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Create a STEP Graph

Source: R/g_step.R
g_step.Rd

[Stable]

Based on the STEP results, creates a ggplot graph showing the estimated HR or OR along the continuous biomarker value subgroups.

Usage 

g_step(
  df,
  use_percentile = "Percentile Center" %in% names(df),
  est = list(col = "black", lty = 1),
  ci_ribbon = list(fill = getOption("ggplot2.discrete.colour")[1], alpha = 0.5),
  col = getOption("ggplot2.discrete.colour")
)

# S3 method for step
tidy(x, ...)

Arguments

df

(tibble)
result of tidy.step().

use_percentile

(flag)
whether to use percentiles for the x axis or actual biomarker values.

est

(named list)
col and lty settings for estimate line.

ci_ribbon

(named list or NULL)
fill and alpha settings for the confidence interval ribbon area, or NULL to not plot a CI ribbon.

col

(character)
colors.

x

(step matrix)
results from fit_survival_step().

...

not used here.

Value

The ggplot2 object.

A tibble with one row per STEP subgroup. The estimates and CIs are on the HR or OR scale, respectively. Additional attributes carry meta data also used for plotting.

Functions

  • tidy(step): Custom Tidy Method for STEP Results

    Tidy the STEP results into a tibble to format them ready for plotting.

See also

g_step() which consumes the result from this function.

Examples 

library(nestcolor)
library(survival)
lung$sex <- factor(lung$sex)

# Survival example.
vars <- list(
  time = "time",
  event = "status",
  arm = "sex",
  biomarker = "age"
)

step_matrix <- fit_survival_step(
  variables = vars,
  data = lung,
  control = c(control_coxph(), control_step(num_points = 10, degree = 2))
)
step_data <- broom::tidy(step_matrix)

# Default plot.
g_step(step_data)


# Add the reference 1 horizontal line.
library(ggplot2)
g_step(step_data) +
  ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2)


# Use actual values instead of percentiles, different color for estimate and no CI,
# use log scale for y axis.
g_step(
  step_data,
  use_percentile = FALSE,
  est = list(col = "blue", lty = 1),
  ci_ribbon = NULL
) + scale_y_log10()


# Adding another curve based on additional column.
step_data$extra <- exp(step_data$`Percentile Center`)
g_step(step_data) +
  ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = "green")


# Response example.
vars <- list(
  response = "status",
  arm = "sex",
  biomarker = "age"
)

step_matrix <- fit_rsp_step(
  variables = vars,
  data = lung,
  control = c(
    control_logistic(response_definition = "I(response == 2)"),
    control_step()
  )
)
step_data <- broom::tidy(step_matrix)
g_step(step_data)

library(survival)
lung$sex <- factor(lung$sex)
vars <- list(
  time = "time",
  event = "status",
  arm = "sex",
  biomarker = "age"
)
step_matrix <- fit_survival_step(
  variables = vars,
  data = lung,
  control = c(control_coxph(), control_step(num_points = 10, degree = 2))
)
broom::tidy(step_matrix)
#> # A tibble: 10 × 12
#>    Percenti…¹ Perce…² Perce…³ Inter…⁴ Inter…⁵ Inter…⁶     n events Hazar…⁷    se
#>  *      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl>  <dbl>   <dbl> <dbl>
#>  1     0.0909  0        0.341    50      39      59      83     56   0.423 0.438
#>  2     0.182   0        0.432    54      39      61.0    99     68   0.541 0.367
#>  3     0.273   0.0227   0.523    57      43.2    64     122     85   0.661 0.297
#>  4     0.364   0.114    0.614    59.5    50.8    66     117     81   0.977 0.345
#>  5     0.455   0.205    0.705    62      55      68     118     84   0.839 0.354
#>  6     0.545   0.295    0.795    64      58      70     115     82   0.571 0.369
#>  7     0.636   0.386    0.886    67      60      73.2   119     90   0.423 0.358
#>  8     0.727   0.477    0.977    69      63      76.8   116     88   0.481 0.344
#>  9     0.818   0.568    1        71      65      82     100     79   0.501 0.357
#> 10     0.909   0.659    1        74      67      82      85     68   0.606 0.416
#> # … with 2 more variables: ci_lower <dbl>, ci_upper <dbl>, and abbreviated
#> #   variable names ¹​`Percentile Center`, ²​`Percentile Lower`,
#> #   ³​`Percentile Upper`, ⁴​`Interval Center`, ⁵​`Interval Lower`,
#> #   ⁶​`Interval Upper`, ⁷​`Hazard Ratio`
#> # ℹ Use `colnames()` to see all variable names