or_gam: Calculate Odds Ratios of Generalized Additive (Mixed) Models

In oddsratio: Odds Ratio Calculation for GAM(M)s & GLM(M)s

Description

This function calculates odds ratio(s) for specific increment steps of GAM(M) models. Odds ratios can also be calculated for continuous (percentage) increment steps across the whole predictor distribution using slice = TRUE.

Usage

or_gam(
  data = NULL,
  model = NULL,
  pred = NULL,
  values = NULL,
  percentage = NULL,
  slice = FALSE,
  ci = NULL
)

Arguments

data	The data used for model fitting.
model	A fitted GAM(M).
pred	Predictor name for which to calculate the odds ratio.
values	Numeric vector of length two. Predictor values to estimate odds ratio from. Function is written to use the first provided value as the "lower" one, i.e. calculating the odds ratio 'from value1 to value2'. Only used if slice = FALSE.
percentage	Percentage number to split the predictor distribution into. A value of 10 would split the predictor distribution by 10\ Only needed if slice = TRUE.
slice	Whether to calculate odds ratios for fixed increment steps over the whole predictor distribution. See percentage for setting the increment values.
ci	Currently fixed to 95\ Currently supported functions: mgcv::gam, mgcv::gamm, gam::gam. For mgcv::gamm, the model input of or_gam needs to be the gam output (e.g. fit_gam$gam).

Value

A data.frame with (up to) eight columns. perc1 and perc2 are only returned if slice = TRUE:

predictor	Predictor name
value1	First value of odds ratio calculation
value2	Second value of odds ratio calculation
perc1	Percentage value of value1
perc2	Percentage value of value2
oddsratio	Calculated odds ratio(s)
ci_low	Lower (2.5%) confident interval of odds ratio
ci_high	Higher (97.5%) confident interval of odds ratio

See Also

or_glm() plot_gam() insert_or()

Examples

library(oddsratio)
library(mgcv)
fit_gam <- gam(y ~ s(x0) + s(I(x1^2)) + s(x2) +
  offset(x3) + x4, data = data_gam) # fit model

# Calculate OR for specific increment step of continuous variable
or_gam(
  data = data_gam, model = fit_gam, pred = "x2",
  values = c(0.099, 0.198)
)

## Calculate OR for change of indicator variable
or_gam(
  data = data_gam, model = fit_gam, pred = "x4",
  values = c("B", "D")
)

## Calculate ORs for percentage increments of predictor distribution
## (here: 20%)
or_gam(
  data = data_gam, model = fit_gam, pred = "x2",
  percentage = 20, slice = TRUE
)

Example output

Loading required package: nlme
This is mgcv 1.8-28. For overview type 'help("mgcv-package")'.
  predictor value1 value2 oddsratio CI_low (2.5%) CI_high (97.5%)
1        x2  0.099  0.198  23.32353      23.30424        23.34283
  predictor value1 value2 oddsratio CI_low (2.5%) CI_high (97.5%)
1        x4      B      D 0.4744264     0.4976375        0.452298
  predictor value1 value2 perc1 perc2 oddsratio CI_low (2.5%) CI_high (97.5%)
1        x2  0.001  0.200     0    20   2510.77       1091.68         5774.53
2        x2  0.200  0.400    20    40      0.03          0.03            0.03
3        x2  0.400  0.599    40    60      0.58          0.56            0.60
4        x2  0.599  0.799    60    80      0.06          0.06            0.06
5        x2  0.799  0.998    80   100      0.41          0.75            0.22

oddsratio documentation built on July 1, 2020, 10:22 p.m.