| bj {Design} | R Documentation |
bj fits the Buckley-James distribution-free least squares multiple
regression model to a possibly right-censored response variable.
This model reduces to ordinary least squares if
there is no censoring. By default, model fitting is done after
taking logs of the response variable.
bj uses the Design class
for automatic anova, fastbw, validate, Function, nomogram,
summary, plot, bootcov, and other functions. The bootcov
function may be worth using with bj fits, as the properties of the
Buckley-James covariance matrix estimator are not fully known for
strange censoring patterns.
The residuals.bj function exists mainly to compute
residuals and to censor them (i.e., return them as
Surv objects) just as the original
failure time variable was censored. These residuals are useful for
checking to see if the model also satisfies certain distributional assumptions.
To get these residuals, the fit must have specified y=TRUE.
The bjplot function is a special plotting function for objects
created by bj with x=TRUE, y=TRUE in effect. It produces three
scatterplots for every covariate in the model: the first plots the
original situation, where censored data are distingushed from
non-censored data by a different plotting symbol. In the second plot,
called a renovated plot, vertical lines show how censored data were
changed by the procte< 0000644 0000000 0000000 00000004763 10767032220 020451 0 ustar root root
| matinv {Design} | R Documentation |
This function inverts or partially inverts a matrix using pivoting (the sweep operator). It is useful for sequential model-building.
matinv(a, which, negate=TRUE, eps=1e-12)
a |
square matrix to invert or partially invert. May have been inverted or partially inverted previously by matinv, in which case its "swept" attribute is updated. Will un-invert if already inverted. |
which |
vector of column/row numbers in a to invert. Default is all, for total inverse. |
negate |
So that the algorithm can keep track of which pivots have been swept as well as roundoff errors, it actually returns the negative of the inverse or partial inverse. By default, these elements are negated to give the usual expected result. Set negate=FALSE if you will be passing the result right back into matinv, otherwise, negate the submatrix before sending back to matinv. |
eps |
singularity criterion |
a square matrix, with attributes "rank" and "swept".
Clarke MRB (1982). Algorithm AS 178: The Gauss-Jordan sweep operator with detection of collinearity. Appl Statist 31:166–9.
Ridout MS, Cobb JM (1986). Algorithm AS R78 : A remark on algorithm AS 178: The Gauss-Jordan sweep operator with detection of collinearity. Appl Statist 38:420–2.
lrm, solve
a <- diag(1:3) a.inv1 <- matinv(a, 1, negate=FALSE) #Invert with respect to a[1,1] a.inv1 a.inv <- -matinv(a.inv1, 2:3, negate=FALSE) #Finish the job a.inv solve(a)
| latex.cph {Design} | R Documentation |
Creates a file containing a LaTeX representation of the fitted model.
## S3 method for class 'cph':
latex(object, title,
file=paste(first.word(deparse(substitute(object))),".tex",sep=""),
append=FALSE, surv=TRUE, maxt=FALSE, which=NULL, varnames, columns=65,
inline=FALSE, before=if(inline)"" else "& &", dec=3,
pretrans=TRUE, caption, ...) # for cph fit
## S3 method for class 'lrm':
latex(object, title, file, append, which, varnames,
columns, inline, before, pretrans, caption, ...) # for lrm fit
## S3 method for class 'ols':
latex(object, title, file, append, which, varnames,
columns, inline, before, pretrans, caption, ...) # ols fit
## S3 method for class 'pphsm':
latex(object, title, file, append, which=NULL, varnames,
columns, inline, before, pretrans, caption, ...) # pphsm fit
## S3 method for class 'psm':
latex(object, title, file, append, which=NULL, varnames,
columns, inline, before, pretrans, caption, ...) # psm fit
object |
a fit object created by a Design fitting function.
|
title |
ignored |
file |
|
append |
see latex.default |
surv |
if surv=TRUE was specified to cph, the underlying survival
probabilities from object$surv.summary will be placed in a table
unless surv=FALSE.
|
maxt |
if the maximum follow-up time in the data (object$maxtime) exceeds the
last entry in object$surv.summary, underlying survival estimates at
object$maxtime will be added to the table if maxt=TRUE.
|
which |
|
varnames |
|
columns |
|
inline |
|
before |
|
dec |
|
pretrans |
see latex.default |
caption |
a character string specifying a title for the equation to be centered and typeset in bold face. Default is no title. |
... |
ignored |
the name of the created file, with class c("latex","file"). This
object works with latex viewing and printing commands in Hmisc.
Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu
latex.Design, rcspline.restate, latex.default
## Not run:
units(ftime) <- "Day"
f <- cph(Surv(ftime, death) ~ rcs(age)+sex, surv=TRUE, time.inc=60)
w <- latex(f) #Interprets fitted model and makes table of S0(t)
#for t=0,60,120,180,... Creates file f.tex
w #displays image, if viewer installed
latex(f,file="") # send LaTeX code to the screen
## End(Not run)
| Function {Design} | R Documentation |
Function is a class of functions for creating other S functions.
Function.Design is the method for creating S functions to compute
X beta, based on a model fitted with Design in effect.
Like latex.Design, Function.Design simplifies restricted cubic
spline functions and factors out terms in second-order interactions.
Function.Design will not work for models that have third-order
interactions involving restricted cubic splines.
Function.cph is a particular method for handling fits from cph, for
which an intercept (the negative of the centering constant) is added to
the model. sascode is a function that takes an S function such
as one created by Function and does most of the editing
to turn the function definition into
a fragment of SAS code for computing X beta from the fitted model, along
with assignment statements that initialize predictors to reference values.
## S3 method for class 'Design': Function(object, intercept=NULL, digits=max(8, .Options$digits), ...) ## S3 method for class 'cph': Function(object, intercept=-object$center, ...) # Use result as fun(predictor1=value1, predictor2=value2, ...) sascode(object, file='', append=FALSE)
object |
a fit created with Design in effect
|
intercept |
an intercept value to use (not allowed to be specified to Funct |