boot_predict: Bootstrap simulation for model prediction

Description

Generate model predictions against a specified set of explanatory levels with bootstrapped confidence intervals. Add a comparison by difference or ratio of the first row of newdata with all subsequent rows.

Usage

boot_predict(fit, newdata, type = "response", R = 100,
  estimate_name = NULL, confint_sep = " to ", condense = TRUE,
  boot_compare = TRUE, compare_name = NULL,
  comparison = "difference", ref_symbol = "-", digits = c(2, 3))

Arguments

fit

A model generated using lm, glm, lmmulti, and glmmulti.

newdata

Dataframe usually generated with finalfit_newdata.

type

the type of prediction required, see predict.glm. The default for glm models is on the scale of the response variable. Thus for a binomial model the default predictions are predicted probabilities.

Number of simulations. Note default R=100 is very low.

estimate_name

Name to be given to prediction variable y-hat.

confint_sep

String separating lower and upper confidence interval

condense

Logical. FALSE gives numeric values, usually for plotting. TRUE gives table for final output.

boot_compare

Include a comparison with the first row of newdata with all subsequent rows. See boot_compare.

compare_name

Name to be given to comparison metric.

comparison

Either "difference" or "ratio".

ref_symbol

Reference level symbol

digits

Rounding for estimate values and p-values, default c(2,3).

Value

A dataframe of predicted values and confidence intervals, with the option of including a comparison of difference between first row and all subsequent rows of newdata.

Details

To use this, first generate newdata for specified levels of explanatory variables using finalfit_newdata. Pass model objects from lm, glm, lmmulti, and glmmulti. The comparison metrics are made on individual bootstrap samples distribution returned as a mean with confidence intervals. A p-value is generated on the proportion of values on the other side of the null from the mean, e.g. for a ratio greater than 1.0, p is the number of bootstrapped predictions under 1.0, multiplied by two so is two-sided.

Examples

Run this code

# NOT RUN {
library(finalfit)
library(dplyr)

# Predict probability of death across combinations of factor levels
explanatory = c("age.factor", "extent.factor", "perfor.factor")
dependent = 'mort_5yr'

# Generate combination of factor levels
colon_s %>%
  finalfit_newdata(explanatory = explanatory, newdata = list(
    c("<40 years",  "Submucosa", "No"),
    c("<40 years", "Submucosa", "Yes"),
    c("<40 years", "Adjacent structures", "No"),
    c("<40 years", "Adjacent structures", "Yes")
   )) -> newdata

# Run simulation
colon_s %>%
  glmmulti(dependent, explanatory) %>%
  boot_predict(newdata, estimate_name = "Predicted probability of death",
    compare_name = "Absolute risk difference", R=100, digits = c(2,3))

# Plotting
explanatory = c("nodes", "extent.factor", "perfor.factor")
colon_s %>%
  finalfit_newdata(explanatory = explanatory, rowwise = FALSE, newdata = list(
  rep(seq(0, 30), 4),
  c(rep("Muscle", 62), rep("Adjacent structures", 62)),
  c(rep("No", 31), rep("Yes", 31), rep("No", 31), rep("Yes", 31))
)) -> newdata

colon_s %>%
  glmmulti(dependent, explanatory) %>%
  boot_predict(newdata, boot_compare = FALSE, R=100, condense=FALSE) -> plot

  library(ggplot2)
  theme_set(theme_bw())
  plot %>%
    ggplot(aes(x = nodes, y = estimate, ymin = estimate_conf.low,
        ymax = estimate_conf.high, fill=extent.factor))+
      geom_line(aes(colour = extent.factor))+
      geom_ribbon(alpha=0.1)+
      facet_grid(.~perfor.factor)+
      xlab("Number of postive lymph nodes")+
      ylab("Probability of death")+
      labs(fill = "Extent of tumour", colour = "Extent of tumour")+
      ggtitle("Probability of death by lymph node count")
# }