Plot method for objects returned by the fitSmoothHazard function. Current plot types are hazard function and hazard ratio. The visreg package must be installed for type="hazard". This function accounts for the possible time-varying exposure effects.

plotHazardRatio(x, newdata, newdata2, ci, ci.lvl, ci.col, rug, xvar, ...)

# S3 method for singleEventCB
  type = c("hazard", "hr"),
  hazard.params = list(),
  increment = 1,
  xvar = NULL,
  ci = FALSE,
  ci.lvl = 0.95,
  rug = !ci,
  ci.col = "grey"

incrVar(var, increment = 1)



Fitted object of class glm, gam, cv.glmnet or gbm. This is the result from the casebase::fitSmoothHazard() function.


Required for type="hr". The newdata argument is the "unexposed" group, while the exposed group is defined by either: (i) a change (defined by the increment argument) in a variable in newdata defined by the var argument ; or (ii) an exposed function that takes a data-frame and returns the "exposed" group (e.g. exposed = function(data) transform(data, treat=1)). This is a generalization of the behavior of the rstpm2 plot function. It allows both numeric and factor variables to be incremented or decremented. See references for rstpm2 package. Only used for type="hr"


data.frame for exposed group. calculated and passed internally to plotHazardRatio function


Logical; if TRUE confidence bands are calculated. Only available for family="glm" and family="gam", and only used for type="hr", Default: !add. Confidence intervals for hazard ratios are calculated using the Delta Method.


Confidence level. Must be in (0,1), Default: 0.95. Only used for type="hr".


Confidence band color. Only used if argument ci=TRUE, Default: 'grey'. Only used for type="hr".


Logical. Adds a rug representation (1-d plot) of the event times (only for status=1), Default: !ci. Only used for type="hr".


Variable to be used on x-axis for hazard ratio plots. If NULL, the function defaults to using the time variable used in the call to fitSmoothHazard. In general, this should be any continuous variable which has an interaction term with another variable. Only used for type="hr".


further arguments passed to plot. Only used if type="hr". Any of lwd,lty,col,pch,cex will be applied to the hazard ratio line, or point (if only one time point is supplied to newdata).


plot type. Choose one of either "hazard" for hazard function or "hr" for hazard ratio. Default: type = "hazard".


Named list of arguments which will override the defaults passed to visreg::visreg(), The default arguments are list(fit = x, trans = exp, plot = TRUE, rug = FALSE, alpha = 1, partial = FALSE, overlay = TRUE). For example, if you want a 95% confidence band, specify hazard.params = list(alpha = 0.05). Note that The cond argument must be provided as a named list. Each element of that list specifies the value for one of the terms in the model; any elements left unspecified are filled in with the median/most common category. Only used for type="hazard". All other argument are used for type="hr". Note that the visreg package must be installed for type="hazard".


function that takes newdata and returns the exposed dataset (e.g. function(data) transform(data, treat = 1)). This argument takes precedence over the var argument, i.e., if both var and exposed are correctly specified, only the exposed argument will be used. Only used for type="hr".


Numeric value indicating how much to increment (if positive) or decrement (if negative) the var variable in newdata. See var argument for more details. Default is 1. Only used for type="hr".


specify the variable name for the exposed/unexposed (name is given as a character variable). If this argument is missing, then the exposed argument must be specified. This is the variable which will be incremented by the increment argument to give the exposed category. If var is coded as a factor variable, then increment=1 will return the next level of the variable in newdata. increment=2 will return two levels above, and so on. If the value supplied to increment is greater than the number of levels, this will simply return the max level. You can also decrement the categorical variable by specifying a negative value, e.g., increment=-1 will return one level lower than the value in newdata. If var is a numeric, than increment will increment (if positive) or decrement (if negative) by the supplied value. Only used for type="hr".


a plot of the hazard function or hazard ratio. For type="hazard", a data.frame (returned invisibly) of the original data used in the fitting along with the data used to create the plots including predictedhazard which is the predicted hazard for a given covariate pattern and time. predictedloghazard is the predicted hazard on the log scale. lowerbound and upperbound are the lower and upper confidence interval bounds on the hazard scale (i.e. used to plot the confidence bands). standarderror is the standard error of the log hazard or log hazard ratio (only if family="glm" or family="gam"). For type="hr", log_hazard_ratio and hazard_ratio is returned, and if ci=TRUE, standarderror (on the log scale) and lowerbound and upperbound of the hazard_ratio are returned.


This function has only been thoroughly tested for family="glm". If the user wants more customized plot aesthetics, we recommend saving the results to a data.frame and using the graphical package of their choice.


Mark Clements and Xing-Rong Liu (2019). rstpm2: Smooth Survival Models, Including Generalized Survival Models. R package version 1.5.1.

Breheny P and Burchett W (2017). Visualization of Regression Models Using visreg. The R Journal, 9: 56-71.

See also


if (requireNamespace("splines", quietly = TRUE)) { data("simdat") # from casebase package library(splines) simdat <- transform(simdat[sample(1:nrow(simdat), size = 200),], treat = factor(trt, levels = 0:1, labels = c("control","treatment"))) fit_numeric_exposure <- fitSmoothHazard(status ~ trt*bs(eventtime), data = simdat, ratio = 1, time = "eventtime") fit_factor_exposure <- fitSmoothHazard(status ~ treat*bs(eventtime), data = simdat, ratio = 1, time = "eventtime") newtime <- quantile(fit_factor_exposure[["data"]][[fit_factor_exposure[["timeVar"]]]], probs = seq(0.05, 0.95, 0.01)) par(mfrow = c(1,3)) plot(fit_numeric_exposure, type = "hr", newdata = data.frame(trt = 0, eventtime = newtime), exposed = function(data) transform(data, trt = 1), xvar = "eventtime", ci = TRUE) #by default this will increment `var` by 1 for exposed category plot(fit_factor_exposure, type = "hr", newdata = data.frame(treat = factor("control", levels = c("control","treatment")), eventtime = newtime), var = "treat", increment = 1, xvar = "eventtime", ci = TRUE, ci.col = "lightblue", xlab = "Time", main = "Hazard Ratio for Treatment", ylab = "Hazard Ratio", lty = 5, lwd = 7, rug = TRUE) # we can also decrement `var` by 1 to give hazard ratio for control/treatment result <- plot(fit_factor_exposure, type = "hr", newdata = data.frame(treat = factor("treatment", levels = c("control","treatment")), eventtime = newtime), var = "treat", increment = -1, xvar = "eventtime", ci = TRUE) # see data used to create plot head(result) }
#> treat eventtime log_hazard_ratio standarderror hazard_ratio lowerbound #> 5% treatment 0.3488444 0.5617254 0.7422200 1.753696 0.4094260 #> 6% treatment 0.3902785 0.6649501 0.7012281 1.944394 0.4919237 #> 7% treatment 0.4148271 0.7239973 0.6788513 2.062662 0.5452413 #> 8% treatment 0.4512202 0.8086798 0.6482910 2.244942 0.6300556 #> 9% treatment 0.4636149 0.8367497 0.6385929 2.308850 0.6604266 #> 10% treatment 0.4862850 0.8870854 0.6217826 2.428043 0.7177844 #> upperbound #> 5% 7.511610 #> 6% 7.685473 #> 7% 7.803102 #> 8% 7.998922 #> 9% 8.071737 #> 10% 8.213317