Missing data set "Stock.df" is making a bunch of tests fail.

Running grep -rnw -e 'Stock.df in directory '~/R/FactorAnalytics/R` lists the following files

print.ffm.R:21:#' data(Stock.df)
plot.ffm.R:106:#' data(Stock.df)
fmVaRDecomp.R:80:#' data(Stock.df)
predict.ffm.R:34:#' data(Stock.df)
summary.ffm.R:38:#' data(Stock.df)
fmCov.R:61:#' data(Stock.df)
fmEsDecomp.R:81:#' data(Stock.df)

Missing data set "stocks145scores6" is making a bunch of tests fail, as it doesn't exist.

Running grep -rnw -e 'stocks145scores6 in the R directory (~/R/FactorAnalytics/R) reveals the following list of 9 files that need to be fixed.

portVolDecomp.R:43:#' data("stocks145scores6")
portVolDecomp.R:44:#' dat = stocks145scores6
portVaRDecomp.R:78:#' data("stocks145scores6")
portVaRDecomp.R:79:#' dat = stocks145scores6
portEsDecomp.R:77:#' data("stocks145scores6")
portEsDecomp.R:78:#' dat = stocks145scores6
portSdDecomp.R:71:#' data("stocks145scores6")
portSdDecomp.R:72:#' dat = stocks145scores6
riskDecomp.R:70:#' data("stocks145scores6")
riskDecomp.R:71:#' dat = stocks145scores6
repExposures.R:41:#' data("stocks145scores6")
repExposures.R:42:#' dat = stocks145scores6
tsPlotMP.R:27:#' data("stocks145scores6")
tsPlotMP.R:28:#' dat = stocks145scores6
repReturn.R:39:#' data("stocks145scores6")
repReturn.R:40:#' dat = stocks145scores6
repRisk.R:81:#' data("stocks145scores6")
repRisk.R:82:#' dat = stocks145scores6

Hi! First of all, thank you for making your package available!

I am using the factorDataSetDjia5Yrs data set you shared to fit some fundamental factors models with fitFfm() and I'd like to ask some questions about some variables definitions.

Specifically, on factors, how is MKTCAP computed? Is it inflation-adjusted? Then, how is SIZE calculated?
On assets returns, it seems RETURN is the 'raw' return? What does RETURN.OLD stand for?

I checked the manual and ffmVignette, but wasn't able to find descriptions.

man/FactorAnalytics-package.Rd: non-ASCII input and no declared encoding

prepare_Rd: readGithubFAdata.Rd:12-14: Dropping empty section \value

problem found in ‘FactorAnalytics-package.Rd’

Running grep -rnw last R/*.R | grep xts gets these:

R/fitFfmDT.R:675: residuals1 <- residuals1[, which(!is.na(xts::last(residuals1)))]
R/fitFfmDT.R:677: # resid.var <- resid.var[which(!is.na(xts::last(residuals1)))]

Running grep -rnw last R/*.R gets a lot but here are the interesting files to refactor and look at

R/fitFfM2_rolling.R:54: names(rollind) <- uniqueDates[sapply(rollind, last)]
R/fitFfM2_rolling.R:78: rebalDate <- uniqueDates[last(Tindx[rollind[[i]]])] # names ?
R/fitFfmDT.R:668: resid.cov <- diag(as.numeric(w[last(index(w)),])) # use the last estimate
R/fitFfmDT.R:1178: first(SpecObj$dataDT[[d_]]), last(SpecObj$dataDT[[d_]])))

Running grep -rnw wtsStocks145GmvLo R/*.R returns the following files.

R/datasets.R:133:#' @name wtsStocks145GmvLo
R/datasets.R:139:#' @Usage data("wtsStocks145GmvLo")
R/portEsDecomp.R:84:#' data("wtsStocks145GmvLo")
R/portEsDecomp.R:85:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/portEsDecomp.R:96:#' portEsDecomp(fit.cross, weights = wtsStocks145GmvLo)
R/portSdDecomp.R:78:#' data("wtsStocks145GmvLo")
R/portSdDecomp.R:79:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/portSdDecomp.R:90:#' portSdDecomp(fit.cross, wtsStocks145GmvLo)
R/portVaRDecomp.R:85:#' data("wtsStocks145GmvLo")
R/portVaRDecomp.R:86:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/portVaRDecomp.R:97:#' portVaRDecomp(fit.cross, weights = wtsStocks145GmvLo)
R/portVolDecomp.R:50:#' data("wtsStocks145GmvLo")
R/portVolDecomp.R:51:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/repExposures.R:48:#' data("wtsStocks145GmvLo")
R/repExposures.R:49:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/repExposures.R:57:#' repExposures(fit.cross, wtsStocks145GmvLo, isPlot = FALSE, digits = 4)
R/repExposures.R:58:#' repExposures(fit.cross, wtsStocks145GmvLo, isPrint = FALSE, isPlot = TRUE,
R/repExposures.R:60:#' repExposures(fit.cross, wtsStocks145GmvLo, isPlot = TRUE, which = 1,
R/repExposures.R:62:#' repExposures(fit.cross, wtsStocks145GmvLo, isPrint = FALSE, isPlot = TRUE,
R/repReturn.R:46:#' data("wtsStocks145GmvLo")
R/repReturn.R:47:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/repReturn.R:56:#' repReturn(fit.cross, wtsStocks145GmvLo, isPlot = FALSE, digits = 4)
R/repReturn.R:57:#' repReturn(fit.cross, wtsStocks145GmvLo, isPrint = FALSE, isPlot = TRUE,
R/repReturn.R:59:#' repReturn(fit.cross, wtsStocks145GmvLo, isPrint = FALSE, isPlot = TRUE,
R/repReturn.R:61:#' repReturn(fit.cross, wtsStocks145GmvLo, isPrint = FALSE, isPlot = TRUE,
R/repRisk.R:88:#' data("wtsStocks145GmvLo")
R/repRisk.R:89:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/repRisk.R:99:#' repRisk(fit.cross, wtsStocks145GmvLo, risk = "Sd", decomp = 'FPCR',
R/repRisk.R:102:#' repRisk(fit.cross, wtsStocks145GmvLo, risk = c("VaR", "ES"), decomp = 'FPCR',
R/repRisk.R:105:#' repRisk(fit.cross, wtsStocks145GmvLo, risk = "Sd", decomp = 'FPCR',
R/riskDecomp.R:77:#' data("wtsStocks145GmvLo")
R/riskDecomp.R:78:#' wtsStocks145GmvLo = round(wtsStocks145GmvLo,5)
R/riskDecomp.R:88:#' portES.decomp = riskDecomp(fit.cross, weights = wtsStocks145GmvLo, risk = "ES", portDecomp = TRUE)

Undocumented data sets:

• ‘r.M’
• ‘r.W’
• ‘managers’
• ‘stocksCRSPweekly’

Running examples in ‘FactorAnalytics-Ex.R’ failed
The error most likely occurred in:

Name: chartRobRisk

Title: 'riskBudget' Plots

Aliases: chartRobRisk

** Examples

data("RussellData")
Warning in data("RussellData") : data set ‘RussellData’ not found
rf = RussellData[, 16]
Error: object 'RussellData' not found
Execution halted

vif(fitDjia5Yr, isPlot = T, isPrint = F, lwd = .7,stripText.cex = .8,axis.cex=.8)
Error in [.data.table(ffmObj$data, , exposure.vars, drop = FALSE) :
j (the 2nd argument inside [...]) is a single symbol but column name 'exposure.vars' is not found. Perhaps you intended DT[, ..exposure.vars]. This difference to data.frame is deliberate and explained in FAQ 1.1.

Uses old Stocks.df file. Need to replace with stocksCRSP and scoreSPGMI.

• fitFfm_vignette_04_09.Rnw

The current README has good information, but could benefit from an improved layout, instructions, and content edits. These include but aren't limited to

• Adding relevant badges which communicate various status to the user
• Edit and expand install instructions if needed.
• Update and double check data sources
• A few examples to get folks started right away

Hi, I've tried to install your package using:

devtools::install_github("braverock/FactorAnalytics", ref="master")

It fails, however, with the message (see screenshot below):

I've installed git lfs, as suggested in the package documentation, and so I was wondering if this a bug or something else?

Thanks very much

See

https://github.com/braverock/FactorAnalytics/blob/master/R/fitTsfmMT.R#L111-L115
115

Get the Following Warning on build: invalid input found on input connection '~/R/FactorAnalytics/R/datasets.R'

Related to issue #35, concerning the r/datasets.R file.

Each data set needs to be broken out into separate .R files. Good examples for data set documentation can be found below

• https://github.com/JustinMShea/wooldridge/tree/master/R
• https://github.com/JustinMShea/neverhpfilter/tree/master/R

Stocks.df contains a single data frame named stock. The data stock is used in Sangeetha's fitFfm vignette, which we are not going to use initially, and perhaps never, stock is used as an example in a number of functions as shown in the spreadsheet issuesFAdatasetsFunctions 07 23 21.xlsx, that I will put in the sandbox. I note that Sangeetha gives no source for the stock data frame. There are two possible solutions:

1. Delete Stocks.df/stock, and comment out the example segments in various functions that make use of stock.
2. Create a new data set from stocksCRSP and scoresSPGMI and swap it in wherever Stocks.df appears.

At this point I strongly prefer 1 above, with idea based on commenting out mentioned above, I could come back later and do 2.

For a variety of useful scripts, especially for R scripts for static vignettes.

Do a thorough review of all functions in the R/ folder. Some are obvious for removal, but some functions that were once useful, may no longer be necessary, or should be moved to the sandbox for refactoring.

Similar to #29

Triggered when running the package

Similar to issue #50 , https://github.com/braverock/FactorAnalytics/blob/master/R/fitTsfmUpDn.R#L122-L126.

Also, part of #43 needs further investigation

Name: fitTsfmUpDn

Title: Fit a up and down market factor model using time series

regression

Aliases: fitTsfmUpDn

** Examples

load data

data(managers, package = 'PerformanceAnalytics')

Make syntactically valid column names

colnames(managers)
[1] "HAM1" "HAM2" "HAM3" "HAM4" "HAM5"
[6] "HAM6" "EDHEC LS EQ" "SP500 TR" "US 10Y TR" "US 3m TR"
colnames(managers) <- make.names( colnames(managers))
colnames(managers)
[1] "HAM1" "HAM2" "HAM3" "HAM4" "HAM5"
[6] "HAM6" "EDHEC.LS.EQ" "SP500.TR" "US.10Y.TR" "US.3m.TR"

example: Up and down market factor model with LS fit

fitUpDn <- fitTsfmUpDn(asset.names=colnames(managers[,(1:6)]),mkt.name="SP500.TR", data=managers, fit.method="LS",control=NULL)
Error in do.call(lmrobdet.control, control[m2, drop = TRUE]) :
second argument must be a list
Calls: fitTsfmUpDn -> fitTsfm -> do.call
Execution halted

Undocumented arguments in documentation object 'calcFLAM'
‘specObj’ ‘modelStats’ ‘fitResults’ ‘...’

Undocumented arguments in documentation object 'convert'
‘SpecObj’ ‘FitObj’ ‘RegStatsObj’ ‘...’

Undocumented arguments in documentation object 'convert.ffmSpec'
‘...’

Undocumented arguments in documentation object 'fitFfm'
‘GARCH.MLE’

Undocumented arguments in documentation object 'fitFfmDT'
‘lmrobdet.control.para.list’ ‘...’

Undocumented arguments in documentation object 'tsPlotMP'
‘ret’
Documented arguments not in \usage in documentation object 'tsPlotMP':
‘data’

Functions with \usage entries need to have the appropriate \alias
entries, and all their arguments documented.
The \usage entries must correspond to syntactically valid R code.
See chapter ‘Writing R documentation files’ in the ‘Writing R
Extensions’ manual.

Various plots no longer work, debug/troubleshoot @sangeeuw

Error in read.dcf(path) :
Found continuation line starting ' time series, fun ...' at begin of record.
10. read.dcf(path)
9. is.data.frame(x)
8. colnames(read.dcf(path))
7. read_dcf(tmp)
6. remote_package_name.github_remote(remote)
5. remote_package_name(remote)
4. FUN(X[[i]], ...)
3. vapply(remotes, install_remote, ..., FUN.VALUE = character(1))
2.
install_remotes(remotes, auth_token = auth_token, host = host,
dependencies = dependencies, upgrade = upgrade, force = force,
quiet = quiet, build = build, build_opts = build_opts, repos = repos,
type = type, ...)
1.
devtools::install_github("braverock/factorAnalytics")

Data with usage in documentation object 'CommonFactors' but not in code:
‘CommonFactors’

Data with usage in documentation object 'RussellData' but not in code:
‘RussellData’

Data with usage in documentation object 'factorDataSPGMI' but not in code:
‘factorDataSPGMI’

Data with usage in documentation object 'factorDataSetDjia14Yrs' but not in code:
‘factorDataSetDjia’

Data with usage in documentation object 'scoresSPGMIraw' but not in code:
‘scoresSPGMIraw’

Data with usage in documentation object 'stocksCRSPscoresSPGMI' but not in code:
‘stocksCRSPscoresSPGMI’

Data with usage in documentation object 'stocksCRSPscoresSPGMIraw' but not in code:
‘stocksCRSPscoresSPGMIraw’

Data with usage in documentation object 'stocksWithFactorsData300' but not in code:
‘stocksWithFactorsData300’

Data with usage in documentation object 'wtsDjiaGmv' but not in code:
‘wtsDjiaGmv’

Data with usage in documentation object 'wtsStocks145Gmv' but not in code:
‘wtsStocks145Gmv’

Justin and I agreed to put the fitSfm vignette and all related functions and data sets in the sandbox, with to goal of adding this very nice capability back into FA in the second CRAN release.

1. Note: possible error in 'lmrobdetMM(data = x, ': unused arguments (mxr = 200, mxf = 200, mxs = 200)
2. Note: possible error in 'standardizeExposures(obj = rollingObject, ': unused argument (obj = rollingObject)
3. Note: possible error in 'extractRegressionStats(obj = rollingObject, ': unused argument (obj = rollingObject)

The error most likely occurred in:

Name: fitTsfmLagLeadBeta

Title: Fit a lagged and lead Betas factor model using time series

regression

Aliases: fitTsfmLagLeadBeta

** Examples

load data from the database

data(managers, package = 'PerformanceAnalytics')

example: A lagged Beetas model with LS fit

fit <- fitTsfmLagLeadBeta(asset.names=colnames(managers[,(1:6)]),LagLeadBeta=2,LagOnly=TRUE, mkt.name="SP500 TR",rf.name="US 3m TR",data=managers)
Error in [.xts(data.xts, , factor.names) : subscript out of bounds
Calls: fitTsfmLagLeadBeta -> fitTsfm -> merge -> merge.xts -> [ -> [.xts
Execution halted

The current stocksCRSPweekly.rda file in the folder data_extra does not have all the components needed. See the yellow-highlighted text in the updated file FactorAnalytics Data 08 15 21.docx in the sandbox

Among the current FA attached packages, I tentatively believe that rrcov and robustbase should not be needed, at least not needed as attached packages. But likely the RobStatTM should be an attached package because its lmrobdetMM() function, among other, are needed as robust regression options for fitTsfm and fitFfm. Consquently, Justin will delete them as attached packages and we will see what happens.

As for packages loaded via a namespace, there are currently 118, which seems way over the top. So we need to review which ones of these we really need, and pair the list down as much as possible.

plot(fit.sub, plot.single=TRUE, asset.name="HAM1")

Make a plot selection (or 0 to exit):

1: Actual and fitted asset returns (not working)
2: Actual vs fitted asset returns
3: Residuals vs fitted asset returns
4: Sqrt. of modified residuals vs fitted
5: Residuals with standard error bands (not working)
6: Time series of squared residuals (not working)
7: Time series of absolute residuals (not working)
8: SACF and PACF of residuals
9: SACF and PACF of squared residuals
10: SACF and PACF of absolute residuals
11: Non-parametric density of residuals with normal overlaid
12: Non-parametric density of residuals with skew-t overlaid
13: Histogram of residuals with non-parametric density and normal overlaid
14: QQ-plot of residuals
15: CUSUM test-Recursive residuals
16: CUSUM test-LS residuals
17: Recursive estimates (RE) test of LS regression coefficients
18: Rolling regression over a 24-period observation window
19: Asset returns vs factor returns (single factor model)

@JustinMShea As the title, I had an install error when running devtools::install_github("braverock/FactorAnalytics"). (After the merge conflict in #41 is resolved.)

* installing *source* package ‘FactorAnalytics’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
Warning: file ‘StockReturns.rda’ has magic number 'versi'
  Use of save versions prior to 2 is deprecated
Error in load(zfile, envir = tmp_env) : 
  bad restore file magic number (file may be corrupted) -- no data loaded
ERROR: lazydata failed for package ‘FactorAnalytics’

Removing the LazyData: true in the DESCRIPTION allows the package to be installed, but I'm not sure if this will mess up the compression.

It's not really helpful, but a similar issue here for reference: carlanetto/M4comp2018#1.

Much more stable, eliminates issues related to sprawling dependencies that have cropped up in the past with testthat.

https://github.com/markvanderloo/tinytest

Fix test issues related to these missing data -R CMD check ERROR
── Error (test-repExposures.R:3:1): (code run outside of test_that()) ────────
Error: object 'stocks145scores6' not found
── Error (test-repReturn.R:4:1): (code run outside of test_that()) ───────────
Error: object 'stocks145scores6' not found
── Error (test-tsPlotMP.R:4:1): (code run outside of test_that()) ────────────
Error: object 'stocks145scores6' not found

Hi, I followed the steps to install "FactorAnalytics" and "ExpectedReturns".

However, when I run the following code chunk:
library(ExpectedReturns)
data("FF3.monthly")
data("factorDataSetDjia5Yrs")

"FF3.monthly" can be correctly loaded.
"factorDataSetDjia5Yrs" will give the following error.

Error in load(zfile, envir = tmp_env) : 
  bad restore file magic number (file may be corrupted) -- no data loaded
In addition: Warning message:
file ‘factorDataSetDjia5Yrs.rda’ has magic number 'versi'
  Use of save versions prior to 2 is deprecated 

My environment is R = 4.1.0, RStudio-1.4.1717 （windows 10）.

Thanks and look forward.

This functionality is really very nice, as is Sangeetha's vignette, and the R script for the vignette seems to run without errors (maybe one warning). So it is highly desirable to leave the functionality in the FA package. The problem is that we need adequate sources for all of the following data:

StockReturns.rda which contains the 120 x 15 data frame r.M, and the 182 x 1618 data frame r.W, and TreasuryYields.rda, which contains only the 4714 x 11 xts object tr.yields.

Brian and Justin, it will be great if you could review the information in the data manual pages, and let Sangeetha and I know what is adequate, and what is not adequate, and what Sangeetha needs to provide, if we are going to keep the data.

plot.ffm: no visible binding for global variable ‘TICKER’
plot.ffm: no visible global function definition for ‘chart.Boxplot’
plot.ffm: no visible global function definition for ‘corrplot.mixed’
plot.tsfm: no visible global function definition for ‘addLegend’
plot.tsfm: no visible global function definition for ‘efp’
plot.tsfm: no visible global function definition for ‘rollapply’
plot.tsfm: no visible global function definition for ‘corrplot.mixed’

Undefined global functions or variables:
TICKER addLegend chart.Boxplot corrplot.mixed efp rollapply

We need to decide whether or not to keep fitFfm in view of the fact that FactorAnalytics benefits greatly from Mido's addition the fitFfmDT data.table version. Just and I discussed this and believe that the best route would be to keep fitFfm by adding an optional argument for specifying whether or not the fitFfmDT version is used. My suggestion is the default DT = TRUE. We should discuss pro's and con's with Mido, come to a consensus among the three of us, and then get Brian's thoughts.

For now, use static PDFs and follow Mark van der Loo's guidance here

http://www.markvanderloo.eu/yaRb/2019/01/11/add-a-static-pdf-vignette-to-an-r-package/

convert:
function(x, ...)
convert.ffmSpec:
function(SpecObj, FitObj, RegStatsObj)

print:
function(x, ...)
print.ffmSpec:
function(SpecObj)

See section ‘Generic functions and methods’ in the ‘Writing R
Extensions’ manual.

Check all data transforms in the sandbox/CRSP folder. switch transforms to xts and related functions if needed in freqChange.R. Recreate data sets created and marked with "Dan's code" if needed. These include

• Risk-free montly.csv
• risk-free weekly.csv

The problem is due to a change in how data.table inherits data.frame features.

fitSfm and the vignette that Sangeetha wrote are very nice, and it will be great to have them in FA. The fitSfm vignette currently uses the following data sets:
StockReturns
r.M originally from Berndt (1991)
r.W source S+FinMetrics Berndt.dat & folio.dat

TreasuryYields
tr.yields text file from companion web site to Ruppert (2010)

We need to replace the above data sets with ones for which we are free to use. For the r.M (monthly) and r.W (weekly) stock data, we can use stocksCRSP. And we can replace tr.yields with identical or similar ones from FRED. This requires revision of the vignette and likely examples used for other functions. I think we should proceed using those data sets to see if there is anything currently broken, and if there is nothing broken, or broken things very easily fixed, and it does cause a build error or warning, then we are in good shape, and only have to replace the data sets, with the attendant revision of the fitSfm vignette and various examples that use the data, and if everything works, it will be justified to make that effort.

Related to #56. This will give us an opportunity to review all imports, and decide what is best to move to "suggests", "imports", or "depends" on the description file.

Manual page examples to be removed from factor model risk decomposition
functions for now, due to removal of data sets for the examples, and saved
here for adding back once new data sets are provided for these examples

fmSdDecomp.R:

Statistical Factor Model:

data(StockReturns)
sfm.pca.fit <- fitSfm(r.M, k=2)
decomp <- fmSdDecomp(sfm.pca.fit)
decomp$pcSd

fmVaRDecomp.R:

Statistical Factor Model:

data(StockReturns)
sfm.pca.fit <- fitSfm(r.M, k=2)
VaR.decomp <- fmVaRDecomp(sfm.pca.fit, type="normal")
VaR.decomp$cVaR

Fundamental Factor Model:

data(Stocks.df)
exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP")
fit <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN",
date.var="DATE", exposure.vars=exposure.vars)
VaR.decomp <- fmVaRDecomp(fit, type="normal")
VaR.decomp$cVaR

fmESDecomp.R:

Statistical Factor Model:

data(StockReturns)
sfm.pca.fit <- fitSfm(r.M, k=2)
ES.decomp <- fmEsDecomp(sfm.pca.fit, type="normal")
ES.decomp$cES

Fundamental Factor Model:

data(Stocks.df)
exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP")
fit <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN",
date.var="DATE", exposure.vars=exposure.vars)
ES.decomp <- fmEsDecomp(fit, type="normal")
head(ES.decomp$cES)

fmCov.R

Statistical Factor Model:

data(StockReturns)
sfm.pca.fit <- fitSfm(r.M, k=2)
fmCov(sfm.pca.fit)

Fundamental factor Model:

data(Stocks.df)
exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP", "GICS.SECTOR")
fit2 <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN",
date.var="DATE", exposure.vars=exposure.vars)
fmCov(fit2)

summary.ffm

#' data(Stocks.df)
#' exposure.vars <- c("BOOK2MARKET", "LOG.MARKETCAP", "GICS.SECTOR")
#' fit2 <- fitFfm(data=stock, asset.var="TICKER", ret.var="RETURN",
#' date.var="DATE", exposure.vars=exposure.vars)
#'
#' # summary of factor returns estimated in each time period
#' summary(fit2)
#'
#' # summary of lm fit for a single period
#' summary(fit2$factor.fit[[1]])

Running grep -rnw managers R/*.R returns the following

R/cusumActMgr.R:9:#' Assessing the performance of the active managers is hard because active returns (i.e. portfolio return - benchmark return) are noisy.
R/cusumActMgr.R:11:#' the standard deviation of active returns. Empirical studies of active managers across a wide range of asset classes suggest that an
R/cusumActMgr.R:14:#' inefficiencies sometimes appear in their place. Consequently, the majority of active managers deliver active returns and IR close to 0, and even
R/fitTsfm.control.R:168:#' data(managers)
R/fitTsfm.control.R:169:#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fitTsfm.control.R:170:#' factor.names=colnames(managers[,(7:9)]),
R/fitTsfm.control.R:171:#' data=managers, variable.selection="subsets",
R/fitTsfm.R:126:#' data(managers)
R/fitTsfm.R:127:#' colnames(managers) = gsub(" ",'.',colnames(managers))
R/fitTsfm.R:129:#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fitTsfm.R:130:#' factor.names=colnames(managers[,(7:9)]), data=managers)
R/fitTsfm.R:141:#' fit.sub <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fitTsfm.R:142:#' factor.names=colnames(managers[,(7:9)]),
R/fitTsfm.R:143:#' data=managers, variable.selection="subsets",
R/fitTsfm.R:147:#' fit.lar <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fitTsfm.R:148:#' factor.names=colnames(managers[,(7:9)]),
R/fitTsfm.R:149:#' rf.name="US.3m.TR", data=managers,
R/fmCov.R:50:#' data(managers)
R/fmCov.R:51:#' fit <- fitTsfm(asset.names=colnames(managers[, (1:6)]),
R/fmCov.R:52:#' factor.names=c("EDHEC.LS.EQ","SP500.TR"), data=managers)
R/fmEsDecomp.R:67:#' data(managers)
R/fmEsDecomp.R:68:#' fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fmEsDecomp.R:69:#' factor.names=colnames(managers[,(7:8)]), data=managers)
R/fmmcSemiParam.R:61:#' data(managers)
R/fmmcSemiParam.R:62:#' fit <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fmmcSemiParam.R:63:#' factor.names=colnames(managers[,(7:9)]), data=managers)
R/fmmcSemiParam.R:67:#' fmmc.returns <- fmmcSemiParam(factor.ret=managers[,(7:9)], beta=fit$beta,
R/fmmcSemiParam.R:73:#' rownames(resid.par) <- colnames(managers[,(1:6)])
R/fmmcSemiParam.R:74:#' fmmc.returns.CF <- fmmcSemiParam(factor.ret=managers[,(7:9)], beta=fit$beta,
R/fmmcSemiParam.R:81:#' rownames(resid.par) <- colnames(managers[,(1:6)])
R/fmmcSemiParam.R:82:#' fmmc.returns.skewt <- fmmcSemiParam(factor.ret=managers[,(7:9)],
R/fmSdDecomp.R:58:#' data(managers)
R/fmSdDecomp.R:59:#' fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fmSdDecomp.R:60:#' factor.names=colnames(managers[,(7:9)]),
R/fmSdDecomp.R:61:#' rf.name="US.3m.TR", data=managers)
R/fmVaRDecomp.R:66:#' data(managers)
R/fmVaRDecomp.R:67:#' fit.macro <- fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/fmVaRDecomp.R:68:#' factor.names=colnames(managers[,(7:8)]), data=managers)
R/portEsDecomp.R:62:#' data(managers)
R/portEsDecomp.R:63:#' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/portEsDecomp.R:64:#' factor.names=colnames(managers[,(7:9)]),
R/portEsDecomp.R:65:#' rf.name=colnames(managers[,10]), data=managers)
R/portEsDecomp.R:73:#' names(wts) <- colnames(managers)[1:6]
R/portSdDecomp.R:56:#' data(managers)
R/portSdDecomp.R:57:#' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/portSdDecomp.R:58:#' factor.names=colnames(managers[,(7:9)]),
R/portSdDecomp.R:59:#' rf.name=colnames(managers[,10]), data=managers)
R/portSdDecomp.R:67:#' names(wts) <- colnames(managers)[1:6]
R/portVaRDecomp.R:62:#' data(managers)
R/portVaRDecomp.R:63:#' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(manager[,(1:6)]),
R/portVaRDecomp.R:64:#' factor.names=colnames(managers[,(7:9)]),
R/portVaRDecomp.R:65:#' rf.name=colnames(managers[,10]), data=manager)
R/portVaRDecomp.R:73:#' names(wts) <- colnames(managers)[1:6]
R/portVolDecomp.R:35:#' data(managers)
R/portVolDecomp.R:36:#' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(manager[,(1:6)]),
R/portVolDecomp.R:37:#' factor.names=colnames(managers[,(7:9)]),
R/portVolDecomp.R:38:#' rf.name=colnames(managers[,10]), data=manager)
R/repRisk.R:68:#' data(managers)
R/repRisk.R:69:#' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/repRisk.R:70:#' factor.names=colnames(managers[,(7:9)]),
R/repRisk.R:71:#' rf.name=colnames(managers[,10]), data=managers)
R/riskDecomp.R:52:#' data(managers)
R/riskDecomp.R:53:#' fit.macro <- FactorAnalytics::fitTsfm(asset.names=colnames(managers[,(1:6)]),
R/riskDecomp.R:54:#' factor.names=colnames(managers[,(7:9)]),
R/riskDecomp.R:55:#' rf.name=colnames(managers[,10]), data=managers)
R/riskDecomp.R:64:#' names(wts) <- colnames(managers)[1:6]

Conversations with Doug Martin indicats the following minor fixes

• Transform GICS to char from factor
• Replace missing values - with NA

Replace fitFfm with fitFfmDT. See 'mido_versionOfSangeetha_fitFfmVignette.R' written by @midoshammaa

We have reduce the main data/ to half the size, but we need to get it under 5 Mb, or lobby for an exception.

sub-directories of 1Mb or more:
data 7.0Mb

Justin and I discussed that these functions should be options in the function fitTsfm.
Probably all three of these functions can go there as is, but we should check the option arguments of each, and how we pass them, as an immediate priority.

Also, fitTsfmLabLeadBeta currently implements the Dimson method without the correction of bias due to serial correlation in the market returns, and that needs to be added as an option, and furthermore the thought is to also enhance this thin trading beta capability to include the Scholes-Williams method.