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variableselection_candidate00.R
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#' ---
#' title: "Variable selection by SDOH domain for analysis of successful community discharge from rehab by RSAs"
#' author: 'RSA Grant Team'
#' abstract: |
#' | Some preliminary unsupervised variable selection
#' documentclass: article
#' description: 'Manuscript'
#' clean: false
#' self_contained: true
#' number_sections: false
#' keep_md: true
#' fig_caption: true
#' css: 'production.css'
#' output:
#' html_document:
#' toc: true
#' toc_float: true
#' pdf_document: default
#' word_document: default
#' ---
#+ init, echo=FALSE, message=FALSE, warning=FALSE,results='hide'
# Init ----
debug <- 0;
knitr::opts_chunk$set(echo=debug>0, warning=debug>0, message=debug>0);
# Global project settings ----
inputdata <- c();
source('config.R',local=T,echo=debug>0);
# inputdata <- c(dat0='data/SIM_SDOH_ZCTA.xlsx' # census data by ZCTA
# ,cx0='data/SIM_ALLCMS.csv' # RSA-ZCTA crosswalk
# ,rsa0='data/SIM_RSAv4 SCD RSRs.csv' # outcomes (RSR)
# ,dct0='data/data_dictionary.csv' # data dictionary
# ,dat1='SDOH_RSR_2013_prelim.csv' # the dat1 dataset
# ,dat2='SDOH_RSR_2013_scaled_prelim.csv' # the scaled version of
# );
# Load libraries ----
library(rio); library(dplyr); library(tidbits); # data handling
library(pander); library(broom); # formatting
#library(GGally);
#library(mice);
library(psych); # factor analysis
library(caret); # cross-validation
library(Boruta); # variable selection
library(nFactors); # optimal number of factors
# Make tables never split
panderOptions('table.split.table',Inf);
panderOptions('table.split.cells',Inf);
panderOptions('p.copula',', ');
options(tinytex.verbose=TRUE)
# Get stepAICc (like stepAIC but adjusting for small sample sizes)
source('project_functions.R');
# Local project settings ----
# tweak base plot settings to avoid captions going off-screen
.par_default <- par(no.readonly = TRUE);
.par_borutaplot <- list(mar=c(0.5, 6, 1, 0.5), mgp=c(0, 0.2, 0), cex=0.9,
tcl=0.2);
# overwrite previously set global values if needed
if(file.exists('local.config.R')){
source('local.config.R',local=TRUE,echo = debug>0);
if(exists('.local.inputdata')){
inputdata <- replace(inputdata,names(.local.inputdata),.local.inputdata)};
};
# Import data ----
# if merged files not already built, run the script that builds them
if(!all(file.exists(inputdata[c('dat1','dat2')]))){
system('R --vanilla -q -s -f data.R',ignore.stdout = debug==0,ignore.stderr = debug==0, wait=TRUE,intern=TRUE)};
dat1 <- import(inputdata['dat1']);
dat2 <- import(inputdata['dat2']);
dct0 <- import(inputdata['dct0']);
dct0 <- subset(dct0,column %in% colnames(dat1));
# Obtain the numeric-only columns as dat3tr and dat3ts for training and test
# sets, respectively
dat3tr <- subset(dat2,subsample=='train') %>% select(where(is.numeric));
dat3ts <- subset(dat2,subsample=='test') %>% select(where(is.numeric));
colorizeVars <- cbind(dct0$column,colorByList(dct0$column,template="[%1$s]{.%2$stext}"));
cGroupRename <- cbind(c('c_AHRQsocial','c_AHRQecon','c_AHRQedu','c_AHRQphysinfr'
,'c_AHRQhealth','c_AHRQgeo')
,c('Social','Economic','Education'
,'Physical Infrastructure','Health','Geography'));
#'
#' ----
formals(colorByList)$colorList %>% eval %>%
sprintf('[%s]{.%stext}',.,names(.)) %>% submulti(cGroupRename) %>%
cbind() %>%
pander(col.names='AHRQ Domain Key',justify='left');
#' ----
ahrqvars <- v() %>% grep('c_AHRQ',.,val=T) %>%
sapply(function(xx) sprintf('v(%s)',xx) %>% parse(text=.) %>% eval);
names(ahrqvars) <- submulti(names(ahrqvars),cGroupRename);
#' # How many variables to consider in factor analysis?
#'
#' There are `r length(unlist(ahrqvars))` numeric variables in the AHRQ data:
lapply(ahrqvars,function(xx) unname(colorByList(xx))) %>% pander #pander(ahrqvars);
#'
#' From the [main variable selection report](https://www.dropbox.com/home/Reistetter%20RSA%20SDOH%20Grant%202021?preview=variableselection.html)
#' we see that factors derived from this full set of variables do not fit
#' cleanly into the five SDOH domains.
#'
#' How can we weed out some of the redundant or non-informative ones to see if
#' the remaining variables have better separation? A possible approach is
#' splitting performing stepwise selection (bi-directional, using the Bayes
#' Information Criterion to determine when convergence has been achieved).
#+ ahrqvarsBIC, results='hide', cache=debug<=0
ahrqvarsBIClms <- sapply(ahrqvars,intersect,names(dat3tr)) %>%
lapply(paste,collapse='+') %>% lapply(function(xx) paste('RSR~',xx))%>%
lapply(function(xx) update(lm(xx,data=dat3tr),.~.)) %>%
lapply(function(xx) stepAICc(xx
,scope=list(lower=RSR~1
,upper=update(xx,.~(.)^2))
,direction='both',k=log(nrow(dat3tr))));
ahrqvarsBIC <- lapply(ahrqvarsBIClms,terms) %>% lapply(attr,'term.labels') %>%
lapply(intersect,names(dat3tr));
ahrqcolsBIC <- unlist(ahrqvarsBIC) %>% unname();
#' Now there are only `r length(ahrqcolsBIC)` variables:
lapply(ahrqvarsBIC,function(xx) unname(colorByList(xx))) %>% pander; #pander(ahrqvarsBIC);
#' We then can perform factor analysis on just this set of variables.
#'
# factor analysis ----
#' # How many factors to use?
#'
#+ scree, cache=debug<=0
set.seed(project_seed);
.junk<-capture.output(fapar3 <- fa.parallel(select(dat3tr,all_of(ahrqcolsBIC))
,fm='ml',fa='fa'
,nfactors = length(ahrqcolsBIC)
,show.legend=F));
#' Looks like it's `r fapar3$nfact`
#'
#' # Factor analysis
#'
#'
#+ fa, cache=debug<=0,messages=FALSE
fa3 <-fa(select(dat3tr,all_of(ahrqcolsBIC)),nfactors = fapar3$nfact
,rotate='varimax',fm='ml') %>% fa.sort();
faload3 <- fa3$loadings[];
faload3[faload3<0.15] <- NA;
faload3 <- apply(faload3,2,function(xx) names(na.omit(xx)),simplify = F) %>%
Filter(function(xx) length(xx)>0,.);
#+ faplot, fig.width=10
pvdat3 <- with(fa3,Vaccounted['Proportion Explained'
,intersect(colnames(Vaccounted), names(faload3))]);
names(pvdat3) <- scales::percent(pvdat3,accuracy=0.1) %>%
paste0(names(.),', ',.);
par(mar=c(7,4.1,4.1,2.1));
barplot(pvdat3,ylab='Proportion of Variance Explained',las=2);
par(.par_default);
# faload3a <- lapply(faload3,function(xx) base::ifelse(xx %in% v(c_domainexpert)
# ,gsub('text\\{([^}]*)\\}','text{[\\1]}',colorByList(xx))
# ,colorByList(xx)));
faload3b <- sapply(faload3,function(xx) lapply(ahrqvars,function(yy) {
oo<-intersect(xx,yy);
ifelse(oo %in% v(c_domainexpert)
,gsub('\\[(.*)\\]','[**\\1**]',colorByList(oo))
#, gsub('text\\{([^}]*)\\}','text{[\\1]}',colorByList(oo))
,colorByList(oo))}) %>%
Filter(function(zz) length(zz)>0,.) ,simplify = F);
#'
#'
#' These `r length(faload3b)` factors account for
#' `r scales::percent(sum(pvdat3),accuracy=0.1)` of the variation. If we only
#' retain variables that have a loading >=0.15 for at least one factor, we are
#' left with the following set:
#+ factorlist
#pander(varsdat3a);
pander(faload3b);
#' This 0.15 loading threshold is the highest that can be used without excluding
#' the [Education]{.greentext} SDOH domain (which has `r length(ahrqvarsBIC$Education)`
#' variables).
#'
frm_exp0 <- v(c_domainexpert,dat3tr) %>% intersect(ahrqcolsBIC) %>%
paste(collapse=' + ') %>% paste('RSR ~ ',.) %>% as.formula(env = NULL);
frm_display <- paste(colorByList(intersect(v(c_domainexpert,dat3tr),ahrqcolsBIC))
,collapse=' + ') %>% paste('RSR ~',.);
frm_displayFull <- paste(colorByList(ahrqcolsBIC),collapse=' + ') %>%
paste('RSR ~',.);
lmbasedat3 <- lm(RSR~1,data=dat3tr);
lmstartdat3 <- update(lmbasedat3,formula=frm_exp0);
lmalldat3 <- paste(ahrqcolsBIC,collapse='+') %>% paste('RSR~(',.,')^2') %>%
lm(data=dat3tr) %>% update(.~.);
#' # A model using all the variables in the reduced set
#'
#+ lmfulldat, comment=''
lmfulldat <- paste(ahrqcolsBIC,collapse='+') %>% paste('RSR~',.) %>%
lm(data=dat3tr) %>% update(.~.);
train(lmfulldat$call$formula,method='lm',data=dat3tr
,trControl=trainControl(method='repeatedcv',number=5,repeats=10));
plot(dat3tr$RSR~predict(lmfulldat),xlab='Predicted',ylab='Observed')
#'
pander(lmfulldat,caption=frm_displayFull);