Generate common metrics for regression model results

ff_metrics(.data)

# S3 method for lm
ff_metrics(.data)

# S3 method for lmlist
ff_metrics(.data)

# S3 method for glm
ff_metrics(.data)

# S3 method for glmlist
ff_metrics(.data)

# S3 method for lmerMod
ff_metrics(.data)

# S3 method for glmerMod
ff_metrics(.data)

# S3 method for coxph
ff_metrics(.data)

# S3 method for coxphlist
ff_metrics(.data)

Arguments

.data

Model output.

Value

Model metrics vector for output.

Examples

library(finalfit) # glm fit = glm(mort_5yr ~ age.factor + sex.factor + obstruct.factor + perfor.factor, data=colon_s, family="binomial") fit %>% ff_metrics()
#> [1] "Number in dataframe = 929, Number in model = 894, Missing = 35, AIC = 1230.7, C-statistic = 0.56, H&L = Chi-sq(8) 5.69 (p=0.682)"
# glmlist explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "mort_5yr" colon_s %>% glmmulti(dependent, explanatory) %>% ff_metrics()
#> [1] "Number in dataframe = 929, Number in model = 894, Missing = 35, AIC = 1230.7, C-statistic = 0.56, H&L = Chi-sq(8) 5.69 (p=0.682)"
# glmerMod explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") random_effect = "hospital" dependent = "mort_5yr" colon_s %>% glmmixed(dependent, explanatory, random_effect) %>% ff_metrics()
#> [1] "Number in model = 894, Number of groups = 5, AIC = 933.8, C-statistic = 0.815"
# lm fit = lm(nodes ~ age.factor + sex.factor + obstruct.factor + perfor.factor, data=colon_s) fit %>% ff_metrics()
#> [1] "Number in dataframe = 929, Number in model = 890, Missing = 39, Log-likelihood = -2394.25, AIC = 4802.5, R-squared = 0.0092, Adjusted R-squared = 0.0036"
# lmerMod explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") random_effect = "hospital" dependent = "nodes" colon_s %>% lmmixed(dependent, explanatory, random_effect) %>% ff_metrics()
#> [1] "Number in model = 890, Number of groups = 5, Log likelihood = -2317.13, REML criterion = 4634.3"
# coxphlist explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "Surv(time, status)" colon_s %>% coxphmulti(dependent, explanatory) %>% ff_metrics()
#> [1] "Number in dataframe = 929, Number in model = 908, Missing = 21, Number of events = 441, Concordance = 0.543 (SE = 0.014), R-squared = 0.011( Max possible = 0.998), Likelihood ratio test = 9.862 (df = 5, p = 0.079)"
# coxph fit = survival::coxph(survival::Surv(time, status) ~ age.factor + sex.factor + obstruct.factor + perfor.factor, data = colon_s) fit %>% ff_metrics()
#> [1] "Number in dataframe = 929, Number in model = 908, Missing = 21, Number of events = 441, Concordance = 0.543 (SE = 0.014), R-squared = 0.011( Max possible = 0.998), Likelihood ratio test = 9.862 (df = 5, p = 0.079)"