# rmsMisc

From rms v2.0-2
0th

Percentile

##### Miscellaneous Design Attributes and Utility Functions

These functions are used internally to anova.rms, fastbw, etc., to retrieve various attributes of a design. These functions allow some fitting functions not in the rms series (e.g,, lm, glm) to be used with rms.Design, fastbw, and similar functions.

For vcov, there are these functions: vcov.default, vcov.lm, vcov.glm. The oos.loglik function for each type of model implemented computes the -2 log likelihood for out-of-sample data (i.e., data not necessarily used to fit the model) evaluated at the parameter estimates from a model fit. Vectors for the model's linear predictors and response variable must be given. oos.loglik is used primarily by bootcov.

The Getlim function retrieves distribution summaries from the fit or from a datadist object. It handles getting summaries from both sources to fill in characteristics for variables that were not defined during the model fit. Getlimi returns the summary for an individual model variable.

The related.predictors function returns a list containing variable numbers that are directly or indirectly related to each predictor. The interactions.containing function returns indexes of interaction effects containing a given predictor. The param.order function returns a vector of logical indicators for whether parameters are associated with certain types of effects (nonlinear, interaction, nonlinear interaction).

The Penalty.matrix function builds a default penalty matrix for non-intercept term(s) for use in penalized maximum likelihood estimation. The Penalty.setup function takes a constant or list describing penalty factors for each type of term in the model and generates the proper vector of penalty multipliers for the current model.

The lrtest function does likelihood ratio tests for two nested models, from fits that have stats components with "Model L.R." values. For models such as psm, survreg, ols, lm which have scale parameters, it is assumed that scale parameter for the smaller model is fixed at the estimate from the larger model (see the example).

univarLR takes a multivariable model fit object from rms and re-fits a sequence of models containing one predictor at a time. It prints a table of likelihood ratio $chi^2$ statistics from these fits.

The Newlabels function is used to override the variable labels in a fit object. Likewise, Newlevels can be used to create a new fit object with levels of categorical predictors changed. These two functions are especially useful when constructing nomograms.

rmsFit is used to convert a fit from non-rms functions (e.g., glm) that were invoked with rms in effect to rms functions so that anova.rms will be called by anova(), etc. So that the original fit's residuals and print methods, if they exist, will be called, there are functions print.rms and residuals.rms to dispatch them.

Keywords
models, methods
##### Usage
## S3 method for class 'cph':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'Glm':
vcov(object, \dots)
## S3 method for class 'Gls':
vcov(object, \dots)
## S3 method for class 'lrm':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'ols':
vcov(object, regcoef.only=TRUE, \dots)
## S3 method for class 'psm':
vcov(object, regcoef.only=TRUE, \dots)oos.loglik(fit, ...)## S3 method for class 'ols':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'lrm':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'cph':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'psm':
oos.loglik(fit, lp, y, \dots)
## S3 method for class 'Glm':
oos.loglik(fit, lp, y, \dots)num.intercepts(fit)Getlim(at, allow.null=FALSE, need.all=TRUE)
Getlimi(name, Limval, need.all=TRUE)related.predictors(at, type=c("all","direct"))
interactions.containing(at, pred)
param.order(at, term.order)Penalty.matrix(at, X)
Penalty.setup(at, penalty)lrtest(fit1, fit2)
## S3 method for class 'lrtest':
print(x, \dots)univarLR(fit)Newlabels(fit, ...)
Newlevels(fit, ...)
## S3 method for class 'rms':
Newlabels(fit, labels, \dots)
## S3 method for class 'rms':
Newlevels(fit, levels, \dots)rmsFit(fit)  # fit from glm, lm, etc.,then use anova etc. on result
##### Arguments
fit
result of a fitting function
object
result of a fitting function
regcoef.only
For fits such as parametric survival models which have a final row and column of the covariance matrix for a non-regression parameter such as a log(scale) parameter, setting regcoef.only=TRUE causes only the first p rows
at
Design element of a fit
pred
index of a predictor variable (main effect)
fit1
fit2
fit objects from lrm,ols,psm,cph etc. It doesn't matter which fit object is the sub-model.
lp
linear predictor vector for oos.loglik. For proportional odds ordinal logistic models, this should have used the first intercept only. If lp and y are omitted, the -2 log likelihood for the original fit are returne
y
values of a new vector of responses passed to oos.loglik.
name
the name of a variable in the model
Limval
an object returned by Getlim
allow.null
prevents Getlim from issuing an error message if no limits are found in the fit or in the object pointed to by options(datadist=)
need.all
set to FALSE to prevent Getlim or Getlimi from issuing an error message if data for a variable are not found
type
set to "direct" to return lists of indexes of directly related factors only (those in interactions with the predictor)
term.order
1 for all parameters, 2 for all parameters associated with either nonlinear or interaction effects, 3 for nonlinear effects (main or interaction), 4 for interaction effects, 5 for nonlinear interaction effects.
X
a design matrix, not including columns for intercepts
penalty
a vector or list specifying penalty multipliers for types of model terms
x
a result of lrtest
labels
a character vector specifying new labels for variables in a fit. To give new labels for all variables, you can specify labels of the form labels=c("Age in Years","Cholesterol"), where the list of new labels is assumed to be the l
levels
a list of named vectors specifying new level labels for categorical predictors. This will override parms as well as datadist information (if available) that were stored with the fit.
...
other arguments
##### Value

• vcov returns a variance-covariance matrix, and num.intercepts returns an integer with the number of intercepts in the model. oos.loglik returns a scalar -2 log likelihood value. Getlim returns a list with components limits and values, either stored in fit or retrieved from the object created by datadist and pointed to in options(datadist=). related.predictors returns a list of vectors, and interactions.containing returns a vector. param.order returns a logical vector corresponding to non-strata terms in the model. Penalty.matrix returns a symmetric matrix with dimension equal to the number of slopes in the model. For all but categorical predictor main effect elements, the matrix is diagonal with values equal to the variances of the columns of X. For segments corresponding to c-1 dummy variables for c-category predictors, puts a c-1 x c-1 sub-matrix in Penalty.matrix that is constructed so that a quadratic form with Penalty.matrix in the middle computes the sum of squared differences in parameter values about the mean, including a portion for the reference cell in which the parameter is by definition zero. Newlabels returns a new fit object with the labels adjusted. rmsFit returns the original object but with oldClass of "rms" and with a new attribute "fitFunction" containing the original vector of classes.

rms, fastbw, anova.rms, summary.lm, summary.glm, datadist, vif, bootcov

##### Aliases
• rmsMisc
• calibrate.rms
• vcov.cph
• vcov.Glm
• vcov.Gls
• vcov.lrm
• vcov.ols
• vcov.psm
• oos.loglik
• oos.loglik.ols
• oos.loglik.lrm
• oos.loglik.cph
• oos.loglik.psm
• oos.loglik.Glm
• num.intercepts
• Getlim
• Getlimi
• related.predictors
• interactions.containing
• param.order
• Penalty.matrix
• Penalty.setup
• lrtest
• univarLR
• Newlabels
• Newlevels
• Newlabels.rms
• Newlevels.rms
• rmsFit
• print.rms
• residuals.rms
• print.lrtest
• survest.rms
##### Examples
f <- psm(S ~ x1 + x2 + sex + race, dist='gau')
g <- psm(S ~ x1 + sex + race, dist='gau',
fixed=list(scale=exp(f\$parms)))
lrtest(f, g)

g <- Newlabels(f, c(x2='Label for x2'))
g <- Newlevels(g, list(sex=c('Male','Female'),race=c('B','W')))
nomogram(g)
Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)

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