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I’m trying to write simulation code, that generates data and runs t-test selection (discarding those predictors whose t-test p-value exceeds 0.05, retaining the rest) on it. The simulation is largely an adaptation of Applied Econometrics with R by Kleiber and Zeileis (2008, pp. 183–189).
When running the code, it usually fails. Yet with certain seeds (e.g. 1534) it produces plausible output. If it does not produce output (e.g. 1911), it fails due to: "Error in x[, ii] : subscript out of bounds", which traces back to na.omit.data.frame(). So, for some reason, the way I attempt to handle the NAs seems to fail, but I'm unable to figure out in how so.
coef <- rep(coef[,3], length.out = pdim+1)
err <- as.vector(rnorm(nobs, sd = sd))
uX <- c(rep(1, times = nobs))
pX <- matrix(scale(rnorm(nobs)), byrow = TRUE, ncol = pdim, nrow = nobs)
X <- cbind(uX, pX)
y <- coef %*% t(X) + err
y <- matrix(y)
tTp <- (summary(lm(y ~ pX)))$coefficients[,4]
tTp <- tTp[2:length(tTp)]
TTT <- matrix(c(tTp, rep(.7, ncol(pX)-length(tTp))))
tX <- matrix(NA, ncol = ncol(pX), nrow = nrow(pX))
for(i in 1:ncol(pX)) {ifelse(TTT[i,] < ALPHA, tX[,i] <- pX[,i], NA)}
tX <- matrix(Filter(function(x)!all(is.na(x)), tX), nrow = nobs)
TTR <- lm(y ~ tX)
The first block is unlikely to the cause of the error. It merely generates the data and works well on its own and with other methods, like PCA, as well. The second block pulls the p-values from the regression output; removes the p-value of the intercept (beta_0); and fills the vector with as many 7s as necessary to have the same length as the number of variables, to ensure the same dimension for matrix calculations. Seven is arbitrary and could be any number larger than 0.05 to not pass the test of the loop. This becomes – I believe – necessary, if R discards predictors due to multicollinearity.
The final block creates an empty matrix of the original dimensions; inserts the original data, if the t-test p-value is lower than 0.05, else retains the NA; while the penultimate line removes all columns containing NAs ((exclusively NA or one NA is the same here) taken from mnel’s answer to Remove columns from dataframe where ALL values are NA); lastly, the modified data is again put in the shape of a linear regression.
Does anyone know what causes this behavior or how it would work as intended? I would expect it to either work or not, but not kind of both. Ideally, the former.
A working version of the code is:
set.seed(1534)
Sim_TTS <- function(nobs = c(1000, 15000), pdim = pdims, coef = coef100,
model = c("MLC", "MHC"), ...){
DGP_TTS <- function(nobs = 1000, model = c("MLC", "MHC"), coef = coef100,
sd = 1, pdim = pdims, ALPHA = 0.05)
{
model <- match.arg(model)
if(model == "MLC") {
coef <- rep(coef[,1], length.out = pdim+1)
err <- as.vector(rnorm(nobs, sd = sd))
uX <- c(rep(1, times = nobs))
pX <- matrix(scale(rnorm(nobs)), byrow = TRUE, ncol = pdim, nrow = nobs)
X <- cbind(uX, pX)
y <- coef %*% t(X) + err
y <- matrix(y)
tTp <- (summary(lm(y ~ pX)))$coefficients[,4]
tTp <- tTp[2:length(tTp)]
TTT <- matrix(c(tTp, rep(.7, ncol(pX)-length(tTp))))
tX <- matrix(NA, ncol = ncol(pX), nrow = nrow(pX))
for(i in 1:ncol(pX)) {ifelse(TTT[i,] < ALPHA, tX[,i] <- pX[,i], NA)}
tX <- matrix(Filter(function(x)!all(is.na(x)), tX), nrow = nobs)
TTR <- lm(y ~ tX)
} else {
coef <- rep(coef[,2], length.out = pdim+1)
err <- as.vector(rnorm(nobs, sd = sd))
uX <- c(rep(1, times = nobs))
pX <- matrix(scale(rnorm(nobs)), byrow = TRUE, ncol = pdim, nrow = nobs)
X <- cbind(uX, pX)
y <- coef %*% t(X) + err
y <- matrix(y)
tTp <- (summary(lm(y ~ pX)))$coefficients[,4]
tTp <- tTp[2:length(tTp)]
TTT <- matrix(c(tTp, rep(.7, ncol(pX)-length(tTp))))
tX <- matrix(NA, ncol = ncol(pX), nrow = nrow(pX))
for(i in 1:ncol(pX)) {ifelse(TTT[i,] < ALPHA, tX[,i] <- pX[,i], NA)}
tX <- matrix(Filter(function(x)!all(is.na(x)), tX), nrow = nobs)
TTR <- lm(y ~ tX)
}
return(TTR)
}
PG_TTS <- function(nrep = 1, ...)
{
rsq <- matrix(rep(NA, nrep), ncol = 1)
rsqad <- matrix(rep(NA, nrep), ncol = 1)
pastr <- matrix(rep(NA, nrep), ncol = 1)
vmat <- cbind(rsq, rsqad, pastr)
colnames(vmat) <- c("R sq.", "adj. R sq.", "p*")
for(i in 1:nrep) {
vmat[i,1] <- summary(DGP_TTS(...))$r.squared
vmat[i,2] <- summary(DGP_TTS(...))$adj.r.squared
vmat[i,3] <- length(DGP_TTS(...)$coefficients)-1
}
return(c(mean(vmat[,1]), mean(vmat[,2]), round(mean(vmat[,3]))))
}
SIM_TTS <- function(...)
{
prs <- expand.grid(pdim = pdim, nobs = nobs, model = model)
nprs <- nrow(prs)
pow <- matrix(rep(NA, 3 * nprs), ncol = 3)
for(i in 1:nprs) pow[i,] <- PG_TTS(pdim = prs[i,1],
nobs = prs[i,2], model = as.character(prs[i,3]), ...)
rval <- rbind(prs, prs, prs)
rval$stat <- factor(rep(1:3, c(nprs, nprs, nprs)),
labels = c("R sq.", "adj. R sq.", "p*"))
rval$power <- c(pow[,1], pow[,2], pow[,3])
rval$nobs <- factor(rval$nobs)
return(rval)
}
psim_TTS <- SIM_TTS()
tab_TTS <- xtabs(power ~ pdim + stat + model + nobs, data = psim_TTS)
ftable(tab_TTS, row.vars = c("model", "nobs", "stat"), col.vars = "pdim")}
FO_TTS <- Sim_TTS()
FO_TTS
}
Preceeded by:
pdims <- seq(12, 100, 4)
coefLC12 <- c(0, rep(0.2, 4), rep(0.1, 4), rep(0, 4))/1.3
rtL <- c(0.2, rep(0, 3))/1.3
coefLC100 <- c(coefLC12, rep(rtL, 22))
coefHC12 <- c(0, rep(0.8, 4), rep(0.4, 4), rep(0, 4))/1.1
rtH <- c(0.8, rep(0, 3))/1.1
coefHC100 <- c(coefHC12, rep(rtH, 22))
coef100 <- cbind(coefLC100, coefHC100)
I’m aware that model selection via the significance of individual predictors is not recommended, but that is the whole point – it is meant to be compared to more sophisticated methods.
I want to acquire the optimized allocation of a set of the asset, so I use the package fPortfolio and BLCOP. Here is my code,
library(xts);library(fPortfolio);library(BLCOP)
sh_return <- xts(ret[,-1],order.by = as.Date(ret[,1]))
prior_mean <- colMeans(sh_return)
prior_mean
prior_cov_matrix <- cov(sh_return)
## onstruct the confidenec interval
pick_matrix <- matrix(0,2,ncol(sh_return))
colnames(pick_matrix) <- colnames(sh_return)
pick_matrix[1,1:4] <- 1
pick_matrix[2,c(1,2,5,ncol(sh_return)-1)] <- c(0.5,0.6,-1,0.8)
pick_matrix
# views
q <- c(0.4,0.32)
confidence <- c(90,95)
views <- BLViews(pick_matrix,q,confidence,assetNames = colnames(sh_return))
views
# posterior
tau <- 0.3
posterior <- posteriorEst(views,tau=tau,prior_mean,prior_cov_matrix)
# optimazation
optimal_portfolio <- optimalPortfolios.fPort(posterior,inputData = NULL,spec = NULL,constraints = "LongOnly",optimizer = "tangencyPortfolio",numSimulations = 100)
And the error turns out:
Error in if (STATUS != 0) { : argument is of length zero
The problem is, when I check the source code, it seems fine to me and there is no such STATUS that exists. Therefore, I have no idea how this code could go wrong like this. Any idea could be helpful.
Or if you want to test the data, here is the code from the source file of the package, the same error exists:
entries <- c(0.001005,0.001328,-0.000579,-0.000675,0.000121,0.000128,
-0.000445, -0.000437, 0.001328,0.007277,-0.001307,-0.000610,
-0.002237,-0.000989,0.001442,-0.001535, -0.000579,-0.001307,
0.059852,0.027588,0.063497,0.023036,0.032967,0.048039,-0.000675,
-0.000610,0.027588,0.029609,0.026572,0.021465,0.020697,0.029854,
0.000121,-0.002237,0.063497,0.026572,0.102488,0.042744,0.039943,
0.065994 ,0.000128,-0.000989,0.023036,0.021465,0.042744,0.032056,
0.019881,0.032235 ,-0.000445,0.001442,0.032967,0.020697,0.039943,
0.019881,0.028355,0.035064 ,-0.000437,-0.001535,0.048039,0.029854,
0.065994,0.032235,0.035064,0.079958 )
varcov <- matrix(entries, ncol = 8, nrow = 8)
mu <- c(0.08, 0.67,6.41, 4.08, 7.43, 3.70, 4.80, 6.60) / 100
pick <- matrix(0, ncol = 8, nrow = 3, dimnames = list(NULL, letters[1:8]))
pick[1,7] <- 1
pick[2,1] <- -1; pick[2,2] <- 1
pick[3, 3:6] <- c(0.9, -0.9, .1, -.1)
confidences <- 1 / c(0.00709, 0.000141, 0.000866)
views <- BLViews(pick, c(0.0525, 0.0025, 0.02), confidences, letters[1:8])
posterior <- posteriorEst(views, tau = 0.025, mu, varcov )
optimalPortfolios.fPort(posterior, optimizer = "tangencyPortfolio")
I wonder if it is possible to efficiently change ncp in the below code such that x becomes .025 and .975 (within rounding error).
x <- pt(q = 5, df = 19, ncp = ?)
----------
Clarification
q = 5 and df = 19 (above) are just two hypothetical numbers, so q and df could be any other two numbers. What I expect is a function / routine, that takes q and df as input.
What is wrong with uniroot?
f <- function (ncp, alpha) pt(q = 5, df = 19, ncp = ncp) - alpha
par(mfrow = c(1,2))
curve(f(ncp, 0.025), from = 5, to = 10, xname = "ncp", main = "0.025")
abline(h = 0)
curve(f(ncp, 0.975), from = 0, to = 5, xname = "ncp", main = "0.975")
abline(h = 0)
So for 0.025 case, the root lies in (7, 8); for 0.975 case, the root lies in (2, 3).
uniroot(f, c(7, 8), alpha = 0.025)$root
#[1] 7.476482
uniroot(f, c(2, 3), alpha = 0.975)$root
#[1] 2.443316
---------
(After some discussion...)
OK, now I see your ultimate goal. You want to implement this equation solver as a function, with input q and df. So they are unknown, but fixed. They might come out of an experiment.
Ideally if there is an analytical solution, i.e., ncp can be written as a formula in terms of q, df and alpha, that would be so great. However, this is not possible for t-distribution.
Numerical solution is the way, but uniroot is not a great option for this purpose, as it relies on "plot - view - guess - specification". The answer by loki is also crude but with some improvement. It is a grid search, with fixed step size. Start from a value near 0, say 0.001, and increase this value and check for approximation error. We stop when this error fails to decrease.
This really initiates the idea of numerical optimization with Newton-method or quasi-Newton method. In 1D case, we can use function optimize. It does variable step size in searching, so it converges faster than a fixed step-size searching.
Let's define our function as:
ncp_solver <- function (alpha, q, df) {
## objective function: we minimize squared approximation error
obj_fun <- function (ncp, alpha = alpha, q = q, df = df) {
(pt(q = q, df = df, ncp = ncp) - alpha) ^ 2
}
## now we call `optimize`
oo <- optimize(obj_fun, interval = c(-37.62, 37.62), alpha = alpha, q = q, df = df)
## post processing
oo <- unlist(oo, use.names = FALSE) ## list to numerical vector
oo[2] <- sqrt(oo[2]) ## squared error to absolute error
## return
setNames(oo, c("ncp", "abs.error"))
}
Note, -37.62 / 37.62 is chosen as lower / upper bound for ncp, as it is the maximum supported by t-distribution in R (read ?dt).
For example, let's try this function. If you, as given in your question, has q = 5 and df = 19:
ncp_solver(alpha = 0.025, q = 5, df = 19)
# ncp abs.error
#7.476472e+00 1.251142e-07
The result is a named vector, with ncp and absolute approximation error.
Similarly we can do:
ncp_solver(alpha = 0.975, q = 5, df = 19)
# ncp abs.error
#2.443347e+00 7.221928e-07
----------
Follow up
Is it possible that in the function ncp_solver(), alpha takes a c(.025, .975) together?
Why not wrapping it up for a "vectorization":
sapply(c(0.025, 0.975), ncp_solver, q = 5, df = 19)
# [,1] [,2]
#ncp 7.476472e+00 2.443347e+00
#abs.error 1.251142e-07 7.221928e-07
How come 0.025 gives upper bound of confidence interval, while 0.975 gives lower bound of confidence interval? Should this relationship reversed?
No surprise. By default pt computes lower tail probability. If you want the "right" relationship, set lower.tail = FALSE in pt:
ncp_solver <- function (alpha, q, df) {
## objective function: we minimize squared approximation error
obj_fun <- function (ncp, alpha = alpha, q = q, df = df) {
(pt(q = q, df = df, ncp = ncp, lower.tail = FALSE) - alpha) ^ 2
}
## now we call `optimize`
oo <- optimize(obj_fun, interval = c(-37.62, 37.62), alpha = alpha, q = q, df = df)
## post processing
oo <- unlist(oo, use.names = FALSE) ## list to numerical vector
oo[2] <- sqrt(oo[2]) ## squared error to absolute error
## return
setNames(oo, c("ncp", "abs.error"))
}
Now you see:
ncp_solver(0.025, 5, 19)[[1]] ## use "[[" not "[" to drop name
#[1] 2.443316
ncp_solver(0.975, 5, 19)[[1]]
#[1] 7.476492
--------
Bug report and fix
I was reported that the above ncp_solver is unstable. For example:
ncp_solver(alpha = 0.025, q = 0, df = 98)
# ncp abs.error
#-8.880922 0.025000
But on the other hand, if we double check with uniroot here:
f <- function (ncp, alpha) pt(q = 0, df = 98, ncp = ncp, lower.tail = FALSE) - alpha
curve(f(ncp, 0.025), from = -3, to = 0, xname = "ncp"); abline(h = 0)
uniroot(f, c(-2, -1.5), 0.025)$root
#[1] -1.959961
So there is clearly something wrong with ncp_solver.
Well it turns out that we can not use too big bound, c(-37.62, 37.62). If we narrow it to c(-35, 35), it will be alright.
Also, to avoid tolerance problem, we can change objective function from squared error to absolute error:
ncp_solver <- function (alpha, q, df) {
## objective function: we minimize absolute approximation error
obj_fun <- function (ncp, alpha = alpha, q = q, df = df) {
abs(pt(q = q, df = df, ncp = ncp, lower.tail = FALSE) - alpha)
}
## now we call `optimize`
oo <- optimize(obj_fun, interval = c(-35, 35), alpha = alpha, q = q, df = df)
## post processing and return
oo <- unlist(oo, use.names = FALSE) ## list to numerical vector
setNames(oo, c("ncp", "abs.error"))
}
ncp_solver(alpha = 0.025, q = 0, df = 98)
# ncp abs.error
#-1.959980e+00 9.190327e-07
Damn, this is a pretty annoying bug. But relax now.
Report on getting warning messages from pt
I also receive some report on annoying warning messages from pt:
ncp_solver(0.025, -5, 19)
# ncp abs.error
#-7.476488e+00 5.760562e-07
#Warning message:
#In pt(q = q, df = df, ncp = ncp, lower.tail = FALSE) :
# full precision may not have been achieved in 'pnt{final}'
I am not too sure what is going on here, but meanwhile I did not observe misleading result. Therefore, I decide to suppress those warnings from pt, using suppressWarnings:
ncp_solver <- function (alpha, q, df) {
## objective function: we minimize absolute approximation error
obj_fun <- function (ncp, alpha = alpha, q = q, df = df) {
abs(suppressWarnings(pt(q = q, df = df, ncp = ncp, lower.tail = FALSE)) - alpha)
}
## now we call `optimize`
oo <- optimize(obj_fun, interval = c(-35, 35), alpha = alpha, q = q, df = df)
## post processing and return
oo <- unlist(oo, use.names = FALSE) ## list to numerical vector
setNames(oo, c("ncp", "abs.error"))
}
ncp_solver(0.025, -5, 19)
# ncp abs.error
#-7.476488e+00 5.760562e-07
OK, quiet now.
You could use two while loops like this:
i <- 0.001
lowerFound <- FALSE
while(!lowerFound){
x <- pt(q = 5, df = 19, ncp = i)
if (round(x, 3) == 0.025){
lowerFound <- TRUE
print(paste("Lower is", i))
lower <- i
} else {
i <- i + 0.0005
}
}
i <- 0.001
upperFound <- FALSE
while(!upperFound){
x <- pt(q = 5, df = 19, ncp = i)
if (round(x, 3) == 0.975){
upperFound <- TRUE
print(paste("Upper is ", i))
upper <- i
} else {
i <- i + 0.0005
}
}
c(Lower = lower, Upper = upper)
# Lower Upper
# 7.4655 2.4330
Of course, you can adapt the increment in i <- i + .... or change the check if (round(x,...) == ....) to fit this solution to your specific needs of accuracy.
I know this is an old question, but there is now a one-line solution to this problem using the conf.limits.nct() function in the MBESS package.
install.packages("MBESS")
library(MBESS)
result <- conf.limits.nct(t.value = 5, df = 19)
result
$Lower.Limit
[1] 2.443332
$Prob.Less.Lower
[1] 0.025
$Upper.Limit
[1] 7.476475
$Prob.Greater.Upper
[1] 0.025
$Lower.Limit is the result where pt = 0.975
$Upper.Limit is the result where pt = 0.025
pt(q=5,df=19,ncp=result$Lower.Limit)
[1] 0.975
> pt(q=5,df=19,ncp=result$Upper.Limit)
[1] 0.025
I am new to R, Trying do MLE using mle2 in bbmle package.
R Code:
rm(list = ls())
library(bbmle)
N <- 100
testmat=rmultinom(N, size=3, prob = c(0.1,0.2,0.8))
LL<- function(s, p){-sum(dmultinom(x=testmat, size = s, prob=p, log = TRUE))}
values.start <- list(3, c(0.1,0.2,0.7))
names(values.start) <- parnames(LL) <- paste0("b",0:1)
mle2(LL, start =values.start)
I keep getting this error
"Error in mle2(LL, start = values.start) :
some named arguments in 'start' are not arguments to the specified log-likelihood function"
I am using mle2, I thought its not needed here. At first I was using "mle"
N <- 100
testmat=t(rmultinom(3, size=3, prob = c(0.1,0.2,0.8)))
LL<- function(s, p1,p2,p3){prob=unlist(as.list(environment()))[2:4]
-sum(dmultinom(x=testmat, size = s, prob=prob, log = TRUE))}
values.start <- list(s=3,p1=0.1,p2=0.2,p3=7)
mle(LL, start =values.start)
which game this error
""Error in dmultinom(x = testmat, size = s, prob = prob, log = TRUE) :
x[] and prob[] must be equal length vectors."
I even edited it as follows
N <- 100
testmat=t(rmultinom(3, size=3, prob = c(0.1,0.2,0.8)))
LL<- function(s=3, p1=0.1,p2=0.2,p3=0.7){
prob=unlist(as.list(environment()))[2:4]
s=unlist(as.list(environment()))[1]
-sum(dmultinom(x=testmat, size = s, prob=prob, log = TRUE))}
mle(LL)
error still persists. Finally I was able to decode the errors, thanks a lot.
library(bbmle)
N <- 1000
X=rmultinom(N,size=3,prob = rep(1/3, 3))
LL <- function( p_1 = 0.1,p_2=0.1,p_3=0.8) {
p <- unlist(as.list(environment()))
-sum(apply(X, MAR = 2, dmultinom, size = NULL, prob = c(p_1,p_2,p_3), log = TRUE))
}
mle(LL,method = "L-BFGS-B", lower = c(-Inf, 0), upper = c(Inf, Inf))
In my current ploblem, I have 5k features, therefore I need to write something like this.
function( p_1 = 0.1,p_2=0.1,p_3=0.8...., p_5000=..)
which not possible. Is there any way out of it?
I was able to do it with mle2. this way
rm(list = ls())
library(bbmle)
N <- 1000
s<-100
X=rmultinom(N,size=s,prob = rep(1/s, s))
LL= function(params){
p <- unlist(as.list(environment()))
minusll = -sum(apply(X, MAR = 2, dmultinom, size = NULL, prob = p, log = TRUE))
return(minusll)
}
values.start<-vector(mode="list", length=s)
values.start <- c(0.02,0.01*rep(98/99,99))
names(values.start) <- parnames(LL)<-paste0("b",1:s)
mle2(LL, start =values.start,vecpar = TRUE, method = "L-BFGS-B", lower = c(rep(0,s)), upper = c(rep(1,s)))
Above I was doing Multinomial MLE parameter estimation for dimension of 100, and 1000 samples. I was able to solve the problem of vector parameters. Now I am having this error
Error in optim(par = c(0.02, 0.0098989898989899, 0.0098989898989899, 0.0098989898989899, :
L-BFGS-B needs finite values of 'fn'
I found out that this error is due to 'fn=Inf', might be due to one of the propabilities becoming zero, therefore fn=-log(0) = Inf. Is there any way to solve this problem?
Thanks for the help.
Please forgive my lack of knowledge,.I would be very thankful for some help.
Here is my problem:
I was using optim to estimate parameters of a model and I get this error message
"Error in optim(x0, fn = riskll, method = "L-BFGS-B", lower = lbs, upper = ubs, :
L-BFGS-B needs finite values of 'fn'"
Below is the R code I have written.
library('GeneralizedHyperbolic')
data=read.table(file="MSCI_USA.csv",sep=',',header=T)
data=data[1:8173,]
#starting value
x0 <- c(-0.011,0.146, 0.013, 0.639, 0.059,0.939, -0.144 , 1.187, 1.601, -0.001)
#lower bound and upper bound
lbs <- c(-5, -5, -5, -0.99999, 0.00001, 0, -1, 0.1, 1.2000001, -2)
ubs<- c( 5, 5, 10, 0.99999, 5, 2, 0, 3, 1000, 10)
#the likelihood function
riskll <- function(data,para) {
m0 <- para[1]
m1 <- para[2]
omega <- para[3]
tau <- para[4]
a <- para[5]
b <- para[6]
beta <- para[7]
theta <- para[8]
gamma <- para[9]
phi <- para[10]
T <- nrow(data)
ret <- data[,2];
rate <- data[,3]
exret=100*(ret+1-((rate/100)+1)^(1/365))
h = rep(0,T);
vx = rep(0,T);
h[1] = 10000*exret[1]^2
vx[1] = (exret[1]-m0-(m1+beta*((gamma^0.5)/(gamma^2+beta^2)^0.5))*h[1])/h[1]
for ( i in (2:T) ) {
h[i] = (omega+a*(abs(h[i-1]*vx[i-1])-tau*h[i-1]*vx[i-1])^theta+b*(h[i- 1]^theta))^(1/theta)
vx[i] = (exret[i]-phi*exret[i-1]-m0-(m1+beta*((gamma^0.5)/(gamma^2+beta^2)^0.5))*h[i])/h[i]
}
mu = -1*beta*((gamma^0.5)/(gamma^2+beta^2)^0.5)
delta=((gamma^1.5)/(gamma^2+beta^2)^0.5)
alpha=gamma
beta=beta
param = c(mu, delta, alpha, beta)
riskll <- -1*sum(log(dnig(vx,param=param)))
return(riskll)
}
#optimization
optim(x0,fn=riskll,method ="L-BFGS-B",lower=lbs,upper=ubs, data = data)
I'm not certain, but I'd look carefully at this line:
riskll <- -1*sum(log(dnig(vx,param=param)))
The log function approaches negative infinity as its argument approaches zero. And it's not defined at all for negative arguments. Perhaps the error message is warning you about this possibility.