Brew with knitR

brew_knit.R

# Preprocess template using brew and then run knit
brew_knit <- function(template, ...){
  input = gsub(".Rnwe", '.Rnw', template)
  brew(template, input)
  knit(input, ...)
}

doc.Rnw

\section{Analysis of Hip Bone Mineral Density}
\subsection{26w BMD and Baseline Predictors}
In this section I model 26w interpolated BMD using baseline BMD (all four measures, not just the target measure), baseline weight, age,sex, race, \$t\$-score stratum, and treatment.  I first fit a saturatedmodel with respect to all of the continuous predictors, using restricted cubic splines with 5 knots, then potentially use partial \$\chi^2\$ (blinded to nonlinearity components) to reassign degrees of freedom.

<< hip-sat26,results="asis", eval = F>>=
f <- ols(hip26 ~ sex*rcs(hip0,5) + 
  rcs(lumbar0,5) + rcs(femur0,5) +
  rcs(forearm0,5) + sex*rcs(wt0, 5) + trtp + 
  rcs(age, 5) + race + sex + blppar + bltscgrp, data=d)
print(f, coefs = FALSE, latex = TRUE)
lan(f)
@

The global test for nonlinearity is nowhere close to being significant, so all nonlinear effects will be dropped.



\section{Analysis of Lumbar Bone Mineral Density}
\subsection{26w BMD and Baseline Predictors}
In this section I model 26w interpolated BMD using baseline BMD (all four measures, not just the target measure), baseline weight, age,sex, race, \$t\$-score stratum, and treatment.  I first fit a saturatedmodel with respect to all of the continuous predictors, using restricted cubic splines with 5 knots, then potentially use partial \$\chi^2\$ (blinded to nonlinearity components) to reassign degrees of freedom.

<< lumbar-sat26,results="asis", eval = F>>=
f <- ols(lumbar26 ~ sex*rcs(lumbar0,5) + 
  rcs(hip0,5) + rcs(femur0,5) +
  rcs(forearm0,5) + sex*rcs(wt0, 5) + trtp + 
  rcs(age, 5) + race + sex + blppar + bltscgrp, data=d)
print(f, coefs = FALSE, latex = TRUE)
lan(f)
@

The global test for nonlinearity is nowhere close to being significant, so all nonlinear effects will be dropped.



\section{Analysis of Femur Bone Mineral Density}
\subsection{26w BMD and Baseline Predictors}
In this section I model 26w interpolated BMD using baseline BMD (all four measures, not just the target measure), baseline weight, age,sex, race, \$t\$-score stratum, and treatment.  I first fit a saturatedmodel with respect to all of the continuous predictors, using restricted cubic splines with 5 knots, then potentially use partial \$\chi^2\$ (blinded to nonlinearity components) to reassign degrees of freedom.

<< femur-sat26,results="asis", eval = F>>=
f <- ols(femur26 ~ sex*rcs(femur0,5) + 
  rcs(hip0,5) + rcs(lumbar0,5) +
  rcs(forearm0,5) + sex*rcs(wt0, 5) + trtp + 
  rcs(age, 5) + race + sex + blppar + bltscgrp, data=d)
print(f, coefs = FALSE, latex = TRUE)
lan(f)
@

The global test for nonlinearity is nowhere close to being significant, so all nonlinear effects will be dropped.



\section{Analysis of Forearm Bone Mineral Density}
\subsection{26w BMD and Baseline Predictors}
In this section I model 26w interpolated BMD using baseline BMD (all four measures, not just the target measure), baseline weight, age,sex, race, \$t\$-score stratum, and treatment.  I first fit a saturatedmodel with respect to all of the continuous predictors, using restricted cubic splines with 5 knots, then potentially use partial \$\chi^2\$ (blinded to nonlinearity components) to reassign degrees of freedom.

<< forearm-sat26,results="asis", eval = F>>=
f <- ols(forearm26 ~ sex*rcs(forearm0,5) + 
  rcs(hip0,5) + rcs(lumbar0,5) +
  rcs(femur0,5) + sex*rcs(wt0, 5) + trtp + 
  rcs(age, 5) + race + sex + blppar + bltscgrp, data=d)
print(f, coefs = FALSE, latex = TRUE)
lan(f)
@

The global test for nonlinearity is nowhere close to being significant, so all nonlinear effects will be dropped.

doc.Rnwe

<% v = c("hip", "lumbar", "femur", "forearm") -%>
<% oth1 = c("lumbar", "hip", "hip", "hip") -%>
<% oth2 = c("femur", "femur", "lumbar", "lumbar") -%>
<% oth3 = c("forearm", "forearm", "forearm", "femur") -%>


<% for (i in 1:4) { -%>

<% title = gsub('^([a-z])', '\\U\\1', v[i], perl = T) %>
\section{Analysis of <%= title %> Bone Mineral Density}

\subsection{26w BMD and Baseline Predictors}
In this section I model 26w interpolated BMD using baseline BMD (all four measures, not just the target measure), baseline weight, age,sex, race, \$t\$-score stratum, and treatment.  I first fit a saturatedmodel with respect to all of the continuous predictors, using restricted cubic splines with 5 knots, then potentially use partial \$\chi^2\$ (blinded to nonlinearity components) to reassign degrees of freedom.

<< <%= v[i] %>-sat26,results="asis", eval = F>>=
f <- ols(<%= v[i] %>26 ~ sex*rcs(<%= v[i] %>0,5) + 
  rcs(<%= oth1[i] %>0,5) + rcs(<%= oth2[i] %>0,5) +
  rcs(<%= oth3[i] %>0,5) + sex*rcs(wt0, 5) + trtp + 
  rcs(age, 5) + race + sex + blppar + bltscgrp, data=d)
print(f, coefs = FALSE, latex = TRUE)
lan(f)
@

The global test for nonlinearity is nowhere close to being significant, so all nonlinear effects will be dropped.

<% } %>