Somehow, I am unable to generate the actual underlying values of the IRFs. See code of a simple VAR model.
irf5<-irf(var2, impulse = "libor", response = "y", n.ahead = 10, ortho = TRUE, boot = TRUE, CI = 0.95, runs = 100)
I can generate the resulting IRF plots just fine with this code:
plot(irf5)
But, I can't generate the underlying values. I'd like to do so to have precise figures. Visually interpreting IRFs is not that accurate. Using the summary() did not provide me this information.
I think you need just to write irf5 in command line and push ctrl+enter. If you can plot irf, so there are no errors, you can easily get IRF's values. In other words, inirf5<-irf(var2,....) you just generate and save new variable, but not call to it
You should be able to get the values by using irf5$irf
Related
I try to use the "vivid" package in R to draw the correlation of variable interactions, because this package can directly analyze the trained machine learning model, and its visualization is very beautiful, but when I execute the vivi() command , I get the wrong information, I don't know what's wrong, because I can execute another instruction in the package: pdpPairs(), and get the PDPS diagram, although I know it's difficult without more information , but I'm hoping to get an answer.Or you can also tell me if there is any other way to directly analyze the trained machine learning model to get a heatmap or network diagram of variable interaction, which will be very useful to me
viFit <- vivi(
fit = gbm_fit,
data = variable_data,
response = "Y1_learning",
gridSize = 10,
importanceType = NULL,
nmax = 100,
reorder = TRUE,
class = 1,
predictFun = NULL)
Agnostic variable importance method used.
Calculating interactions...
Error in mat[vars2] <- res[["value"]] : invalid subscript type 'list'
I want to change the underlying distribution for my garch.sim function, but I get the following error : could not find function "rnd"
I would like to change it to either t or chi-squared distribution, as the default one is normal from a normal distribution. The below code is trying to mimic the normal distribution with sd = 4, but it gives the same error. The source code does not give additional hint in my opinion to solve the issue.
The code itself:
u_garch <-garch.sim(alpha = c(0.01,0.3), beta = 0.69,n = 251,rnd = rnorm(351,sd = 4),ntrans = 100)
I have a big dataset (around 20GB for training and 2GB for testing) and I want to use MXnet and R. Due to lack of memory, I search for an iterator to load the training and test set by a custom iterator and I found this solution.
Now, I can train the model using the code on this page, but the problem is that if I read the test set with the save iterator as follow:
test.iter <- CustomCSVIter$new(iter = NULL, data.csv = "test.csv", data.shape = 480, batch.size = batch.size)
Then, the prediction command does not work and there is no prediction template in the page;
preds <- predict(model, test.iter)
So, my specific problem is, if I build my model using the code on the page, how can I read my test set and predict its labels for the evaluation process? My test set and train set is in this format.
Thank you for your help
It actually works exactly as you explained. You just call predict with model and iterator:
preds = predict(model, test.iter)
The only trick here is that the predictions are displayed column-wise. By that I mean, if you take the whole sample you are referring to, execute it and add the following lines:
test.iter <- CustomCSVIter$new(iter = NULL, data.csv = "mnist_train.csv", data.shape = 28, batch.size = batch.size)
preds = predict(model, test.iter)
preds[,1] # index of the sample to see in the column position
You receive:
[1] 5.882561e-11 2.826923e-11 7.873914e-11 2.760162e-04 1.221306e-12 9.997239e-01 4.567645e-11 3.177564e-08 1.763889e-07 3.578671e-09
This show the softmax output for the 1st element of the training set. If you try to print everything by just writing preds, then you will see only empty values because of the RStudio print limit of 1000 - real data will have no chance to appear.
Notice that I reuse the training data for prediction. I do so, since I don't want to adjust iterator's code, which needs to be able to consume the data with and without a label in front (training and test sets). In real-world scenario you would need to adjust iterator so it would work with and without a label.
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'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.