Create an lm stat object. — stat

This function creates a lm stat object which can be passed as input to the set_stats() function when building an aba model. This stat performs a traditional linear regression analysis using the lm function. Coefficients will be presented as beta coefficients. Default metrics include adjusted R2.

Usage

stat_lm(std.beta = FALSE, complete.cases = TRUE, poly = NULL)

Arguments

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.
poly: numeric or list. Whether to use polynomial regression. Supplying a single number will call poly(..., #NUMBER#, raw=TRUE) on every covariate and predictor. Supplying a list allows you to perform a polynomial expansion on specific variables. NULL means no polynomials.

Value

An abaStat object with lm stat type.

Examples

data <- adnimerge %>% dplyr::filter(VISCODE == 'bl')

# fit lm model with continuous outcome variables
model <- data %>% aba_model() %>%
  set_groups(
    everyone(),
    DX_bl %in% c('MCI', 'AD')
  ) %>%
  set_outcomes(CDRSB_bl, MMSE_bl) %>%
  set_predictors(
    PLASMA_ABETA_bl, PLASMA_PTAU181_bl, PLASMA_NFL_bl,
    c(PLASMA_ABETA_bl, PLASMA_PTAU181_bl, PLASMA_NFL_bl)
  ) %>%
  set_covariates(AGE, GENDER, EDUCATION) %>%
  set_stats(
    stat_lm(std.beta = TRUE)
  ) %>%
  fit()
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_PTAU181_bl"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_NFL_bl"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl + PLASMA_PTAU181_bl + PLASMA_NFL_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_PTAU181_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_NFL_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl + PLASMA_PTAU181_bl + PLASMA_NFL_bl"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_PTAU181_bl"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_NFL_bl"
#> [1] "CDRSB_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl + PLASMA_PTAU181_bl + PLASMA_NFL_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_PTAU181_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_NFL_bl"
#> [1] "MMSE_bl ~ AGE + GENDER + EDUCATION + PLASMA_ABETA_bl + PLASMA_PTAU181_bl + PLASMA_NFL_bl"

# summarise model
model_summary <- model %>% summary()

# plot results
fig1 <- model_summary %>% aba_plot_coef()
fig2 <- model_summary %>% aba_plot_metric()

## polynomial regression ##
model <- data %>%
  aba_model() %>%
  set_outcomes(MRI_HIPP_bl) %>%
  set_covariates(
    c('AGE', 'EDUCATION')
  ) %>%
  set_stats(
    stat_lm(),
    stat_lm(poly = list(AGE=1)),
    stat_lm(poly = 2),
    stat_lm(poly = list(AGE = 2))
  ) %>%
  fit()
#> [1] "MRI_HIPP_bl ~ AGE + EDUCATION"
#> [1] "MRI_HIPP_bl ~ stats::poly(AGE, 1, raw = TRUE) + EDUCATION"
#> [1] "MRI_HIPP_bl ~ stats::poly(AGE, 2, raw = TRUE) + stats::poly(EDUCATION, 2, raw = TRUE)"
#> [1] "MRI_HIPP_bl ~ stats::poly(AGE, 2, raw = TRUE) + EDUCATION"

model_summary <- model %>% summary()