I'm trying to run the lmrob() function in the R package robustbase, using data from a mixed factorial design study. My dependent variable is repeated (pre+ post) and I have two independent variables. I'm entering the post-measurement as the dependent variable, and controlling for the pre-measurement.
The function I'm trying to run is basically:
lmrob(Post ~ pre +IV1 + IV2, setting = "KS2014", data=mydataframe)
When I run this, I get an error message saying:
S-estimated scale == 0: Probably exact fit; check your data.
When I don't control for the pre-measurement, I don't get this warning message. So I assume it has something to do with the repeated measures being correlated? Does anyone have any idea what might be causing the issue and how to resolve it?
This is the first time I'm posting here so please bear with me if I've not included enough detail or my question isn't clear enough, I will provide clarification if needed! I've copied in the output from the console, the session info, and the relevant extract from the dataset below.
Call:
lmrob(formula = Post_Intention ~ TPB_vs_no_TPB + Tailored_vs_Untailored +
Pre_Intention, data = dfa, setting = "KS2014")
\--> method = "S"
Residuals:
Min 1Q Median 3Q Max
-80 0 0 10 90
Exact fit detected
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0 0 NA NA
TPB_vs_no_TPBNo TPB 0 0 NA NA
Tailored_vs_UntailoredUntailored 0 0 NA NA
Pre_Intention 1 0 NA NA
Robustness weights:
88 observations c(1,5,10,13,16,19,21,25,27,28,29,31,32,34,36,40,42,50,53,56,57,61,66,68,75,79,83,86,87,93,98,104,108,112,116,119,124,125,128,134,135,136,138,139,144,145,148,153,156,161,164,165,171,173,175,176,177,179,182,184,190,193,197,198,199,203,213,215,216,217,218,219,220,226,232,233,235,236,239,241,242,243,245,246,247,250,251,252)
are outliers with |weight| = 0 ( < 0.0004);
165 weights are ~= 1.
Algorithmic parameters:
tuning.chi1 tuning.chi2 tuning.chi3 tuning.chi4 bb tuning.psi1 tuning.psi2
-5.000e-01 1.500e+00 NA 5.000e-01 5.000e-01 -5.000e-01 1.500e+00
tuning.psi3 tuning.psi4 refine.tol rel.tol solve.tol eps.outlier eps.x
9.500e-01 NA 1.000e-07 1.000e-07 1.000e-07 3.953e-04 1.819e-10
warn.limit.reject warn.limit.meanrw
5.000e-01 5.000e-01
nResample max.it best.r.s k.fast.s k.max maxit.scale trace.lev mts
1000 500 20 2 2000 200 0 1000
compute.rd fast.s.large.n
0 2000
setting psi subsampling cov compute.outlier.stats
"KS2014" "lqq" "nonsingular" ".vcov.w" "SMDM"
seed : int(0)
> source('~/R scripts/lmrob.R')
re-encoding from UTF-8
Warning message:
In lmrob.S(x, y, control = control, mf = mf) :
S-estimated scale == 0: Probably exact fit; check your data
> sessionInfo
function (package = NULL)
{
z <- list()
z$R.version <- R.Version()
z$platform <- z$R.version$platform
if (nzchar(.Platform$r_arch))
z$platform <- paste(z$platform, .Platform$r_arch, sep = "/")
z$platform <- paste0(z$platform, " (", 8 * .Machine$sizeof.pointer,
"-bit)")
z$locale <- Sys.getlocale()
if (.Platform$OS.type == "windows") {
z$running <- win.version()
}
else if (nzchar(Sys.which("uname"))) {
uname <- system("uname -a", intern = TRUE)
os <- sub(" .*", "", uname)
z$running <- switch(os, Linux = if (file.exists("/etc/os-release")) {
tmp <- readLines("/etc/os-release")
t2 <- if (any(startsWith(tmp, "PRETTY_NAME="))) sub("^PRETTY_NAME=",
"", grep("^PRETTY_NAME=", tmp, value = TRUE)[1L]) else if (any(startsWith(tmp,
"NAME"))) sub("^NAME=", "", grep("^NAME=", tmp,
value = TRUE)[1L]) else "Linux (unknown distro)"
sub("\\"(.*)\\"", "\\\\1", t2)
} else if (file.exists("/etc/system-release")) {
readLines("/etc/system-release")
}, Darwin = {
ver <- readLines("/System/Library/CoreServices/SystemVersion.plist")
ind <- grep("ProductUserVisibleVersion", ver)
ver <- ver[ind + 1L]
ver <- sub(".*<string>", "", ver)
ver <- sub("</string>$", "", ver)
ver1 <- strsplit(ver, ".", fixed = TRUE)[[1L]][2L]
sprintf("%s %s %s", ifelse(as.numeric(ver1) < 12,
"OS X", "macOS"), switch(ver1, `6` = "Snow Leopard",
`7` = "Lion", `8` = "Mountain Lion", `9` = "Mavericks",
`10` = "Yosemite", `11` = "El Capitan", `12` = "Sierra",
`13` = "High Sierra", ""), ver)
}, SunOS = {
ver <- system("uname -r", intern = TRUE)
paste("Solaris", strsplit(ver, ".", fixed = TRUE)[[1L]][2L])
}, uname)
}
if (is.null(package)) {
package <- grep("^package:", search(), value = TRUE)
keep <- vapply(package, function(x) x == "package:base" ||
!is.null(attr(as.environment(x), "path")), NA)
package <- .rmpkg(package[keep])
}
pkgDesc <- lapply(package, packageDescription, encoding = NA)
if (length(package) == 0)
stop("no valid packages were specified")
basePkgs <- sapply(pkgDesc, function(x) !is.null(x$Priority) &&
x$Priority == "base")
z$basePkgs <- package[basePkgs]
if (any(!basePkgs)) {
z$otherPkgs <- pkgDesc[!basePkgs]
names(z$otherPkgs) <- package[!basePkgs]
}
loadedOnly <- loadedNamespaces()
loadedOnly <- loadedOnly[!(loadedOnly %in% package)]
if (length(loadedOnly)) {
names(loadedOnly) <- loadedOnly
pkgDesc <- c(pkgDesc, lapply(loadedOnly, packageDescription))
z$loadedOnly <- pkgDesc[loadedOnly]
}
z$matprod <- as.character(options("matprod"))
es <- extSoftVersion()
z$BLAS <- as.character(es["BLAS"])
z$LAPACK <- La_library()
class(z) <- "sessionInfo"
z
}
<bytecode: 0x0000000017538350>
<environment: namespace:utils>
Dataset
dput(dfa)
structure(list(Pre_Intention = c(50, 10, 50, 100, 80, 100, 10,
0, 100, 50, 90, 100, 50, 100, 100, 50, 100, 100, 30, 0, 10, 0,
60, 10, 0, 0, 0, 0, 40, 10, 50, 0, 70, 50, 50, 20, 0, 50, 0,
70, 50, 60, 0, 100, 50, 100, 100, 100, 0, 50, 0, 10, 0, 60, 0,
50, 50, 20, 100, 100, 50, 100, 100, 0, 60, 20, 100, 10, 50, 100,
100, 100, 0, 50, 10, 0, 80, 50, 30, 100, 100, 80, 40, 100, 0,
10, 20, 100, 100, 100, 100, 0, 50, 100, 70, 100, 40, 20, 100,
100, 100, 100, 0, 50, 100, 100, 0, 50, 50, 90, 20, 50, 10, 40,
50, 20, 50, 0, 0, 100, 100, 10, 10, 80, 10, 100, 80, 50, 100,
10, 100, 100, 90, 50, 50, 50, 100, 50, 40, 100, 100, 40, 20,
10, 10, 20, 100, 20, 100, 0, 100, 0, 30, 0, 10, 10, 40, 0, 40,
0, 50, 0, 0, 30, 0, 0, 50, 0, 0, 0, 0, 10, 0, 0, 10, 10, 20,
50, 20, 100, 50, 20, 90, 20, 100, 70, 20, 90, 10, 20, 80, 100,
90, 100, 100, 100, 10, 10, 20, 0, 60, 0, 10, 100, 100, 100, 10,
10, 0, 20, 0, 0, 40, 0, 30, 0, 40, 70, 0, 0, 30, 40, 0, 10, 20,
20, 50, 10, 10, 0, 0, 10, 0, 50, 0, 40, 0, 20, 0, 0, 20, 100,
0, 100, 30, 0, 50, 70, 10, 0, 0, 30, 10), Post_Intention = c(70,
10, 50, 100, 0, 100, 10, 0, 100, 90, 90, 100, 100, 100, 100,
80, 100, 100, 70, 0, 30, 0, 60, 10, 10, 0, 40, 20, 70, 10, 80,
40, 70, 40, 50, 30, 0, 50, 0, 100, 50, 100, 0, 100, 50, 100,
100, 100, 0, 60, 0, 10, 40, 60, 0, 100, 80, 20, 100, 100, 90,
100, 100, 0, 60, 30, 100, 0, 50, 100, 100, 100, 0, 50, 20, 0,
80, 50, 100, 100, 100, 80, 50, 100, 0, 0, 30, 100, 100, 100,
100, 0, 60, 100, 70, 100, 40, 30, 100, 100, 100, 100, 0, 100,
100, 100, 0, 100, 50, 90, 20, 70, 10, 40, 50, 50, 50, 0, 10,
100, 100, 10, 10, 20, 40, 100, 80, 80, 100, 10, 100, 100, 90,
80, 70, 70, 100, 80, 50, 100, 100, 40, 20, 100, 50, 20, 100,
30, 100, 0, 100, 0, 60, 0, 10, 20, 40, 0, 40, 0, 60, 0, 0, 10,
10, 0, 50, 0, 0, 0, 30, 10, 30, 0, 0, 30, 10, 50, 80, 100, 50,
50, 90, 0, 100, 70, 20, 90, 10, 30, 80, 100, 100, 100, 100, 100,
20, 20, 40, 0, 60, 0, 90, 100, 100, 100, 10, 10, 0, 20, 0, 0,
60, 0, 50, 20, 70, 80, 20, 20, 30, 40, 0, 10, 20, 40, 50, 10,
10, 0, 0, 20, 60, 50, 10, 60, 0, 20, 50, 0, 60, 70, 10, 100,
40, 30, 100, 70, 10, 30, 20, 40, 10), TPB_vs_no_TPB = structure(c(1L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L), .Label = c("TPB",
"No TPB"), class = "factor"), Tailored_vs_Untailored = structure(c(2L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L), .Label = c("Tailored",
"Untailored"), class = "factor")), .Names = c("Pre_Intention",
"Post_Intention", "TPB_vs_no_TPB", "Tailored_vs_Untailored"), class = "data.frame", row.names = c(NA,
-253L))
Best Answer
The initial S estimate is based on random sampling. It has itself quite a few tuning parameters, see help(lmrob.control) and probably ?lmrob.S . As @mdewey was also thinking, it could be that too many subsamples (of the random sample) gave perfect fits. I (as maintainer of
robustbase) would be happy to investigate in more detail (when back at work), but for that I (or others helping) need to be able to get your data (or subset of your data which gives the same error). If it's small enough, you could use todput(<dataframe>)and paste the result here.Even before that you could try to use the currently most recommended
setting="KS2014"and see if it helps (it already tries to use "better" S estimate tuning). Last but not least: Are you using the most current version ofrobustbase?