Description Usage Arguments Value Examples

This function creates a glm stat object which can be passed as input
to the `set_stats()`

function when building an aba model. This stat performs
a traditional logistic regression analysis using the `glm`

function with
a binary outcome. Coefficients will be presented as odds ratios. Default
metrics include AUC.

1 | ```
stat_glm(std.beta = FALSE, complete.cases = TRUE)
``` |

`std.beta` |
logical. Whether to standardize model predictors and covariates prior to analysis. |

`complete.cases` |
logical. Whether to only include the subset of data with no missing data for any of the outcomes, predictors, or covariates. Note that complete cases are considering within each group - outcome combination but across all predictor sets. |

An abaStat object with `glm`

stat type.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
data <- adnimerge %>% dplyr::filter(VISCODE == 'bl')
# fit glm model with binary outcome variables
model <- data %>% aba_model() %>%
set_groups(everyone()) %>%
set_outcomes(ConvertedToAlzheimers, CSF_ABETA_STATUS_bl) %>%
set_predictors(
PLASMA_PTAU181_bl, PLASMA_NFL_bl,
c(PLASMA_PTAU181_bl, PLASMA_NFL_bl)
) %>%
set_stats(
stat_glm(std.beta = TRUE)
) %>%
fit()
# summarise glm model
model_summary <- model %>% summary()
# plot glm results
fig1 <- model_summary %>% aba_plot_coef()
fig2 <- model_summary %>% aba_plot_metric()
fig3 <- model_summary %>% aba_plot_roc()
``` |

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