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Tidying Methods for mmrm Objects

Source: R/tidiers.R
mmrm_tidiers.Rd

[Experimental]

These methods tidy the estimates from an mmrm object into a summary.

Usage 

# S3 method for mmrm
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for mmrm
glance(x, ...)

# S3 method for mmrm
augment(
  x,
  newdata = NULL,
  interval = c("none", "confidence", "prediction"),
  se_fit = (interval != "none"),
  type.residuals = c("response", "pearson", "normalized"),
  ...
)

Arguments

x

(mmrm)
fitted model.

conf.int

(flag)
if TRUE columns for the lower (conf.low) and upper bounds (conf.high) of coefficient estimates are included.

conf.level

(number)
defines the range of the optional confidence internal.

...

only used by augment() to pass arguments to the predict.mmrm_tmb() method.

newdata

(data.frame or NULL)
optional new data frame.

interval

(string)
type of interval calculation.

se_fit

(flag)
whether to return standard errors of fit.

type.residuals

(string)
passed on to residuals.mmrm_tmb().

Functions

  • tidy(mmrm): derives tidy tibble from an mmrm object.

  • glance(mmrm): derives glance tibble from an mmrm object.

  • augment(mmrm): derives augment tibble from an mmrm object.

See also

mmrm_methods, mmrm_tmb_methods for additional methods.

Examples 

fit <- mmrm(
  formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)
# Applying tidy method to return summary table of covariate estimates.
fit |> tidy()
#> # A tibble: 11 × 6
#>    term                          estimate std.error    df statistic  p.value
#>    <chr>                            <dbl>     <dbl> <dbl>     <dbl>    <dbl>
#>  1 (Intercept)                    30.8        0.887  219.   34.7    5.96e-91
#>  2 RACEBlack or African American   1.53       0.624  169.    2.45   1.53e- 2
#>  3 RACEWhite                       5.64       0.666  157.    8.48   1.56e-14
#>  4 SEXFemale                       0.326      0.532  166.    0.613  5.41e- 1
#>  5 ARMCDTRT                        3.77       1.07   146.    3.51   5.89e- 4
#>  6 AVISITVIS2                      4.84       0.802  144.    6.04   1.27e- 8
#>  7 AVISITVIS3                     10.3        0.823  156.   12.6    1.86e-25
#>  8 AVISITVIS4                     15.1        1.31   138.   11.5    8.11e-22
#>  9 ARMCDTRT:AVISITVIS2            -0.0419     1.13   139.   -0.0371 9.70e- 1
#> 10 ARMCDTRT:AVISITVIS3            -0.694      1.19   158.   -0.584  5.60e- 1
#> 11 ARMCDTRT:AVISITVIS4             0.624      1.85   130.    0.337  7.36e- 1
fit |> tidy(conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 11 × 8
#>    term           estimate std.error    df statistic  p.value conf.low conf.high
#>    <chr>             <dbl>     <dbl> <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#>  1 (Intercept)     30.8        0.887  219.   34.7    5.96e-91   29.3       32.2 
#>  2 ARMCDTRT         3.77       1.07   146.    3.51   5.89e- 4    2.00       5.55
#>  3 ARMCDTRT:AVIS…  -0.0419     1.13   139.   -0.0371 9.70e- 1   -1.91       1.83
#>  4 ARMCDTRT:AVIS…  -0.694      1.19   158.   -0.584  5.60e- 1   -2.66       1.27
#>  5 ARMCDTRT:AVIS…   0.624      1.85   130.    0.337  7.36e- 1   -2.44       3.69
#>  6 AVISITVIS2       4.84       0.802  144.    6.04   1.27e- 8    3.51       6.17
#>  7 AVISITVIS3      10.3        0.823  156.   12.6    1.86e-25    8.98      11.7 
#>  8 AVISITVIS4      15.1        1.31   138.   11.5    8.11e-22   12.9       17.2 
#>  9 RACEBlack or …   1.53       0.624  169.    2.45   1.53e- 2    0.498      2.56
#> 10 RACEWhite        5.64       0.666  157.    8.48   1.56e-14    4.54       6.74
#> 11 SEXFemale        0.326      0.532  166.    0.613  5.41e- 1   -0.554      1.21
# Applying glance method to return summary table of goodness of fit statistics.
fit |> glance()
#> # A tibble: 1 × 4
#>     AIC   BIC logLik deviance
#>   <dbl> <dbl>  <dbl>    <dbl>
#> 1 3406. 3439. -1693.    3386.
# Applying augment method to return merged `tibble` of model data, fitted and residuals.
fit |> augment()
#> # A tibble: 537 × 9
#>    .rownames  FEV1 RACE               SEX   ARMCD AVISIT USUBJID .fitted  .resid
#>        <dbl> <dbl> <fct>              <fct> <fct> <fct>  <fct>     <dbl>   <dbl>
#>  1         2  40.0 Black or African … Fema… TRT   VIS2   PT1        40.0  -1.23 
#>  2         4  20.5 Black or African … Fema… TRT   VIS4   PT1        20.5 -31.6  
#>  3         6  31.5 Asian              Male  PBO   VIS2   PT2        31.5  -4.16 
#>  4         7  36.9 Asian              Male  PBO   VIS3   PT2        36.9  -4.24 
#>  5         8  48.8 Asian              Male  PBO   VIS4   PT2        48.8   2.98 
#>  6        10  36.0 Black or African … Fema… PBO   VIS2   PT3        36.0  -1.49 
#>  7        12  37.2 Black or African … Fema… PBO   VIS4   PT3        37.2 -10.5  
#>  8        13  33.9 Asian              Fema… TRT   VIS1   PT4        33.9  -0.985
#>  9        14  33.7 Asian              Fema… TRT   VIS2   PT4        33.7  -5.93 
#> 10        16  54.5 Asian              Fema… TRT   VIS4   PT4        54.5   3.89 
#> # ℹ 527 more rows
fit |> augment(interval = "confidence")
#> # A tibble: 537 × 12
#>    .rownames  FEV1 RACE         SEX   ARMCD AVISIT USUBJID .fitted .lower .upper
#>        <dbl> <dbl> <fct>        <fct> <fct> <fct>  <fct>     <dbl>  <dbl>  <dbl>
#>  1         2  40.0 Black or Af… Fema… TRT   VIS2   PT1        40.0   40.0   40.0
#>  2         4  20.5 Black or Af… Fema… TRT   VIS4   PT1        20.5   20.5   20.5
#>  3         6  31.5 Asian        Male  PBO   VIS2   PT2        31.5   31.5   31.5
#>  4         7  36.9 Asian        Male  PBO   VIS3   PT2        36.9   36.9   36.9
#>  5         8  48.8 Asian        Male  PBO   VIS4   PT2        48.8   48.8   48.8
#>  6        10  36.0 Black or Af… Fema… PBO   VIS2   PT3        36.0   36.0   36.0
#>  7        12  37.2 Black or Af… Fema… PBO   VIS4   PT3        37.2   37.2   37.2
#>  8        13  33.9 Asian        Fema… TRT   VIS1   PT4        33.9   33.9   33.9
#>  9        14  33.7 Asian        Fema… TRT   VIS2   PT4        33.7   33.7   33.7
#> 10        16  54.5 Asian        Fema… TRT   VIS4   PT4        54.5   54.5   54.5
#> # ℹ 527 more rows
#> # ℹ 2 more variables: .se.fit <dbl>, .resid <dbl>
fit |> augment(type.residuals = "pearson")
#> # A tibble: 537 × 9
#>    .rownames  FEV1 RACE                SEX   ARMCD AVISIT USUBJID .fitted .resid
#>        <dbl> <dbl> <fct>               <fct> <fct> <fct>  <fct>     <dbl>  <dbl>
#>  1         2  40.0 Black or African A… Fema… TRT   VIS2   PT1        40.0 -0.240
#>  2         4  20.5 Black or African A… Fema… TRT   VIS4   PT1        20.5 -3.23 
#>  3         6  31.5 Asian               Male  PBO   VIS2   PT2        31.5 -0.807
#>  4         7  36.9 Asian               Male  PBO   VIS3   PT2        36.9 -1.10 
#>  5         8  48.8 Asian               Male  PBO   VIS4   PT2        48.8  0.305
#>  6        10  36.0 Black or African A… Fema… PBO   VIS2   PT3        36.0 -0.288
#>  7        12  37.2 Black or African A… Fema… PBO   VIS4   PT3        37.2 -1.08 
#>  8        13  33.9 Asian               Fema… TRT   VIS1   PT4        33.9 -0.155
#>  9        14  33.7 Asian               Fema… TRT   VIS2   PT4        33.7 -1.15 
#> 10        16  54.5 Asian               Fema… TRT   VIS4   PT4        54.5  0.398
#> # ℹ 527 more rows