Add the number of observations in a new column n_obs, taking into account any
weights if they have been defined.
Usage
tidy_add_n(x, model = tidy_get_model(x))Details
For continuous variables, it corresponds to all valid observations contributing to the model.
For categorical variables coded with treatment or sum contrasts,
each model term could be associated to only one level of the original
categorical variable. Therefore, n_obs will correspond to the number of
observations associated with that level. n_obs will also be computed for
reference rows. For polynomial contrasts (defined with stats::contr.poly()),
all levels will contribute to the computation of each model term. Therefore,
n_obs will be equal to the total number of observations. For Helmert and custom
contrasts, only rows contributing positively (i.e. with a positive contrast)
to the computation of a term will be considered for estimating n_obs. The
result could therefore be difficult to interpret. For a better understanding
of which observations are taken into account to compute n_obs values, you
could look at model_compute_terms_contributions().
For interaction terms, only rows contributing to all the terms of the
interaction will be considered to compute n_obs.
For binomial logistic models, tidy_add_n() will also return the
corresponding number of events (n_event) for each term, taking into account
any defined weights. Observed proportions could be obtained as n_obs / n_event.
Similarly, a number of events will be computed for multinomial logistic
models (nnet::multinom()) for each level of the outcome (y.level),
corresponding to the number of observations equal to that outcome level.
For Poisson models, n_event will be equal to the number of counts per term.
In addition, a third column exposure will be computed. If no offset is
defined, exposure is assumed to be equal to 1 (eventually multiplied by
weights) per observation. If an offset is defined, exposure will be equal
to the (weighted) sum of the exponential of the offset (as a reminder, to
model the effect of x on the ratio y / z, a Poisson model will be defined
as glm(y ~ x + offset(log(z)), family = poisson)). Observed rates could be
obtained with n_event / exposure.
For Cox models (survival::coxph()), an individual could be coded
with several observations (several rows). n_obs will correspond to the
weighted number of observations which could be different from the number of
individuals n_ind. tidy_add_n() will also compute a (weighted) number of
events (n_event) according to the definition of the survival::Surv()
object.
Exposure time is also returned in exposure column. It is equal to the
(weighted) sum of the time variable if only one variable time is passed to
survival::Surv(), and to the (weighted) sum of time2 - time if two time
variables are defined in survival::Surv().
For competing risk regression models (tidycmprsk::crr()), n_event takes
into account only the event of interest defined by failcode.
The (weighted) total number of observations (N_obs), of individuals
(N_ind), of events (N_event) and of exposure time (Exposure) are
stored as attributes of the returned tibble.
See also
Other tidy_helpers:
tidy_add_coefficients_type(),
tidy_add_contrasts(),
tidy_add_estimate_to_reference_rows(),
tidy_add_header_rows(),
tidy_add_pairwise_contrasts(),
tidy_add_reference_rows(),
tidy_add_term_labels(),
tidy_add_variable_labels(),
tidy_attach_model(),
tidy_disambiguate_terms(),
tidy_group_by(),
tidy_identify_variables(),
tidy_plus_plus(),
tidy_remove_intercept(),
tidy_select_variables()
Examples
# \donttest{
lm(Petal.Length ~ ., data = iris) |>
tidy_and_attach() |>
tidy_add_n()
#> # A tibble: 6 × 8
#> term n_obs estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 150 -1.11 0.270 -4.12 6.45e- 5 -1.64 -0.578
#> 2 Sepal.Length 150 0.608 0.0502 12.1 1.07e-23 0.509 0.707
#> 3 Sepal.Width 150 -0.181 0.0804 -2.25 2.62e- 2 -0.339 -0.0217
#> 4 Petal.Width 150 0.602 0.121 4.96 1.97e- 6 0.362 0.842
#> 5 Speciesversico… 50 1.46 0.173 8.44 3.14e-14 1.12 1.81
#> 6 Speciesvirgini… 50 1.97 0.245 8.06 2.60e-13 1.49 2.46
lm(Petal.Length ~ ., data = iris, contrasts = list(Species = contr.sum)) |>
tidy_and_attach() |>
tidy_add_n()
#> # A tibble: 6 × 8
#> term n_obs estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 150 0.0349 0.259 0.135 8.93e- 1 -0.477 0.546
#> 2 Sepal.Length 150 0.608 0.0502 12.1 1.07e-23 0.509 0.707
#> 3 Sepal.Width 150 -0.181 0.0804 -2.25 2.62e- 2 -0.339 -0.0217
#> 4 Petal.Width 150 0.602 0.121 4.96 1.97e- 6 0.362 0.842
#> 5 Species1 50 -1.15 0.138 -8.31 6.37e-14 -1.42 -0.873
#> 6 Species2 50 0.318 0.0451 7.04 7.19e-11 0.228 0.407
lm(Petal.Length ~ ., data = iris, contrasts = list(Species = contr.poly)) |>
tidy_and_attach() |>
tidy_add_n()
#> # A tibble: 6 × 8
#> term n_obs estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 150 0.0349 0.259 0.135 8.93e- 1 -0.477 0.546
#> 2 Sepal.Length 150 0.608 0.0502 12.1 1.07e-23 0.509 0.707
#> 3 Sepal.Width 150 -0.181 0.0804 -2.25 2.62e- 2 -0.339 -0.0217
#> 4 Petal.Width 150 0.602 0.121 4.96 1.97e- 6 0.362 0.842
#> 5 Species.L 150 1.40 0.173 8.06 2.60e-13 1.05 1.74
#> 6 Species.Q 150 -0.389 0.0552 -7.04 7.19e-11 -0.498 -0.280
lm(Petal.Length ~ poly(Sepal.Length, 2), data = iris) |>
tidy_and_attach() |>
tidy_add_n()
#> # A tibble: 3 × 8
#> term n_obs estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 150 3.76 0.0685 54.9 7.97e-100 3.62 3.89
#> 2 poly(Sepal.Le… 150 18.8 0.839 22.4 3.01e- 49 17.1 20.4
#> 3 poly(Sepal.Le… 150 -2.85 0.839 -3.39 8.87e- 4 -4.50 -1.19
df <- Titanic |>
dplyr::as_tibble() |>
dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
glm(
Survived ~ Class + Age + Sex,
data = df, weights = df$n, family = binomial,
contrasts = list(Age = contr.sum, Class = "contr.helmert")
) |>
tidy_and_attach() |>
tidy_add_n()
#> # A tibble: 6 × 9
#> term n_obs n_event estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Inter… 2201 711 1.66 0.162 10.2 1.52e-24 1.35 1.99
#> 2 Class1 285 118 -0.509 0.0980 -5.19 2.05e- 7 -0.703 -0.318
#> 3 Class2 706 178 -0.423 0.0479 -8.83 1.09e-18 -0.518 -0.330
#> 4 Class3 885 212 0.0186 0.0303 0.613 5.40e- 1 -0.0406 0.0782
#> 5 Age1 2092 654 -0.531 0.122 -4.35 1.36e- 5 -0.771 -0.292
#> 6 SexMale 1731 367 -2.42 0.140 -17.2 1.43e-66 -2.70 -2.15
glm(
Survived ~ Class * (Age:Sex),
data = df, weights = df$n, family = binomial,
contrasts = list(Age = contr.sum, Class = "contr.helmert")
) |>
tidy_and_attach() |>
tidy_add_n()
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: collapsing to unique 'x' values
#> Warning: collapsing to unique 'x' values
#> Warning: collapsing to unique 'x' values
#> Warning: collapsing to unique 'x' values
#> # A tibble: 20 × 9
#> term n_obs n_event estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Inter… 2201 711 1.03e+1 294. 3.52e-2 0.972 -1.14e+1 NA
#> 2 Class1 285 118 3.33e-1 440. 7.57e-4 0.999 -9.87e+0 11.3
#> 3 Class2 706 178 -5.62e+0 147. -3.83e-2 0.969 NA 5.22
#> 4 Class3 885 212 8.48e-2 0.0354 2.40e+0 0.0166 1.67e-2 0.156
#> 5 AgeAdu… 425 316 -8.56e+0 294. -2.92e-2 0.977 NA 30.7
#> 6 AgeChi… 45 28 -7.90e-3 461. -1.71e-5 1.00 -6.52e+0 5.51
#> 7 AgeAdu… 1667 338 -1.18e+1 294. -4.03e-2 0.968 NA 9.55
#> 8 AgeChi… 64 29 NA NA NA NA NA NA
#> 9 Class1… 93 80 -1.20e+0 440. -2.73e-3 0.998 -1.73e+2 6.47
#> 10 Class2… 165 76 4.67e+0 147. 3.18e-2 0.975 -1.15e+1 NA
#> 11 Class3… 23 20 -4.50e-2 0.167 -2.70e-1 0.787 -3.43e-1 0.333
#> 12 Class1… 13 13 5.60e-1 692. 8.10e-4 0.999 -1.72e+1 16.7
#> 13 Class2… 31 14 4.02e-1 231. 1.74e-3 0.999 -3.97e+0 5.57
#> 14 Class3… 0 0 NA NA NA NA NA NA
#> 15 Class1… 168 14 -1.17e+0 440. -2.65e-3 0.998 -9.86e+1 1.62
#> 16 Class2… 462 75 5.59e+0 147. 3.81e-2 0.970 -8.68e+0 NA
#> 17 Class3… 862 192 NA NA NA NA NA NA
#> 18 Class1… 11 11 NA NA NA NA NA NA
#> 19 Class2… 48 13 NA NA NA NA NA NA
#> 20 Class3… 0 0 NA NA NA NA NA NA
glm(response ~ age + grade * trt, gtsummary::trial, family = poisson) |>
tidy_and_attach() |>
tidy_add_n()
#> # A tibble: 7 × 10
#> term n_obs n_event exposure estimate std.error statistic p.value conf.low
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Interce… 183 58 183 -1.95 0.581 -3.36 7.89e-4 -3.15
#> 2 age 183 58 183 0.0113 0.00947 1.19 2.34e-1 -0.00719
#> 3 gradeII 58 17 58 0.00792 0.518 0.0153 9.88e-1 -1.04
#> 4 gradeIII 60 20 60 0.553 0.458 1.21 2.27e-1 -0.333
#> 5 trtDrug B 94 31 94 0.511 0.449 1.14 2.55e-1 -0.353
#> 6 gradeII:… 29 10 29 -0.201 0.668 -0.301 7.64e-1 -1.52
#> 7 gradeIII… 33 8 33 -1.06 0.642 -1.65 9.85e-2 -2.36
#> # ℹ 1 more variable: conf.high <dbl>
glm(
response ~ trt * grade + offset(log(ttdeath)),
gtsummary::trial,
family = poisson
) |>
tidy_and_attach() |>
tidy_add_n()
#> # A tibble: 6 × 10
#> term n_obs n_event exposure estimate std.error statistic p.value conf.low
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Interc… 193 61 3795. -4.52 0.354 -12.8 2.01e-37 -5.30
#> 2 trtDrug… 98 33 1853. 0.576 0.449 1.28 2.00e- 1 -0.288
#> 3 gradeII 63 19 1207. 0.0184 0.518 0.0355 9.72e- 1 -1.03
#> 4 gradeIII 63 21 1183. 0.728 0.449 1.62 1.05e- 1 -0.136
#> 5 trtDrug… 33 12 576. 0.0548 0.654 0.0838 9.33e- 1 -1.24
#> 6 trtDrug… 33 8 607. -1.11 0.635 -1.75 8.00e- 2 -2.40
#> # ℹ 1 more variable: conf.high <dbl>
# }