---
title: "The modelsum function"
author: "Beth Atkinson, Ethan Heinzen, Pat Votruba, Jason Sinnwell, Shannon McDonnell and Greg Dougherty"
output:
  rmarkdown::html_vignette:
    toc: yes
    toc_depth: 3
vignette: |
  %\VignetteIndexEntry{The modelsum function}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r, echo=FALSE, message=FALSE, results='hide', warning=FALSE}
require(knitr)
require(broom)
require(MASS)
require(pROC)
require(rpart)
 
opts_chunk$set(comment = NA, echo=TRUE, prompt=TRUE, collapse=TRUE)

```


# Introduction

Very often we are asked to summarize model results from multiple fits into a nice table.
The endpoint might be of different types (e.g., survival, case/control, continuous) and there
may be several independent variables that we want to examine univariately or adjusted for certain
variables such as age and sex. Locally at Mayo, the SAS macros `%modelsum`, `%glmuniv`, and `%logisuni`
were written to create such summary tables. With the increasing interest in R, we have developed the
function `modelsum` to create similar tables within the R environment.  

In developing the `modelsum` function, the goal was to bring the best features of these macros into an R function.
However, the task was not simply to duplicate all the functionality, but rather to make use of R's strengths
(modeling, method dispersion, flexibility in function definition and output format) and make a tool that fits
the needs of R users.  Additionally, the results needed to fit within the general reproducible research framework
so the tables could be displayed within an R markdown report.

This report provides step-by-step directions for using the functions associated with `modelsum`.
All functions presented here are available within the `arsenal` package.  An assumption is made that users
are somewhat familiar with R markdown documents.  For those who are new to the topic, a good initial
resource is available at [rmarkdown.rstudio.com](https://rmarkdown.rstudio.com/). 

# Simple Example

The first step when using the `modelsum` function is to load the `arsenal` package.  All the examples in this report
use a dataset called `mockstudy` made available by Paul Novotny which includes a variety of types of variables
(character, numeric, factor, ordered factor, survival) to use as examples.

```{r, load-data}
require(arsenal)
data(mockstudy) # load data
dim(mockstudy)  # look at how many subjects and variables are in the dataset 
# help(mockstudy) # learn more about the dataset and variables
str(mockstudy) # quick look at the data
```

To create a simple linear regression table (the default), use a formula statement to specify the variables
that you want summarized.  The example below predicts BMI with the variables sex and age. 

```{r simple1}
tab1 <- modelsum(bmi ~ sex + age, data=mockstudy)
```

If you want to take a quick look at the table, you can use `summary` on your modelsum object and the table will
print out as text in your R console window.  If you use `summary` without any options you will see a number of
$\ $ statements which translates to "space" in HTML.

## Pretty text version of table

If you want a nicer version in your console window then adding the `text=TRUE` option.  

```{r simple-text}
summary(tab1, text=TRUE)
```

## Pretty Rmarkdown version of table

In order for the report to look nice within an R markdown (knitr) report, you just need to specify
`results="asis"` when creating the r chunk. This changes the layout slightly (compresses it) and bolds
the variable names.

```{r simple-markdown, results='asis'}
summary(tab1)
```

## Data frame version of table

If you want a data.frame version, simply use `as.data.frame`.  

```{r}
as.data.frame(tab1)
```

## Add an adjustor to the model

The argument `adjust` allows the user to indicate that all the variables should be adjusted for these terms. To adjust each model
for age and sex (for instance), we use `adjust = ~ age + sex`:

```{r adjust, results="asis"}
tab2 <- modelsum(alk.phos ~ arm + ps + hgb, adjust= ~age + sex, data=mockstudy)
summary(tab2)
```

# Models for each endpoint type

To make sure the correct model is run you need to specify "family".  The options available right
now are : gaussian, binomial, survival, and poisson.  If there is enough interest, additional models can be added.  

## Gaussian

### Fit and summarize linear regression model

Look at whether there is any evidence that AlkPhos values vary by study arm after adjusting for sex and age (assuming a linear age relationship).

```{r}
fit <- lm(alk.phos ~ arm + age + sex, data=mockstudy)
summary(fit)
plot(fit)
```

The results suggest that the endpoint may need to be transformed.  Calculating the Box-Cox transformation suggests a log transformation.

```{r}
require(MASS)
boxcox(fit)
```

```{r}
fit2 <- lm(log(alk.phos) ~ arm + age + sex, data=mockstudy)
summary(fit2)
plot(fit2)
```

Finally, look to see whether there there is a non-linear relationship with age.

```{r}
require(splines)
fit3 <- lm(log(alk.phos) ~ arm + ns(age, df=2) + sex, data=mockstudy)

# test whether there is a difference between models 
stats::anova(fit2,fit3)

# look at functional form of age
termplot(fit3, term=2, se=T, rug=T)
```

In this instance it looks like there isn't enough evidence to say that the relationship is non-linear.

### Extract data using the `broom` package

The `broom` package makes it easy to extract information from the fit.

```{r}
tmp <- tidy(fit3) # coefficients, p-values
class(tmp)
tmp

glance(fit3)
```

### Create a summary table using modelsum

```{r, results="asis"}
ms.logy <- modelsum(log(alk.phos) ~ arm + ps + hgb, data=mockstudy, adjust= ~age + sex, 
                    family=gaussian,  
                    gaussian.stats=c("estimate","CI.lower.estimate","CI.upper.estimate","p.value"))
summary(ms.logy)
```

## Binomial

### Fit and summarize logistic regression model

```{r}
boxplot(age ~ mdquality.s, data=mockstudy, ylab=attr(mockstudy$age,'label'), xlab='mdquality.s')

fit <- glm(mdquality.s ~ age + sex, data=mockstudy, family=binomial)
summary(fit)

# create Odd's ratio w/ confidence intervals
tmp <- data.frame(summary(fit)$coef)
tmp

tmp$OR <- round(exp(tmp[,1]),2)
tmp$lower.CI <- round(exp(tmp[,1] - 1.96* tmp[,2]),2)
tmp$upper.CI <- round(exp(tmp[,1] + 1.96* tmp[,2]),2)
names(tmp)[4] <- 'P-value'

kable(tmp[,c('OR','lower.CI','upper.CI','P-value')])

# Assess the predictive ability of the model

# code using the pROC package
require(pROC)
pred <- predict(fit, type='response')
tmp <- pROC::roc(mockstudy$mdquality.s[!is.na(mockstudy$mdquality.s)]~ pred, plot=TRUE, percent=TRUE)
tmp$auc

```

### Extract data using `broom` package

The `broom` package makes it easy to extract information from the fit.

```{r}
tidy(fit, exp=T, conf.int=T) # coefficients, p-values, conf.intervals

glance(fit) # model summary statistics
```

### Create a summary table using modelsum

```{r, results="asis"}
summary(modelsum(mdquality.s ~ age + bmi, data=mockstudy, adjust=~sex, family=binomial))

fitall <- modelsum(mdquality.s ~ age, data=mockstudy, family=binomial,
                   binomial.stats=c("Nmiss2","OR","p.value"))
summary(fitall)
```


## Survival

### Fit and summarize a Cox regression model 

```{r survival}
require(survival)

# multivariable model with all 3 terms
fit  <- coxph(Surv(fu.time, fu.stat) ~ age + sex + arm, data=mockstudy)
summary(fit)

# check proportional hazards assumption
fit.z <- cox.zph(fit)
fit.z
plot(fit.z[1], resid=FALSE) # makes for a cleaner picture in this case
abline(h=coef(fit)[1], col='red')

# check functional form for age using pspline (penalized spline)
# results are returned for the linear and non-linear components
fit2 <- coxph(Surv(fu.time, fu.stat) ~ pspline(age) + sex + arm, data=mockstudy)
fit2

# plot smoothed age to visualize why significant
termplot(fit2, se=T, terms=1)
abline(h=0)

# The c-statistic comes out in the summary of the fit
summary(fit2)$concordance

# It can also be calculated using the survConcordance function
survConcordance(Surv(fu.time, fu.stat) ~ predict(fit2), data=mockstudy)
```

### Extract data using `broom` package

The `broom` package makes it easy to extract information from the fit.

```{r}
tidy(fit) # coefficients, p-values

glance(fit) # model summary statistics
```

### Create a summary table using modelsum

```{r results="asis"}
##Note: You must use quotes when specifying family="survival" 
##      family=survival will not work
summary(modelsum(Surv(fu.time, fu.stat) ~ arm, 
                 adjust=~age + sex, data=mockstudy, family="survival"))

##Note: the pspline term is not working yet
#summary(modelsum(Surv(fu.time, fu.stat) ~ arm, 
#                adjust=~pspline(age) + sex, data=mockstudy, family='survival'))
```


## Poisson

Poisson regression is useful when predicting an outcome variable representing counts.
It can also be useful when looking at survival data.  Cox models and Poisson models are very closely
related and survival data can be summarized using Poisson regression. If you have overdispersion (see
if the residual deviance is much larger than degrees of freedom), you may want to use `quasipoisson()`
instead of `poisson()`.  Some of these diagnostics need to be done outside of `modelsum`. 

### Example 1: fit and summarize a Poisson regression model 

For the first example, use the solder dataset available in the `rpart` package.  The endpoint `skips` has a definite Poisson look.

```{r poisson}
require(rpart) ##just to get access to solder dataset
data(solder)
hist(solder$skips)

fit <- glm(skips ~ Opening + Solder + Mask , data=solder, family=poisson)
stats::anova(fit, test='Chi')
summary(fit)
```

Overdispersion is when the Residual deviance is larger than the degrees of freedom.  This can be tested, approximately using the following code.  The goal is to have a p-value that is $>0.05$.

```{r}
1-pchisq(fit$deviance, fit$df.residual)
```

One possible solution is to use the quasipoisson family instead of the poisson family.  This adjusts for the overdispersion.

```{r}
fit2 <- glm(skips ~ Opening + Solder + Mask, data=solder, family=quasipoisson)
summary(fit2)
```

### Extract data using `broom` package

The `broom` package makes it easy to extract information from the fit.

```{r}
tidy(fit) # coefficients, p-values

glance(fit) # model summary statistics
```


### Create a summary table using modelsum

```{r results='asis'}
summary(modelsum(skips~Opening + Solder + Mask, data=solder, family="quasipoisson"))
summary(modelsum(skips~Opening + Solder + Mask, data=solder, family="poisson"))
```

### Example 2: fit and summarize a Poisson regression model 

This second example uses the survival endpoint available in the `mockstudy` dataset.  There is a close
relationship between survival and Poisson models, and often it is easier to fit the model using Poisson
regression, especially if you want to present absolute risk.

```{r}
# add .01 to the follow-up time (.01*1 day) in order to keep everyone in the analysis
fit <- glm(fu.stat ~ offset(log(fu.time+.01)) + age + sex + arm, data=mockstudy, family=poisson)
summary(fit)
1-pchisq(fit$deviance, fit$df.residual)

coef(coxph(Surv(fu.time,fu.stat) ~ age + sex + arm, data=mockstudy))
coef(fit)[-1]

# results from the Poisson model can then be described as risk ratios (similar to the hazard ratio)
exp(coef(fit)[-1])

# As before, we can model the dispersion which alters the standard error
fit2 <- glm(fu.stat ~ offset(log(fu.time+.01)) + age + sex + arm, 
            data=mockstudy, family=quasipoisson)
summary(fit2)
```

### Extract data using `broom` package

The `broom` package makes it easy to extract information from the fit.

```{r}
tidy(fit) ##coefficients, p-values

glance(fit) ##model summary statistics
```


### Create a summary table using `modelsum`

Remember that the result from `modelsum` is different from the `fit` above.  The `modelsum`
summary shows the results for `age + offset(log(fu.time+.01))` then `sex + offset(log(fu.time+.01))`
instead of `age + sex + arm + offset(log(fu.time+.01))`.

```{r results="asis", eval=TRUE}
summary(modelsum(fu.stat ~ age, adjust=~offset(log(fu.time+.01))+ sex + arm, 
                 data=mockstudy, family=poisson))
                 
```


# Additional Examples


Here are multiple examples showing how to use some of the different options.

## 1. Change summary statistics globally

There are standard settings for each type of model regarding what information is summarized in the table.
This behavior can be modified using the modelsum.control function. In fact, you can save your standard
settings and use that for future tables.  


```{r, results='asis'}
mycontrols  <- modelsum.control(gaussian.stats=c("estimate","std.error","adj.r.squared","Nmiss"),
                                show.adjust=FALSE, show.intercept=FALSE)                            
tab2 <- modelsum(bmi ~ age, adjust=~sex, data=mockstudy, control=mycontrols)
summary(tab2)
```

You can also change these settings directly in the modelsum call.  

```{r, results='asis'}
tab3 <- modelsum(bmi ~  age, adjust=~sex, data=mockstudy,
                 gaussian.stats=c("estimate","std.error","adj.r.squared","Nmiss"), 
                 show.intercept=FALSE, show.adjust=FALSE)
summary(tab3)
```

## 2. Add labels to independent variables

In the above example, age is shown with a label (Age in Years), but sex is listed "as is".
This is because the data was created in SAS and in the SAS dataset, age had a label but sex did not.
The label is stored as an attribute within R.

```{r check-labels}
## Look at one variable's label
attr(mockstudy$age,'label')

## See all the variables with a label
unlist(lapply(mockstudy,'attr','label'))

## or
cbind(sapply(mockstudy,attr,'label'))
```

If you want to add labels to other variables, there are a couple of options.  First, you could add labels to the variables in your dataset.

```{r add-label, results='asis'}
attr(mockstudy$age,'label')  <- 'Age, yrs'

tab1 <- modelsum(bmi ~  age, adjust=~sex, data=mockstudy)
summary(tab1)
```

You can also use the built-in `data.frame` method for `labels<-`:

```{r, results = 'asis'}
labels(mockstudy)  <- c(age = 'Age, yrs')

tab1 <- modelsum(bmi ~  age, adjust=~sex, data=mockstudy)
summary(tab1)
```

Another option is to add labels after you have created the table

```{r, results='asis'}
mylabels <- list(sexFemale = "Female", age ="Age, yrs")
summary(tab1, labelTranslations = mylabels)
```

Alternatively, you can check the variable labels and manipulate them with a function called `labels`, which works on the `modelsum` object.

```{r, eval=TRUE}
labels(tab1)
labels(tab1) <- c(sexFemale="Female", age="Baseline Age (yrs)")
labels(tab1)
```

```{r, results='asis'}
summary(tab1)
```

## 3. Don't show intercept values

```{r, results='asis'}
summary(modelsum(age~mdquality.s+sex, data=mockstudy), show.intercept=FALSE)
```

## 4. Don't show results for adjustment variables

```{r, results='asis'}
summary(modelsum(mdquality.s ~ age + bmi, data=mockstudy, adjust=~sex, family=binomial),
        show.adjust=FALSE)  
```

## 5. Summarize multiple variables without typing them out

Often one wants to summarize a number of variables.  Instead of typing by hand each individual variable,
an alternative approach is to create a formula using the `paste` command with the `collapse="+"` option.  

```{r, results='asis'}
# create a vector specifying the variable names
myvars <- names(mockstudy)

# select the 8th through the 12th
# paste them together, separated by the + sign
RHS <- paste(myvars[8:12], collapse="+")
RHS

# create a formula using the as.formula function
as.formula(paste('mdquality.s ~ ', RHS))

# use the formula in the modelsum function
summary(modelsum(as.formula(paste('mdquality.s ~', RHS)), family=binomial, data=mockstudy))
```

These steps can also be done using the `formulize` function.

```{r, results='asis'}
## The formulize function does the paste and as.formula steps
tmp <- formulize('mdquality.s',myvars[8:10])
tmp

## More complex formulas could also be written using formulize
tmp2 <- formulize('mdquality.s',c('ps','hgb','sqrt(bmi)'))

## use the formula in the modelsum function
summary(modelsum(tmp, data=mockstudy, family=binomial))
```


## 6. Subset the dataset used in the analysis

Here are two ways to get the same result (limit the analysis to subjects age>50 and in the F: FOLFOX treatment group). 

* The first approach uses the subset function applied to the dataset `mockstudy`.
This example also selects a subset of variables.  The `modelsum` function is then applied to this subsetted data.


```{r}
newdata <- subset(mockstudy, subset=age>50 & arm=='F: FOLFOX', select = c(age,sex, bmi:alk.phos))
dim(mockstudy)
table(mockstudy$arm)
dim(newdata)
names(newdata)
```

```{r, results='asis'}
summary(modelsum(alk.phos ~ ., data=newdata))
```

* The second approach does the same analysis but uses the subset
argument within `modelsum` to subset the data.

```{r, results='asis', eval=TRUE}
summary(modelsum(log(alk.phos) ~ sex + ps + bmi, subset=age>50 & arm=="F: FOLFOX", data=mockstudy))
summary(modelsum(alk.phos ~ ps + bmi, adjust=~sex, subset = age>50 & bmi<24, data=mockstudy))
summary(modelsum(alk.phos ~ ps + bmi, adjust=~sex, subset=1:30, data=mockstudy))
```

## 7. Create combinations of variables on the fly

```{r}
## create a variable combining the levels of mdquality.s and sex
with(mockstudy, table(interaction(mdquality.s,sex)))
```

```{r, results='asis'}
summary(modelsum(age ~ interaction(mdquality.s,sex), data=mockstudy))
```

## 8. Transform variables on the fly

Certain transformations need to be surrounded by `I()` so that R knows to treat it as a variable
transformation and not some special model feature.  If the transformation includes any of the
symbols `/ - + ^ *` then surround the new variable by `I()`.


```{r, results='asis'}
summary(modelsum(arm=="F: FOLFOX" ~ I(age/10) + log(bmi) + mdquality.s,
                 data=mockstudy, family=binomial))
```


## 9. Change the ordering of the variables or delete a variable

```{r, results='asis'}
mytab <- modelsum(bmi ~ sex + alk.phos + age, data=mockstudy)
mytab2 <- mytab[c('age','sex','alk.phos')]
summary(mytab2)
summary(mytab[c('age','sex')])
summary(mytab[c(3,1)])
```

## 10. Merge two `modelsum` objects together 

It is possible to merge two modelsum objects so that they print out together, however you need to pay
attention to the columns that are being displayed.  It is sometimes easier to combine two models of the same
family (such as two sets of linear models). Overlapping y-variables will have their x-variables
concatenated, and (if `all=TRUE`) non-overlapping y-variables will have their tables printed separately.

```{r, results="asis"}
## demographics
tab1 <- modelsum(bmi ~ sex + age, data=mockstudy)
## lab data
tab2 <- modelsum(mdquality.s ~ hgb + alk.phos, data=mockstudy, family=binomial)
                
tab12 <- merge(tab1, tab2, all = TRUE)
class(tab12)
summary(tab12)
```

## 11. Add a title to the table

When creating a pdf the tables are automatically numbered and the title appears below the table.
In Word and HTML, the titles appear un-numbered and above the table.  

```{r, results='asis'}
t1 <- modelsum(bmi ~ sex + age, data=mockstudy)
summary(t1, title='Demographics')
```

## 12. Modify how missing values are treated

Depending on the report you are writing you have the following options: 

* Use all values available for each variable

* Use only those subjects who have measurements available for all the variables

```{r}
## look at how many missing values there are for each variable
apply(is.na(mockstudy),2,sum)
```

```{r, results='asis'}
## Show how many subjects have each variable (non-missing)
summary(modelsum(bmi ~ ast + age, data=mockstudy,
                control=modelsum.control(gaussian.stats=c("N","estimate"))))

## Always list the number of missing values
summary(modelsum(bmi ~ ast + age, data=mockstudy,
                control=modelsum.control(gaussian.stats=c("Nmiss2","estimate"))))

## Only show the missing values if there are some (default)
summary(modelsum(bmi ~ ast + age, data=mockstudy, 
                control=modelsum.control(gaussian.stats=c("Nmiss","estimate"))))

## Don't show N at all
summary(modelsum(bmi ~ ast + age, data=mockstudy, 
                control=modelsum.control(gaussian.stats=c("estimate"))))
```

## 13. Modify the number of digits used

Within modelsum.control function there are 3 options for controlling the number of significant digits shown.  

* digits: controls the number of digits after the decimal point for continuous values

* digits.ratio: controls the number of digits after the decimal point for continuous values

* digits.p: controls the number of digits after the decimal point for continuous values

```{r, results='asis'}
summary(modelsum(bmi ~ sex + age + fu.time, data=mockstudy), digits=4, digits.test=2)
```

## 14. Use case-weights in the models

Occasionally it is of interest to fit models using case weights.
The `modelsum` function allows you to pass on the weights to the models and it will do the appropriate fit.

```{r}
mockstudy$agegp <- cut(mockstudy$age, breaks=c(18,50,60,70,90), right=FALSE)

## create weights based on agegp and sex distribution
tab1 <- with(mockstudy,table(agegp, sex))
tab1
tab2 <- with(mockstudy, table(agegp, sex, arm))
gpwts <- rep(tab1, length(unique(mockstudy$arm)))/tab2

## apply weights to subjects
index <- with(mockstudy, cbind(as.numeric(agegp), as.numeric(sex), as.numeric(as.factor(arm)))) 
mockstudy$wts <- gpwts[index]

## show weights by treatment arm group
tapply(mockstudy$wts,mockstudy$arm, summary)
```

```{r results='asis'}
mockstudy$newvarA <- as.numeric(mockstudy$arm=='A: IFL')
tab1 <- modelsum(newvarA ~ ast + bmi + hgb, data=mockstudy, subset=(arm !='G: IROX'), 
                 family=binomial)
summary(tab1, title='No Case Weights used')

suppressWarnings({
tab2 <- modelsum(newvarA ~ ast + bmi + hgb, data=mockstudy, subset=(arm !='G: IROX'), 
                 weights=wts, family=binomial)
summary(tab2, title='Case Weights used')
})
```
          
## 15. Use `modelsum` within an Sweave document

For those users who wish to create tables within an Sweave document, the following code seems to work.

```
\documentclass{article}

\usepackage{longtable}
\usepackage{pdfpages}

\begin{document}

\section{Read in Data}
<<echo=TRUE>>=
require(arsenal)
require(knitr)
require(rmarkdown)
data(mockstudy)

tab1 <- modelsum(bmi~sex+age, data=mockstudy)
@

\section{Convert Summary.modelsum to LaTeX}
<<echo=TRUE, results='hide', message=FALSE>>=
capture.output(summary(tab1), file="Test.md")

## Convert R Markdown Table to LaTeX
render("Test.md", pdf_document(keep_tex=TRUE))
@ 

\includepdf{Test.pdf}

\end{document}
```
## 16. Export `modelsum` results to a .CSV file

When looking at multiple variables it is sometimes useful to export the results to a csv file.
The `as.data.frame` function creates a data frame object that can be exported or further manipulated within R.


```{r}
summary(tab2, text=T)
tmp <- as.data.frame(summary(tab2, text = TRUE))
tmp
# write.csv(tmp, '/my/path/here/mymodel.csv')
```

## 17. Write `modelsum` object to a separate Word or HTML file

```{r eval = FALSE}
## write to an HTML document
write2html(tab2, "~/ibm/trash.html")

## write to a Word document
write2word(tab2, "~/ibm/trash.doc", title="My table in Word")
```

## 18. Use `modelsum` in R Shiny

The easiest way to output a `modelsum()` object in an R Shiny app is to use the `tableOutput()` UI in combination with
the `renderTable()` server function and `as.data.frame(summary(modelsum()))`:

```{r eval=FALSE}
# A standalone shiny app
library(shiny)
library(arsenal)
data(mockstudy)

shinyApp(
  ui = fluidPage(tableOutput("table")),
  server = function(input, output) {
    output$table <- renderTable({
      as.data.frame(summary(modelsum(age ~ sex, data = mockstudy), text = "html"))
    }, sanitize.text.function = function(x) x)
  }
)
```

This can be especially powerful if you feed the selections from a `selectInput(multiple = TRUE)` into `formulize()` to make
the table dynamic!

## 23. Use `modelsum` in bookdown

Since the backbone of `modelsum()` is `knitr::kable()`, tables still render well in bookdown. However, `print.summary.modelsum()` doesn't use
the `caption=` argument of `kable()`, so some tables may not have a properly numbered caption. To fix this, use the method described
[on the bookdown site](https://bookdown.org/yihui/bookdown/tables.html) to give the table a tag/ID.

```{r eval=FALSE}
summary(modelsum(age ~ sex, data = mockstudy), title="(\\#tab:mytableby) Caption here")
```

## 24. Model multiple endpoints

You can now use `list()` on the left-hand side of `modelsum()` to give multiple endpoints.
Note that only one "family" can be specified this way (use `merge()` instead if you want multiple families).

```{r results='asis'}
summary(modelsum(list(age, hgb) ~ bmi + sex, adjust = ~ arm, data = mockstudy))
```

To avoid confusion about which table is which endpoint, you can set `term.name=TRUE` in `summary()`. This takes the labels
for each endpoint and puts them in the top-left of the table.

```{r results='asis'}
summary(modelsum(list(age, hgb) ~ bmi + sex, adjust = ~ arm, data = mockstudy), term.name = TRUE)
```

## 25. Model data by a non-test group (strata)

You can also specify a grouping variable that doesn't get tested (but instead separates results): a *strata* variable.

```{r results='asis'}
summary(modelsum(list(age, hgb) ~ bmi + sex, strata = arm, data = mockstudy))
```

## 26. Add multiple sets of adjustors to the model

By putting multiple formulas into a list, you can use multiple sets of adjustors. Use `~ 1` or `NULL` for an "unadjusted" model. By using the 
`adjustment.names=TRUE` argument and giving names to your adjustor sets in the list, you can name the various analyses.

```{r}
adj.list <- list(
  Unadjusted = ~ 1, # can also specify NULL here
  "Adjusted for Arm" = ~ arm
)
multi.adjust <- modelsum(list(age, bmi) ~ fu.time + ast, adjust = adj.list, data = mockstudy)
summary(multi.adjust, adjustment.names = TRUE)
summary(multi.adjust, adjustment.names = TRUE, show.intercept = FALSE, show.adjust = FALSE)
```


# Available Function Options

## Summary statistics

The available summary statistics, by varible type, are:

* `ordinal`: Ordinal logistic regression models
  + default: `Nmiss, OR, CI.lower.OR, CI.upper.OR, p.value`
  + optional: `estimate, CI.OR, CI.estimate, CI.lower.estimate, CI.upper.estimate,`
       `N, Nmiss2, endpoint, std.error, statistic, logLik, AIC, BIC, edf, deviance, df.residual, p.value.lrt`
* `binomial`,`quasibinomial`: Logistic regression models 
  +  default:  `OR, CI.lower.OR, CI.upper.OR, p.value, concordance, Nmiss`
  +  optional: `estimate, CI.OR, CI.estimate, CI.lower.estimate, CI.upper.estimate,`
        `CI.wald, CI.lower.wald, CI.upper.wald, CI.OR.wald, CI.lower.OR.wald, CI.upper.OR.wald,`
        `N, Nmiss2, Nevents, endpoint, std.error, statistic, logLik, AIC, BIC, null.deviance, deviance, df.residual, df.null, p.value.lrt`
* `gaussian`: Linear regression models 
  +  default:  `estimate, std.error, p.value, adj.r.squared, Nmiss`
  +  optional: `CI.estimate, CI.lower.estimate, CI.upper.estimate, N, Nmiss2, statistic,`
        `standard.estimate, endpoint, r.squared, AIC, BIC, logLik, statistic.F, p.value.F, p.value.lrt`
* `poisson`, `quasipoisson`: Poisson regression models 
  +  default: `RR, CI.lower.RR, CI.upper.RR, p.value, Nmiss`
  +  optional: `CI.RR, CI.estimate, CI.lower.estimate, CI.upper.estimate, CI.RR, Nmiss2, std.error,`
        `estimate, statistic, endpoint, AIC, BIC, logLik, dispersion, null.deviance, deviance, df.residual, df.null, p.value.lrt`
* `negbin`: Negative binomial regression models 
  +  default: `RR, CI.lower.RR, CI.upper.RR, p.value, Nmiss`
  +  optional: `CI.RR, CI.estimate, CI.lower.estimate, CI.upper.estimate, CI.RR, Nmiss2, std.error, estimate,`
        `statistic, endpoint, AIC, BIC, logLik, dispersion, null.deviance, deviance, df.residual, df.null, theta, SE.theta, p.value.lrt`
* `clog`: Conditional Logistic models
  + default: `OR, CI.lower.OR, CI.upper.OR, p.value, concordance, Nmiss`
  + optional: `CI.OR, CI.estimate, CI.lower.estimate, CI.upper.estimate, N, Nmiss2, estimate, std.error, endpoint, Nevents, statistic,`
        `r.squared, r.squared.max, logLik, AIC, BIC, statistic.log, p.value.log, statistic.sc, p.value.sc,`
        `statistic.wald, p.value.wald, N, std.error.concordance, p.value.lrt`
* `survival`: Cox models
  +  default: `HR, CI.lower.HR, CI.upper.HR, p.value, concordance, Nmiss`
  +  optional: `CI.HR, CI.estimate, CI.lower.estimate, CI.upper.estimate, N, Nmiss2, estimate, std.error, endpoint,`
        `Nevents, statistic, r.squared, r.squared.max, logLik, AIC, BIC, statistic.log, p.value.log, statistic.sc, p.value.sc,`
        `statistic.wald, p.value.wald, N, std.error.concordance, p.value.lrt`

The full description of these parameters that can be shown for models include:

* `N`: a count of the number of observations used in the analysis
* `Nmiss`: only show the count of the number of missing values if there are some missing values 
* `Nmiss2`: always show a count of the number of missing values for a model 
* `endpoint`: dependent variable used in the model
* `std.err`: print the standard error
* `statistic`: test statistic
* `statistic.F`: test statistic (F test)
* `p.value`: print the p-value
* `p.value.lrt`: print the likelihood ratio p-value for *the main effect only* (not the adjustors)
* `r.squared`: print the model R-square 
* `adj.r.squared`: print the model adjusted R-square 
* `r.squared.max`: print the model R-square
* `concordance`: print the model C statistic (which is the AUC for logistic models)
* `logLik`: print the loglikelihood value
* `p.value.log`: print the p-value for the overall model likelihood test
* `p.value.wald`: print the p-value for the overall model wald test
* `p.value.sc`: print the p-value for overall model score test
* `AIC`: print the Akaike information criterion
* `BIC`: print the Bayesian information criterion
* `null.deviance`: null deviance
* `deviance`: model deviance
* `df.residual`: degrees of freedom for the residual
* `df.null`: degrees of freedom for the null model
* `dispersion`: This is used in Poisson models and is defined as the deviance/df.residual
* `statistic.sc`: overall model score statistic
* `statistic.wald`: overall model score statistic
* `statistic.log`: overall model score statistic
* `std.error.concordance`: standard error for the C statistic
* `HR`: print the hazard ratio (for survival models), i.e. exp(beta)
* `CI.lower.HR, CI.upper.HR`: print the confidence interval for the HR 
* `OR`: print the odd's ratio (for logistic models), i.e. exp(beta)
* `CI.lower.OR, CI.upper.OR`: print the confidence interval for the OR
* `CI.lower.OR.wald, CI.upper.OR.wald`: print the Wald confidence interval for the OR
* `RR`: print the risk ratio (for poisson models), i.e. exp(beta)
* `CI.lower.RR, CI.upper.RR`: print the confidence interval for the RR
* `estimate`: print beta coefficient
* `standardized.estimate`: print the standardized beta coefficient 
* `CI.lower.estimate, CI.upper.estimate`: print the confidence interval for the beta coefficient
* `CI.lower.wald, CI.upper.wald`: print the Wald confidence interval for the beta coefficient
* `edf`: print the effective degrees of freedom.
* `theta`: print the estimate of theta.
* `SE.theta`: print the estimate of theta's standard error.

## `modelsum.control` settings

A quick way to see what arguments are possible to utilize in a function is to use the `args()`
command. Settings involving the number of digits can be set in `modelsum.control` or in `summary.modelsum`.

```{r}
args(modelsum.control)
```

## `summary.modelsum` settings

The summary.modelsum function has options that modify how the table appears (such as adding a title or modifying labels).  

```{r}
args(arsenal:::summary.modelsum)
```

