(as.character(matrix(1:16, nrow = 4)))
## message: data must have same number of rows and columns
check.data(matrix(1:12, nrow = 4))
## message: data must be numbers
check.data(matrix(LETTERS[1:16], nrow = 4))