## allestimates:
Effect Estimates from All Possible Models

Calculates effect estimates from models with all possible
combinations of a list of variables specified by users. Effect estimates
can be regression coefficients, odds ratios and hazard ratios depending
on modeling methods. This approach can be used for assessing treatment
effects in clinical trials or the effects of a risk factor in
observational biomedical and epidemiological studies.

## Installation

You can install the released version of allestimates from CRAN with:

`install.packages("allestimates")`

## Example

Using `diab_df`

data to assess the association between
marital status `Married`

and and `Diabetes`

as an
example. Several other factors might potentially influence the
association (odds ratio estimates) between `Married`

and
`Diabetes`

variables.

```
library(allestimates)
data(diab_df)
diab_df$Overweight = as.numeric(diab_df$BMI >= 25)
vlist <- c("Age", "Sex", "Education","Smoke", "BMI", "Income")
results <- all_speedglm(crude = "Diabetes ~ Married", xlist = vlist, data = diab_df)
#> estimate: Odds Ratio or Rate Ratio
#> Crude model: Diabetes ~ Married
```

All those possible effect estimates (odds ratios in this case) are
stored in an object `results`

and can be used later for
further analysis and generating various graphs.

From
this `all_plot`

graph, we can see that all estimated odds
ratio values fell in the left-side two quarters, with either a positive
or a negative association but p values in all possible models were
greater than 0.05.

`all_plot2`

graph shows effect estimates with a specific
variable being included or not included in the model. This can be
helpful in combination with biological background knowledge to
understand potential confounding effects, uncertainly of the effect
estimates, and inappropriate inclusion of some variables.

*Note*: Interaction terms and other derived
variables can be listed. Those terms need to be generated first before
running an `allestimates`

function.