Description Usage Arguments Details Value Examples

View source: R/customizable_functions.R

Fits a specified model to each of the simulated datasets and returns a dataframe
summarizing results from fitting the model to each dataset, including the estimated
effect and the estimated standard error for that estimated effect. The model is specified
through a user-created R function, which must take specific input and return
output in a specific format. For more details, see the parameter definitions,
the Details section, and the vignette for the `eesim`

package.

1 |

`data` |
A list of simulated data sets. Each simulated dataset must include a
column called |

`custom_model` |
The object name of an R function that defines the code that will be used to fit the model. This object name should not be in quotations. See Details for more. |

`custom_model_args` |
A list of arguments and their values for a custom
model. These arguments are passed through to the function specified with |

The function specified by the `custom_model`

argument should be
a user-created function that inputs a data frame with columns named "x" for
exposure values and "outcome" for outcome values. The function must output a data
frame with columns called `Estimate`

, `Std. Error`

, `t value`

,
`Pr(>|t|)`

, `2.5%`

, and `97.5%`

. Note that these columns are the output
from `summary`

and `confint`

for models fit using a `glm`

call. You may
use the function `format_out`

from eesim within your function to produce output
with these columns if this model is fit using `glm`

or something similar.
For more details and examples, see the vignette for `eesim`

.

A data frame in which each row gives the results from the model-fitting function run
on one of the simulated datasets input to the function as the `data`

object. The returned
data frame has one row per simulated dataset and the following columns:

`Estimate`

: The estimated*β*(log relative risk) as estimated by the model specified with`custom_model`

.`Std.Error`

: The standard error for the estimated*β*.`t.value`

: The test statistic for a test of the null hypothesis*β = 0*.`p.value`

: The p-value for a test of the null hypothesis*β = 0*.`lower_ci`

: The lower value in the 95% confidence interval estimated for*β*.`upper_ci`

: The upper value in the 95% confidence interval estimated for*β*.

1 2 3 4 5 6 7 8 9 | ```
# Create a set of simulated datasets and then fit the model defined in `spline_mod` to
# all datasets, using the argument `df_year = 7` in the call to `spline_mod`. The `spline_mod`
# function is included in the `eesim` package and can be investigating by calling the function
# name without parentheses (i.e., `spline_mod`).
sims <- create_sims(n_reps = 10, n = 5 * 365, central = 100, sd = 10,
exposure_type = "continuous", exposure_trend = "cos1",
exposure_amp = .6, average_outcome = 22,
outcome_trend = "no trend", outcome_amp = .6, rr = 1.01)
fit_mods(data = sims, custom_model = spline_mod, custom_model_args = list(df_year = 7))
``` |

eesim documentation built on June 4, 2017, 1:03 a.m.

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