R/ff_permute.R
ff_permute.RdPermuate explanatory variables to produce multiple output tables for common regression models
ff_permute(
.data,
dependent = NULL,
explanatory_base = NULL,
explanatory_permute = NULL,
multiple_tables = FALSE,
include_base_model = TRUE,
include_full_model = TRUE,
base_on_top = TRUE,
...
)
finalfit_permute(
.data,
dependent = NULL,
explanatory_base = NULL,
explanatory_permute = NULL,
multiple_tables = FALSE,
include_base_model = TRUE,
include_full_model = TRUE,
base_on_top = TRUE,
...
)Data frame or tibble.
Character vector of length 1: quoted name of dependent
variable. Can be continuous, a binary factor, or a survival object of form
Surv(time, status).
Character vector of any length: quoted name(s) of base model explanatory variables.
Character vector of any length: quoted name(s) of explanatory variables to permute through models.
Logical. Multiple model tables as a list, or a single table including multiple models.
Logical. Include model using explanatory_base
variables only.
Logical. Include model using all explanatory_base
and explanatory_permute variables.
Logical. Base variables at top of table, or bottom of table.
Other arguments to finalfit
Returns a list of data frame with the final model table.
explanatory_base = c("age.factor", "sex.factor")
explanatory_permute = c("obstruct.factor", "perfor.factor", "node4.factor")
# Linear regression
colon_s %>%
finalfit_permute("nodes", explanatory_base, explanatory_permute)
#> Note: dependent includes missing data. These are dropped.
#> Note: dependent includes missing data. These are dropped.
#> Note: dependent includes missing data. These are dropped.
#> Note: dependent includes missing data. These are dropped.
#> Note: dependent includes missing data. These are dropped.
#> Note: dependent includes missing data. These are dropped.
#> Dependent: nodes unit value
#> Age <40 years Mean (sd) 4.7 (4.5)
#> 40-59 years Mean (sd) 3.6 (3.3)
#> 60+ years Mean (sd) 3.6 (3.6)
#> Sex Female Mean (sd) 3.7 (3.6)
#> Male Mean (sd) 3.6 (3.6)
#> Obstruction No Mean (sd) 3.7 (3.7)
#> Yes Mean (sd) 3.5 (3.2)
#> Perforation No Mean (sd) 3.7 (3.6)
#> Yes Mean (sd) 3.9 (2.8)
#> >4 positive nodes No Mean (sd) 2.0 (1.1)
#> Yes Mean (sd) 8.2 (4.1)
#> Coefficient (univariable) Coefficient (multivariable) 1
#> - -
#> -1.14 (-2.08 to -0.21, p=0.016) -1.14 (-2.07 to -0.20, p=0.017)
#> -1.19 (-2.10 to -0.28, p=0.010) -1.18 (-2.09 to -0.27, p=0.011)
#> - -
#> -0.14 (-0.60 to 0.33, p=0.565) -0.12 (-0.58 to 0.35, p=0.626)
#> - -
#> -0.24 (-0.83 to 0.36, p=0.435) -
#> - -
#> 0.24 (-1.13 to 1.61, p=0.735) -
#> - -
#> 6.19 (5.84 to 6.53, p<0.001) -
#> Coefficient (multivariable) 2 Coefficient (multivariable) 3
#> - -
#> -1.21 (-2.16 to -0.26, p=0.012) -1.14 (-2.07 to -0.20, p=0.017)
#> -1.25 (-2.17 to -0.33, p=0.008) -1.18 (-2.09 to -0.27, p=0.011)
#> - -
#> -0.07 (-0.54 to 0.40, p=0.779) -0.12 (-0.58 to 0.35, p=0.627)
#> - -
#> -0.30 (-0.90 to 0.30, p=0.327) -
#> - -
#> - 0.24 (-1.13 to 1.60, p=0.736)
#> - -
#> - -
#> Coefficient (multivariable) 4 Coefficient (multivariable) 5
#> - -
#> -0.63 (-1.24 to -0.02, p=0.042) -0.64 (-1.26 to -0.02, p=0.043)
#> -0.47 (-1.06 to 0.13, p=0.123) -0.48 (-1.08 to 0.12, p=0.119)
#> - -
#> 0.10 (-0.20 to 0.40, p=0.527) 0.13 (-0.17 to 0.44, p=0.391)
#> - -
#> - -0.13 (-0.53 to 0.26, p=0.500)
#> - -
#> - 0.50 (-0.40 to 1.39, p=0.277)
#> - -
#> 6.18 (5.84 to 6.52, p<0.001) 6.19 (5.84 to 6.54, p<0.001)
# Cox proportional hazards regression
colon_s %>%
finalfit_permute("Surv(time, status)", explanatory_base, explanatory_permute)
#> Dependent: Surv(time, status) all HR (univariable)
#> Age <40 years 70 (7.5) -
#> 40-59 years 344 (37.0) 0.76 (0.53-1.09, p=0.132)
#> 60+ years 515 (55.4) 0.93 (0.66-1.31, p=0.668)
#> Sex Female 445 (47.9) -
#> Male 484 (52.1) 1.01 (0.84-1.22, p=0.888)
#> Obstruction No 732 (80.6) -
#> Yes 176 (19.4) 1.29 (1.03-1.62, p=0.028)
#> Perforation No 902 (97.1) -
#> Yes 27 (2.9) 1.17 (0.70-1.95, p=0.556)
#> >4 positive nodes No 674 (72.6) -
#> Yes 255 (27.4) 2.60 (2.15-3.14, p<0.001)
#> HR (multivariable) 1 HR (multivariable) 2 HR (multivariable) 3
#> - - -
#> 0.76 (0.53-1.08, p=0.129) 0.79 (0.55-1.13, p=0.198) 0.76 (0.53-1.08, p=0.127)
#> 0.93 (0.66-1.31, p=0.660) 0.98 (0.69-1.40, p=0.931) 0.92 (0.65-1.31, p=0.656)
#> - - -
#> 1.02 (0.85-1.23, p=0.847) 1.02 (0.85-1.24, p=0.803) 1.02 (0.85-1.22, p=0.854)
#> - - -
#> - 1.31 (1.04-1.64, p=0.022) -
#> - - -
#> - - 1.18 (0.70-1.97, p=0.535)
#> - - -
#> - - -
#> HR (multivariable) 4 HR (multivariable) 5
#> - -
#> 0.85 (0.59-1.22, p=0.379) 0.90 (0.63-1.30, p=0.590)
#> 1.09 (0.77-1.55, p=0.615) 1.19 (0.83-1.69, p=0.346)
#> - -
#> 1.04 (0.87-1.26, p=0.647) 1.05 (0.87-1.27, p=0.597)
#> - -
#> - 1.35 (1.07-1.70, p=0.011)
#> - -
#> - 1.16 (0.69-1.94, p=0.581)
#> - -
#> 2.64 (2.18-3.19, p<0.001) 2.68 (2.21-3.26, p<0.001)
# Logistic regression
# colon_s %>%
# finalfit_permute("mort_5yr", explanatory_base, explanatory_permute)
# Logistic regression with random effect (glmer)
# colon_s %>%
# finalfit_permute("mort_5yr", explanatory_base, explanatory_permute,
# random_effect = "hospital")