Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/p00ReadInOneFactorData.R
The function prepareCGOneFactorData
reads in a data frame and
settings
in order to create a
cgOneFactorData
object. The created object is designed to have exploratory and
fit methods applied to it.
1 2 3 4 
dfr 
A valid data frame, see the 
format 
Default value of

analysisname 
Optional, a character text or
mathvalid expression that will be set for
default use in graph title and table methods. The default
value is the empty 
endptname 
Optional, a character text or mathvalid expression
that will be set for default use as the yaxis label of graph
methods, and also used for table methods. The default
value is the empty 
endptunits 
Optional, a character text or mathvalid
expression that can be used in combination with the endptname
argument.
Parentheses are
automatically added to this input, which will be added to the end
of the endptname character value or expression. The default
value is the empty 
logscale 
Apply a logtransformation to the data for
evaluations. The default value is 
zeroscore 
Optional,
replace response values of zero with a derived or specified
numeric value, as an approach to overcome the presence of zeroes
when evaluation in the
logarithmic scale ( 
addconstant 
Optional,
add a numeric constant to all response values, as an
approach to overcome the presence of zeroes when evaluation in the
logarithmic scale 
rightcensor 
Optional, can be specified with a numeric
value where any value equal to or greater will be regarded as
right censored in the evaluation. The value of 
leftcensor 
Optional, can be specified with a numeric
value where any value equal to or lesser will be regarded as
left censored in the evaluation. The value of 
digits 
Optional, for output display purposes in graphs
and table methods, values will be rounded to this numeric
value. Only the integers of 0, 1, 2, 3, and 4 are accepted. No
rounding is done during any calculations. The default value is

refgrp 
Optional, specify one of the factor levels to be the
“reference group”, such as a “control” group.
The default value is 
stamps 
Optional, specify a time stamp in graphs, along
with cg package
version identification. The default value is 
The input data frame dfr
can be of the format
"listed"
or "groupcolumns"
. Another distinguishing
characteristic is whether or not it contains censored data
representations.
Censored observations can be represented by <
for
leftcensoring
and >
for
rightcensoring. The <
value refers to values less than or equal
to a numeric value. For example, <0.76
denotes a leftcensored
value of 0.76
or less. Similarly, >2.02
denotes a value of 2.02 or greater for
a rightcensored value. There must be no space between the direction
indicator and the numeric value. These representations can be used in
either the listed
or groupcolumns
formats for dfr
.
No intervalcensored representations are currently handled when
format="groupcolumns"
.
If format="groupcolumns"
for dfr
is specified, then the
number of columns must equal the number of groups, and any censored
values must follow the <
and >
representations.
The individual group values are of mode character, since any
censored values will be represented for example as <0.76
or
>2.02
. If any of the groups have less number of
observations than any others, i.e. there are unequal sample sizes,
then the corresponding "no data" cells in the data frame need to
contain empty quote ""
values.
If format="listed"
for dfr
is specified, then there may be
anywhere from two to four columns for an input data frame.
The first column has the group levels to define the
factor, and the second column contains the response values. Censored
representations of <
and >
can be used here. One or
both of
rightcensor
or leftcensor
may also be specified as a
number. If
a number is specified for rightcensor
, then all values in
the second column equal to this value will be processed as
rightcensored. Analogously, if
a number is specified for leftcensor
, then all values in
the second column equal to this value will be processed as
leftcensored. WARNING: This should be used cautiously to make sure the
equality occurs as desired. This convention is designed for simple
Type I censoring scenarios.
Like the two column case, the first column has
the group
levels to define the
factor, and the second column contains the response values, which will
all be coerced to numeric. Any censoring information must be specified
in the third column. Borrowing the convention of Surv
from the survival package, 0
=right censored, 1
=no censoring, and
2
=left censored. If rightcensor=NULL
and
leftcensor=NULL
are left as defaults in the call, and
values of 0, 1, and 2 are all represented, then the
processing will create a suitable data frame dfru
for
modeling that the canonical survreg
function understands.
However, if 0 and 1 are the only specified values
in the third censoring status column, then one of
rightcensor=TRUE
or leftcensor=TRUE
must be specified,
but NOT both, or an error message will occur. A column of all 1's or
all 0's will also raise an error message.
Like the two column case, the first column has
the group
levels to define the
factor. The second and third columns need to have numeric response
information, and the fourth column needs to have censoring
status. This is the most general representation, where any combination
of leftcensoring, rightcensoring, and intervalcensoring is
permitted. The rightcensor
and leftcensor
input
arguments are ignored and set to NULL
. IMPORTANT: The
convention of Surv
from the survival package, 0=right censored, 1=no censoring, and
2=left censored, 3=interval censored, and
type="interval"
,
is followed. For status=0, 1, and 2, the second and
third columns match in value, so that the status variable in the
fourth column distinguishes the lower and upper bounds for the
rightcensored (0) and leftcensored (2) cases.
For status=3, the two values differ to
define the interval boundaries. The
processing will create a suitable data frame dfru
for
modeling that the canonical survreg
and survfit
functions from the survival package understand.
If zeroscore="estimate"
is specified, a number
close to zero is derived to replace all zeroes for subsequent
logscale analyses. A spline fit (using spline
and
method="natural"
)
of the log of the
response vector on the original response vector is performed. The
zeroscore is then derived from the logscale value of the spline curve at the original
scale value of zero. This approach comes from the concept of
arithmeticlogarithmic scaling discussed in Tukey, Ciminera, and
Heyse (1985).
If addconstant="simple"
or
addconstant="VR"
is specified, a number is derived and added
to all response values.
"simple"
Taken from the "white" book on S (Chambers and Hastie, 1992),
page 68. The range (max  min
) of the response values
is multiplied by 0.0001
to derive the number to add to all the
response values.
"VR"
Based on the logtrans
function discussed in Venables and Ripley
(2002), pages 171172 and available in the MASS
package. The algorithm applies a BoxCox profile likelihood
approach with a log scale translation model.
A cgOneFactorData
object is returned, with the following slots:
dfr 
The original input data frame that is the specified value of the

dfru 
Processed version of the input data frame, which will be used for the various evaluation methods. 
fmt.dfru 
A list version of the input data frame, which will only
differ from the 
has.censored 
Boolean 
settings 
A list of properties associated with the data frame:

Contact [email protected] for bug reports, questions, concerns, and comments.
Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]
Tukey, J.W., Ciminera, J.L., and Heyse, J.F. (1985). "Testing the Statistical Certainty of a Response to Increasing Doses of a Drug," Biometrics, Volume 41, 295301.
Chambers, J.M, and Hastie, T.R. (1992), Statistical Modeling in S. Chapman & Hall/CRC.
Venables, W. N., and Ripley, B. D. (2002), Modern Applied Statistics with S. Fourth edition. Springer.
Surv
, canine
,
gmcsfcens
,
prepare
1 2 3 4 5 6 7 8 9 10 11 12 13  data(canine)
canine.data < prepareCGOneFactorData(canine, format="groupcolumns",
analysisname="Canine",
endptname="Prostate Volume",
endptunits=expression(plain(cm)^3),
digits=1, logscale=TRUE, refgrp="CC")
## Censored Data
data(gmcsfcens)
gmcsfcens.data < prepareCGOneFactorData(gmcsfcens, format="groupcolumns",
analysisname="cytokine",
endptname="GMCSF (pg/ml)",
logscale=TRUE)

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