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.
boot_predict(fit, newdata, type = "response", R = 100, estimate_name = NA, confint_sep = " to ", condense = TRUE, boot_compare = TRUE, compare_name = NA, comparison = "difference", ref_symbol = "-", digits = c(2, 3))
Arguments
| fit | |
|---|---|
| newdata | Dataframe usually generated with
|
| type | the type of prediction required, see
|
| R | 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 |
| 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.
See also
/codefinalfit predict functions
Examples
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))#> Age Extent of spread Perforation Predicted probability of death #> 1 <40 years Submucosa No 0.28 (0.06 to 0.54) #> 2 <40 years Submucosa Yes 0.29 (0.07 to 0.61) #> 3 <40 years Adjacent structures No 0.71 (0.49 to 0.88) #> 4 <40 years Adjacent structures Yes 0.72 (0.49 to 0.91) #> Absolute risk difference #> 1 - #> 2 0.02 (-0.12 to 0.20, p=0.780) #> 3 0.45 (0.13 to 0.65, p<0.001) #> 4 0.44 (0.08 to 0.71, p=0.020)# 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")
