I am assisting a colleague with adding functionality to one of his R packages.
I have implemented nonparametric bootstrapping using a for loop construct in R.
# perform resampling
# resample `subsample_size` values with or without replacement replicate_size times
for (i in 1:replicate_size) {
if (replacement == TRUE) { # bootstrapping
z <- sample(x, size = subsample_size, replace = TRUE)
zz <- sample(x, size = subsample_size, replace = TRUE)
} else { # subsampling
z <- sample(x, size = subsample_size, replace = FALSE)
zz <- sample(x, size = subsample_size, replace = FALSE)
}
# calculate statistic
boot_samples[i] <- min(zz) - max(z)
}
The above loop is nested within another for loop, which itself is nested within a function (details not shown). The code I'm dealing with is messy, and there are most certainly more efficient ways of coding things up, but I've had to leave it be since my colleague is only familiar with very basic and rudimentary coding constructs.
Upon running said function, I specified all required arguments (replicate_size, replacement) except subsample_size. subsample_size is needed to carry out the resampling. This mistake on my part was revealing because, for some strange reason, the code still runs without throwing an error regarding missing a value for subsample_size.
Question: Does anyone have any idea on why this happens?
I'd include more code, but it is very verbose and unwieldy (his code, not mine). Running the for loop outside the function does indeed raise the error regarding the missing value as expected.
Related
I am running simulations in R using R Studio. I need to get an optimal solution using Rglpk library. I call repeatedly the function Rglpk_solve_LP to solve the problem. At times it works fine but at times it runs forever. I want to be able to skip to next line when Rglpk_solve_LP can't converge.
I have attempted to change some of the settings in the Rglpk_solve_LP function such as control = list(lp_time_limit = time_limit ) but it still gets stuck
R code
lp = Rglpk_solve_LP(obj = obj, mat = mat, dir = dir,
rhs = rhs, max = max, types = rep("B",length(obj)),
control = list(lp_time_limit = 5 ))
print(lp)
lp$solution = lp$solution
lp$objval = lp$optimum
return (lp)
I expect a binary vector indicating whether a particular item was included in the solution. However, in some instances it does not finish the calculation and carries on forever.
I have 30 datasets that are conbined in a data list. I wanted to analyze spatial point pattern by L function along with randomisation test. Codes are following.
The first code works well for a single dataset (data1) but once it is applied to a list of dataset with lapply() function as shown in 2nd code, it gives me a very long error like so,
"Error in Kcross(X, i, j, ...) : No points have mark i = Acoraceae
Error in envelopeEngine(X = X, fun = fun, simul = simrecipe, nsim =
nsim, : Exceeded maximum number of errors"
Can anybody tell me what is wrong with 2nd code?
grp <- factor(data1$species)
window <- ripras(data1$utmX, data1$utmY)
pp.grp <- ppp(data1$utmX, data1$utmY, window=window, marks=grp)
L.grp <- alltypes(pp.grp, Lest, correlation = "Ripley")
LE.grp <- alltypes(pp.grp, Lcross, nsim = 100, envelope = TRUE)
plot(L.grp)
plot(LE.grp)
L.LE.sp <- lapply(data.list, function(x) {
grp <- factor(x$species)
window <- ripras(x$utmX, x$utmY)
pp.grp <- ppp(x$utmX, x$utmY, window = window, marks = grp)
L.grp <- alltypes(pp.grp, Lest, correlation = "Ripley")
LE.grp <- alltypes(pp.grp, Lcross, envelope = TRUE)
result <- list(L.grp=L.grp, LE.grp=LE.grp)
return(result)
})
plot(L.LE.sp$LE.grp[1])
This question is about the R package spatstat.
It would help if you could add a minimal working example including data which demonstrate this problem.
If that is not available, please generate the error on your computer, then type traceback() and capture the output and post it here. This will trace the location of the error.
Without this information, my best guess is the following:
The error message says No points have mark i=Acoraceae. That means that the code is expecting a point pattern to include points of type Acoraceae but found that there were none. This can happen because in alltypes(... envelope=TRUE) the code generates random point patterns according to complete spatial randomness. In the simulated patterns, the number of points of type Acoraceae (say) will be random according to a Poisson distribution with a mean equal to the number of points of type Acoraceae in the observed data. If the number of Acoraceae in the actual data is small then there is a reasonable chance that the simulated pattern will contain no Acoraceae at all. This is probably what is causing the error message No points have mark i=Acoraceae.
If this interpretation is correct then you should be able to suppress the error by including the argument fix.marks=TRUE, that is,
alltypes(pp.grp, Lcross, envelope=TRUE, fix.marks=TRUE, nsim=99)
I'm not suggesting this is necessarily appropriate for your application, but this should remove the error message if my guess is correct.
In the latest development version of spatstat, available on github, the code for envelope has been tweaked to detect this error.
I am trying to use the Nomad technique for blackbox optimisation from the crs package (C implementation), which is called via the snomadr function. The method works when trying straight numerical optimisation, but errors when categorical features are included. However the help for categorical optimisation is not very well documented, so I am struggling to see where I am going wrong. Reproducible code below:
library(crs)
library(randomForest)
Illustrating this on randomForest & the iris dataset.
Creating the randomForest model (leaving the last row out as starting points for the optimizer)
rfIris <- randomForest(x=iris[-150,-c(1)], y=unlist(iris[-150,1]))
The objective function (functions we want to optimize)
objFn <- function(x0,model){
preds <- predict(object = model, newdata = x0)
as.numeric(preds)
}
Test to see if the objective function works (should return ~6.37)
objOut <- objFn(x0=unlist(iris[150,-c(1)]),model = rfIris)
Creating initial conditions, options list, and upper/lower bounds for Nomad
x0 <- iris[150,-c(1)]
x0 <- unlist(x0)
options <- list("MAX_BB_EVAL"=10000,
"MIN_MESH_SIZE"=0.001,
"INITIAL_MESH_SIZE"=1,
"MIN_POLL_SIZE"=0.001,
"NEIGHBORS_EXE" = c(1,2,3),
"EXTENDED_POLL_ENABLED" = 'yes',
"EXTENDED_POLL_TRIGGER" = 'r0.01',
"VNS_SEARCH" = '1')
up <- c(10,10,10,10)
low <- c(0,0,0,0)
Calling the optimizer
opt <- snomadr(eval.f = objFn, n = 4, bbin = c(0,0,0,2), bbout = 0, x0= x0 ,model = rfIris, opts=options,
ub = up, lb = low)
and I get an error about the NEIGHBORS_EXE parameter in the options list. It seems as if I need to supply NEIGHBORS_EXE a file corresponding to a set of 'extended poll' coordinates, however is it not clear what these exactly are.
The method works by setting "EXTENDED_POLL_ENABLED" = 'no' in the options list, as it then ignores the categorical variables and defaults to numerical optimisation, but this is not what I want.
I also managed to pull up some additional information for NEIGHBORS_EXE using
snomadr(information=list("help"="-h NEIGHBORS_EXE"))
and again, do not understand what the 'neighbours.exe' is meant to be.
Any help would be much appreciated!
This is the response from Zhenghua who coded the R interface:
The issue is that he did not configure the parameter “NEIGHBORS_EXE” properly. He need to prepare an Executable file for defining the neighbors, put the executable file in the folder where R is called, and then set the parameter “NEIGHBORS_EXE” to the executable file name.
You can contact us at nomad#gerad.ca if you wish to continue the discussion.
About the neighbours_exe parameter you can refer to the section 7.1 of user guide of Nomad
https://www.gerad.ca/nomad/Downloads/user_guide.pdf
I am making an R package that streamlines my microarray analysis. It is driving me mad. I have 4 main functions at work, I try to combine them into one. Individually they work but together I get the following error.
Error in topTable(normfit, number = 1e+05, adjust = "BH", lfc = 1) :
fit must be an MArrayLM object
The problem which I think is happening, since each function uses an input of what comes before it , the 3rd function does not complete running before the last one starts.
function (mydir, group, design, contrast)
{
lonoan(mydir)
filterc(normData)
normfit(normData, group, design, contrast)
results(normfit)
}
The error comes from my results function.
function (normfit)
{
probeset.list <- topTable(normfit, number = 1e+05, adjust = "BH",
lfc = 1)
Symbol <- getSYMBOL(rownames(probeset.list), "hugene20sttranscriptcluster.db")
results <- cbind(probeset.list, Symbol)
assign("results", results, envir = .GlobalEnv)
}
At the end of the run the normfit variable is in the global environment and if I run the function again it works. This is driving me mad. Any solutions? Thanks.
I'm relatively new in R and I would appreciated if you could take a look at the following code. I'm trying to estimate the shape parameter of the Frechet distribution (or inverse weibull) using mmedist (I tried also the fitdist that calls for mmedist) but it seems that I get the following error :
Error in mmedist(data, distname, start = start, fix.arg = fix.arg, ...) :
the empirical moment function must be defined.
The code that I use is the below:
require(actuar)
library(fitdistrplus)
library(MASS)
#values
n=100
scale = 1
shape=3
# simulate a sample
data_fre = rinvweibull(n, shape, scale)
memp=minvweibull(c(1,2), shape=3, rate=1, scale=1)
# estimating the parameters
para_lm = mmedist(data_fre,"invweibull",start=c(shape=3,scale=1),order=c(1,2),memp = "memp")
Please note that I tried many times en-changing the code in order to see if my mistake was in syntax but I always get the same error.
I'm aware of the paradigm in the documentation. I've tried that as well but with no luck. Please note that in order for the method to work the order of the moment must be smaller than the shape parameter (i.e. shape).
The example is the following:
require(actuar)
#simulate a sample
x4 <- rpareto(1000, 6, 2)
#empirical raw moment
memp <- function(x, order)
ifelse(order == 1, mean(x), sum(x^order)/length(x))
#fit
mmedist(x4, "pareto", order=c(1, 2), memp="memp",
start=c(shape=10, scale=10), lower=1, upper=Inf)
Thank you in advance for any help.
You will need to make non-trivial changes to the source of mmedist -- I recommend that you copy out the code, and make your own function foo_mmedist.
The first change you need to make is on line 94 of mmedist:
if (!exists("memp", mode = "function"))
That line checks whether "memp" is a function that exists, as opposed to whether the argument that you have actually passed exists as a function.
if (!exists(as.character(expression(memp)), mode = "function"))
The second, as I have already noted, relates to the fact that the optim routine actually calls funobj which calls DIFF2, which calls (see line 112) the user-supplied memp function, minvweibull in your case with two arguments -- obs, which resolves to data and order, but since minvweibull does not take data as the first argument, this fails.
This is expected, as the help page tells you:
memp A function implementing empirical moments, raw or centered but
has to be consistent with distr argument. This function must have
two arguments : as a first one the numeric vector of the data and as a
second the order of the moment returned by the function.
How can you fix this? Pass the function moment from the moments package. Here is complete code (assuming that you have made the change above, and created a new function called foo_mmedist):
# values
n = 100
scale = 1
shape = 3
# simulate a sample
data_fre = rinvweibull(n, shape, scale)
# estimating the parameters
para_lm = foo_mmedist(data_fre, "invweibull",
start= c(shape=5,scale=2), order=c(1, 2), memp = moment)
You can check that optimization has occurred as expected:
> para_lm$estimate
shape scale
2.490816 1.004128
Note however, that this actually reduces to a crude way of doing overdetermined method of moments, and am not sure that this is theoretically appropriate.