Fits a binary or ordinal logistic model for a given design matrix and response vector with no missing values in either. Ordinary or penalized maximum likelihood estimation is used.
Predict
(Package: rms) :
Compute Predicted Values and Confidence Limits
Predict allows the user to easily specify which predictors are to vary. When the vector of values over which a predictor should vary is not specified, the range will be all levels of a categorical predictor or equally-spaced points between the datadist"Low:prediction" and "High:prediction" values for the variable (datadist by default uses the 10th smallest and 10th largest predictor values in the dataset). Predicted values are the linear predictor (X beta), a user-specified transformation of that scale, or estimated probability of surviving past a fixed single time point given the linear predictor. Predict is usually used for plotting predicted values but there is also a print method.
ExProb
(Package: rms) :
Function Generator For Exceedance Probabilities
For an orm object generates a function for computing the estimates of the function Prob(Y>=y) given one or more values of the linear predictor using the reference (median) intercept. This function can optionally be evaluated at only a set of user-specified y values, otherwise a right-step function is returned. There is a plot method for plotting the step functions, and if more than one linear predictor was evaluated multiple step functions are drawn. ExProb is especially useful for nomogram.
groupkm
(Package: rms) :
Kaplan-Meier Estimates vs. a Continuous Variable
Function to divide x (e.g. age, or predicted survival at time u created by survest) into g quantile groups, get Kaplan-Meier estimates at time u (a scaler), and to return a matrix with columns x=mean x in quantile, n=number of subjects, events=no. events, and KM=K-M survival at time u, std.err = s.e. of -log K-M. Confidence intervals are based on -log S(t). Instead of supplying g, the user can supply the minimum number of subjects to have in the quantile group (m, default=50). If cuts is given (e.g. cuts=c(0,.1,.2,...,.9,.1)), it overrides m and g. Calls Therneau's survfitKM in the survival package to get Kaplan-Meiers estimates and standard errors.
nomogram
(Package: rms) :
Draw a Nomogram Representing a Regression Fit
Draws a partial nomogram that can be used to manually obtain predicted values from a regression model that was fitted with rms. The nomogram does not have lines representing sums, but it has a reference line for reading scoring points (default range 0–100). Once the reader manually totals the points, the predicted values can be read at the bottom. Non-monotonic transformations of continuous variables are handled (scales wrap around), as are transformations which have flat sections (tick marks are labeled with ranges). If interactions are in the model, one variable is picked as the “axis variable”, and separate axes are constructed for each level of the interacting factors (preference is given automatically to using any discrete factors to construct separate axes) and levels of factors which are indirectly related to interacting factors (see DETAILS). Thus the nomogram is designed so that only one axis is actually read for each variable, since the variable combinations are disjoint. For categorical interacting factors, the default is to construct axes for all levels. The user may specify coordinates of each predictor to label on its axis, or use default values. If a factor interacts with other factors, settings for one or more of the interacting factors may be specified separately (this is mandatory for continuous variables). Optional confidence intervals will be drawn for individual scores as well as for the linear predictor. If more than one confidence level is chosen, multiple levels may be displayed using different colors or gray scales. Functions of the linear predictors may be added to the nomogram.
predictrms
(Package: rms) :
Predicted Values from Model Fit
The predict function is used to obtain a variety of values or predicted values from either the data used to fit the model (if type="adjto" or "adjto.data.frame" or if x=TRUE or linear.predictors=TRUE were specified to the modeling function), or from a new dataset. Parameters such as knots and factor levels used in creating the design matrix in the original fit are "remembered". See the Function function for another method for computing the linear predictors.
anova.rms
(Package: rms) :
Analysis of Variance (Wald and F Statistics)
The anova function automatically tests most meaningful hypotheses in a design. For example, suppose that age and cholesterol are predictors, and that a general interaction is modeled using a restricted spline surface. anova prints Wald statistics (F statistics for an ols fit) for testing linearity of age, linearity of cholesterol, age effect (age + age by cholesterol interaction), cholesterol effect (cholesterol + age by cholesterol interaction), linearity of the age by cholesterol interaction (i.e., adequacy of the simple age * cholesterol 1 d.f. product), linearity of the interaction in age alone, and linearity of the interaction in cholesterol alone. Joint tests of all interaction terms in the model and all nonlinear terms in the model are also performed. For any multiple d.f. effects for continuous variables that were not modeled through rcs, pol, lsp, etc., tests of linearity will be omitted. This applies to matrix predictors produced by e.g. poly or ns. print.anova.rms is the printing method. plot.anova.rms draws dot charts depicting the importance of variables in the model, as measured by Wald chi-square, chi-square minus d.f., AIC, P-values, partial R^2, R^2 for the whole model after deleting the effects in question, or proportion of overall model R^2 that is due to each predictor. latex.anova.rms is the latex method. It substitutes Greek/math symbols in column headings, uses boldface for TOTAL lines, and constructs a caption. Then it passes the result to latex.default for conversion to LaTeX.
Computes variance inflation factors from the covariance matrix of parameter estimates, using the method of Davis et al. (1986), which is based on the correlation matrix from the information matrix.
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