Extract the partial effects of non-linear model terms

This function basically creates a new df from data for each term in terms, creating a range from minimum and maximum of the predict(fit, newdata=df, type="terms"). Terms are then stacked to a tidy data frame.

get_terms(data, fit, terms, ...)

Arguments

data	A data frame containing variables used to fit the model. Only first row will be used.
fit	A fitted object of class gam.
terms	A character vector (can be length one). Specifies the terms for which partial effects will be returned
...	Further arguments passed to seq_range.

Value

A tibble with 5 columns.

Examples

library(survival)
fit <- coxph(Surv(time, status) ~ pspline(karno) + pspline(age), data=veteran)
terms_df <- veteran %>% get_terms(fit, terms = c("karno", "age"))
head(terms_df)
#> # A tibble: 6 x 6
#>   term      x   eff    se ci_lower ci_upper
#>   <chr> <dbl> <dbl> <dbl>    <dbl>    <dbl>
#> 1 karno  10    1.90 0.884    0.127     3.66
#> 2 karno  10.9  1.89 0.822    0.243     3.53
#> 3 karno  11.8  1.88 0.763    0.353     3.41
#> 4 karno  12.7  1.87 0.707    0.455     3.28
#> 5 karno  13.6  1.86 0.654    0.549     3.16
#> 6 karno  14.5  1.84 0.604    0.634     3.05
tail(terms_df)
#> # A tibble: 6 x 6
#>   term      x    eff    se ci_lower ci_upper
#>   <chr> <dbl>  <dbl> <dbl>    <dbl>    <dbl>
#> 1 age    78.6 -0.166 0.712    -1.59     1.26
#> 2 age    79.1 -0.192 0.751    -1.69     1.31
#> 3 age    79.6 -0.219 0.793    -1.80     1.37
#> 4 age    80.1 -0.247 0.837    -1.92     1.43
#> 5 age    80.5 -0.275 0.884    -2.04     1.49
#> 6 age    81   -0.303 0.933    -2.17     1.56