nomogram.mk6: Draw a Nomogram with modified function 'Nomogram'
In jixccf/QHScrnomo: Construct nomograms for competing risk models

Description Usage Arguments Details Value Note References See Also Examples

Draw a Nomogram from a regression model

nomogram.mk6(fit, ..., adj.to, lp = TRUE, lp.at,
lplabel = "Linear Predictor",
fun, fun.at, fun.lp.at, funlabel = "Predicted Value", fun.side,
interact = NULL, intercept = 1, conf.int = FALSE,
col.conf = c(1, 12), conf.space = c(0.08, 0.2),
conf.lp = c("representative", "all", "none"), est.all = TRUE,
abbrev = FALSE, minlength = 4, maxscale = 100, nint = 10,
label.every = 1, force.label = FALSE, xfrac = 0.35, cex.axis = 0.85,
cex.var = 1, col.grid = NULL, vnames = c("labels", "names"),
varname.label = TRUE, varname.label.sep = "=", ia.space = 0.7,
tck = NA, tcl = -0.25, lmgp = 0.4, omit = NULL, naxes, 
points.label = "Points",
total.points.label = "Total Points", total.sep.page = FALSE,
total.fun, verbose = FALSE, cap.labels = FALSE, total.min,
total.max, survtime, mikeomit = NULL)

`fit`	a regression model fit that was created with `library(rms)` in effect, and (usually) with `options(datadist = "object.name")` in effect.
`...`	settings of variables to use in constructing axes. If `datadist` was in effect, the default is to use `pretty(total range, nint)` for continuous variables, and the class levels for discrete ones. For `legend.nomabbrev`, `...` specifies optional parameters to pass to `legend`. Common ones are `bty = "n"` to suppress drawing the box. You may want to specify a non-proportionally spaced font (e.g., courier) number if abbreviations are more than one letter long. This will make the abbreviation definitions line up (e.g., specify `font = 2`, the default for courier). Ignored for `print`.
`adj.to`	If you didn't define `datadist` for all predictors, you will have to define adjustment settings for the undefined ones, e.g. `adj.to= list(age = 50, sex = "female")`.
`lp`	Set to `FALSE` to suppress creation of an axis for scoring X beta.
`lp.at`	If `lp=TRUE`, `lp.at` may specify a vector of settings of X beta. Default is to use `pretty(range of linear predictors, nint)`.
`lplabel`	label for linear predictor axis. Default is `"Linear Predictor"`.
`fun`	on another axis. If more than one transformation is plotted, put them in a list, e.g. `list(function(x) x/2, function(x) 2*x)`. Any function values equal to `NA` will be ignored.
`fun.at`	function values to label on axis. Default `fun` evaluated at `lp.at`. If more than one `fun` was specified, using a vector for `fun.at` will cause all functions to be evaluated at the same argument values. To use different values, specify a list of vectors for `fun.at`, with elements corresponding to the different functions (lists of vectors also applies to `fun.lp.at` and `fun.side`).
`fun.lp.at`	If you want to evaluate one of the functions at a different set of linear predictor values than may have been used in constructing the linear predictor axis, specify a vector or list of vectors of linear predictor values at which to evaluate the function. This is especially useful for discrete functions. The presence of this attribute also does away with the need for `nomogram` to compute numerical approximations of the inverse of the function. It also allows the user-supplied function to return `factor` objects, which is useful when e.g. a single tick mark position actually represents a range. If the `fun.lp.at` parameter is present, the `fun.at` vector for that function is ignored.
`funlabel`	label for `fun` axis. If more than one function was given but funlabel is of length one, it will be duplicated as needed. If `fun` is a list of functions for which you specified names (see the final example below), these names will be used as labels.
`fun.side`	a vector or list of vectors of `side` parameters for the `axis` function for labeling function values. Values may be 1 to position a tick mark label below the axis (the default), or 3 for above the axis. If for example an axis has 5 tick mark labels and the second and third will run into each other, specify `fun.side=c(1,1,3,1,1)` (assuming only one function is specified as `fun`).
`interact`	When a continuous variable interacts with a discrete one, axes are constructed so that the continuous variable moves within the axis, and separate axes represent levels of interacting factors. For interactions between two continuous variables, all but the axis variable must have discrete levels defined in `interact`. For discrete interacting factors, you may specify levels to use in constructing the multiple axes. For continuous interacting factors, you must do this. Examples: `interact = list(age = seq(10,70,by=10), treat = c("A","B","D"))`.
`intercept`	for models such as the ordinal logistic model with multiple intercepts, specifies which one to use in evaluating the linear predictor.
`conf.int`	confidence levels to display for each scoring. Default is `FALSE` to display no confidence limits. Setting `conf.int` to `TRUE` is the same as setting it to `c(0.7, 0.9)`, with the line segment between the 0.7 and 0.9 levels shaded using gray scale.
`col.conf`	colors corresponding to `conf.int`. Use fractions for gray scale(for UNIX S-PLUS).
`conf.space`	a 2-element vector with the vertical range within which to draw confidence bars, in units of 1=spacing between main bars. Four heights are used within this range (8 for the linear predictor if more than 16 unique values were evaluated), cycling them among separate confidence intervals to reduce overlapping
`conf.lp`	default is `"representative"` to group all linear predictors evaluated into deciles, and to show, for the linear predictor confidence intervals, only the mean linear predictor within the deciles along with the median standard error within the deciles. Set `conf.lp = "none"` to suppress confidence limits for the linear predictors, and to `"all"` to show all confidence limits.
`est.all`	To plot axes for only the subset of variables named in `...`, set `est.all = FALSE`. Note: This option only works when zero has a special meaning for the variables that are omitted from the graph
`abbrev`	Set to `TRUE` to use the `abbreviate` function to abbreviate levels of categorical factors, both for labeling tick marks and for axis titles. If you only want to abbreviate certain predictor variables, set `abbrev` to a vector of character strings containing their names.
`minlength`	`abbreviate` function. If you set `minlength = 1`, the letters of the alphabet are used to label tick marks for categorical predictors, and all letters are drawn no matter how close together they are. For labeling axes (interaction settings), `minlength = 1` causes `minlength = 4` to be used
`maxscale`	default maximum point score is 100
`nint`	number of intervals to label for axes representing continuous variables. See `pretty`.
`label.every`	Specify `label.every = i` to label on every `i`th tick mark
`force.label`	set to `TRUE` to force every tick mark intended to be labeled to have a label plotted (whether the labels run into each other or not)
`xfrac`	fraction of horizontal plot to set aside for axis titles
`cex.axis`	character size for tick mark labels
`cex.var`	character size for axis titles (variable names)
`col.grid`	If left unspecified, no vertical reference lines are drawn. Specify a vector of length one (to use the same color for both minor and major reference lines) or two (corresponding to the color for the major and minor divisions, respectively) containing colors, to cause vertical reference lines to the top points scale to be drawn. For R, a good choice is `col.grid = gray(c(0.8, 0.95))`.
`vnames`	By default, variable labels are used to label axes. Set `vnames = "names"` to instead use variable names.
`varname.label`	In constructing axis titles for interactions, the default is to add `(interacting.varname = level)` on the right. Specify `varname.label = FALSE` to instead use `"(level)"`.
`varname.label.sep`	If `varname.label = TRUE`, you can change the separator to something other than `=` by specifying this parameter.
`ia.space`	When multiple axes are draw for levels of interacting factors, the default is to group combinations related to a main effect. This is done by spacing the axes for the second to last of these within a group only 0.7 (by default) of the way down as compared with normal space of 1 unit.
`tck`	see `tck` under `par`
`tcl`	length of tick marks in nomogram
`lmgp`	spacing between numeric axis labels and axis (see `par` for `mgp`)
`omit`	vector of character strings containing names of variables for which to suppress drawing axes. Default is to show all variables.
`naxes`	maximum number of axes to allow on one plot. If the nomogram requires more than one “page”, the “Points” axis will be repeated at the top of each page when necessary.
`points.label`	a character string giving the axis label for the points scale
`total.points.label`	a character string giving the axis label for the total points scale
`total.sep.page`	set to `TRUE` to force the total points and later axes to be placed on a separate page
`total.fun`	a user-provided function that will be executed before the total points axis is drawn. Default is not toe xecute a function. This is useful e.g. when `total.sep.page = TRUE` and you wish to use `locator` to find the coordinates for positioning an abbreviation legend before it's too late and a new page is started (i.e., `total.fun = function() print(locator(1))`).
`verbose`	set to `TRUE` to get printed output detailing how tick marks are chosen and labeled for function axes. This is useful in seeing how certain linear predictor values cannot be solved for using inverse linear interpolation on the (requested linear predictor values, function values at these lp values). When this happens you will see `NA`s in the verbose output, and the corresponding tick marks will not appear in the nomogram.
`cap.labels`	logical: should the factor labels have their first letter capitalized?
`total.min`	the minimum point for the total point axis
`total.max`	the maxmum point for the total point axis
`survtime`	specified survival time for the predicted survival probability
`mikeomit`	a modified version of `omit`

a modified version of nomogram in rms package

a nomogram object

internal use only. please reference to nomogram details.

Banks J: Nomograms. Encylopedia of Statistical Sciences, Vol 6. Editors: S Kotz and NL Johnson. New York: Wiley; 1985.

Lubsen J, Pool J, van der Does, E: A practical device for the application of a diagnostic or prognostic function. Meth. Inform. Med. 17:127–129; 1978.

Wikipedia: Nomogram, http://en.wikipedia.org/wiki/Nomogram.

rms, plot.rms, plot.summary.rms, axis, pretty, approx, latex.rms, rms.Misc

data(prostate.dat)
dd <- datadist(prostate.dat)
options(datadist = "dd")
prostate.f <- cph(Surv(TIME_EVENT,EVENT_DOD == 1) ~ TX  + rcs(PSA,3) +
           BX_GLSN_CAT +  CLIN_STG + rcs(AGE,3) +
           RACE_AA, data = prostate.dat,
           x = TRUE, y= TRUE, surv=TRUE,time.inc = 144)
## make a cph nomogram
nomogram.mk6(prostate.f, survtime=120, lp=FALSE,
funlabel = "Predicted 10-year cumulative incidence")