I have a simple question on R. This is a simple code to generate random variables from a bivariate normal clayton copula with normally distributed margins. How could I do this neatly if I had d equally distributed margins, without having to write c("norm","norm","norm", ... ) etc.?
myMvd1 <- mvdc(copula = archmCopula(family = "clayton", param = 2),
margins = c("norm", "norm"), paramMargins = list(list(mean = 0,
sd = 1), list(mean = 0, sd = 1)))
You can use rep:
d <- 5
mvdc(copula = archmCopula(family = "clayton", param = 2),
margins = rep("norm", d),
paramMargins = rep(list(list(mean = 0, sd = 1)), d))
(And not knowing what this is about, I am not sure if param should be 2 or d.)
You can do something like this :
matrix(rMvdc(d*nRow, myMvd1),nRow,d)
Related
I am trying to fit a variety of (truncated) probability distributions to the same very thin set of quantiles. I can do it but it seems to require lots of duplication of the same code. Is there a neater way?
I am using this code by Nadarajah and Kotz to generate the pdf of the truncated distributions:
qtrunc <- function(p, spec, a = -Inf, b = Inf, ...)
{
tt <- p
G <- get(paste("p", spec, sep = ""), mode = "function")
Gin <- get(paste("q", spec, sep = ""), mode = "function")
tt <- Gin(G(a, ...) + p*(G(b, ...) - G(a, ...)), ...)
return(tt)
}
where spec can be the name of any untruncated distribution for which code in R exists, and the ... argument is used to provide the names of the parameters of that untruncated distribution.
To achieve the best fit I need to measure the distance between the given quantiles and those calculated using arbitrary values of the parameters of the distribution. In the case of the gamma distribution, for example, the code is as follows:
spec <- "gamma"
fit_gamma <- function(x, l = 0, h = 20, t1 = 5, t2 = 13){
ct1 <- qtrunc(p = 1/3, spec, a = l, b = h, shape = x[1],rate = x[2])
ct2 <- qtrunc(p = 2/3, spec, a = l, b = h, shape = x[1],rate = x[2])
dist <- vector(mode = "numeric", length = 2)
dist[1] <- (t1 - ct1)^2
dist[2] <- (t2- ct2)^2
return(sqrt(sum(dist)))
}
where l is the lower truncation, h is the higher and I am given the two tertiles t1 and t2.
Finally, I seek the best fit using optim, thus:
gamma_fit <- optim(par = c(2, 4),
fn = fit_gamma,
l = l,
h = h,
t1 = t1,
t2 = t2,
method = "L-BFGS-B",
lower = c(1.01, 1.4)
Now suppose I want to do the same thing but fitting a normal distribution instead. The names of the parameters of the normal distribution that I am using in R are mean and sd.
I can achieve what I want but only by writing a whole new function fit_normal that is extremely similar to my fit_gamma function but with the new parameter names used in the definition of ct1 and ct2.
The problem of duplication of code becomes very severe because I wish to try fitting a large number of different distributions to my data.
What I want to know is whether there is a way of writing a generic fit_spec as it were so that the parameter names do not have to be written out by me.
Use x as a named list to create a list of arguments to pass into qtrunc() using do.call().
fit_distro <- function(x, spec, l = 0, h = 20, t1 = 5, t2 = 13){
args <- c(x, list(spec = spec, a = l, b = h))
ct1 <- do.call(qtrunc, args = c(list(p = 1/3), args))
ct2 <- do.call(qtrunc, args = c(list(p = 2/3), args))
dist <- vector(mode = "numeric", length = 2)
dist[1] <- (t1 - ct1)^2
dist[2] <- (t2 - ct2)^2
return(sqrt(sum(dist)))
}
This is called as follows, which is the same as your original function.
fit_distro(list(shape = 2, rate = 3), "gamma")
# [1] 13.07425
fit_gamma(c(2, 3))
# [1] 13.07425
This will work with other distributions, for however many parameters they have.
fit_distro(list(mean = 10, sd = 3), "norm")
# [1] 4.08379
fit_distro(list(shape1 = 2, shape2 = 3, ncp = 10), "beta")
# [1] 12.98371
I am trying to fit a list of dataframes and I can't figure out why I can't define conc and t0 outside of the function.
If I do it like this I get error:
'Error in nls.multstart::nls_multstart(y ~ fit_drx_mono(assoc_time,
t0, : There must be as many parameter starting bounds as there are
parameters'
conc <- 5e-9
t0 <- 127
nls.multstart::nls_multstart(y ~ fit_mono(assoc_time, t0, conc, kon, koff, ampon, ampoff),
data = data_to_fit,
iter = 100,
start_lower = c(kon = 1e4, koff = 0.00001, ampon = 0.05, ampoff = 0),
start_upper = c(kon = 1e7, koff = 0.5, ampon = 0.6, ampoff = 0.5),
lower = c(kon = 0, koff = 0, ampon = 0, ampoff = 0))
When I specify the values in the function everything works as it is supposed to. And I don't understand why.
It turned out I cannot define data = data_to_fit otherwise the function looks for variables only in that dataframe. Once I defined every variable outside of the function without specifying data it works.
My data are not timeseries, but it has sequential properties.
Consider one sample:
data1 = matrix(rnorm(10, 0, 1), nrow = 1)
label1 = rnorm(1, 0, 1)
label1 is a function of the data1, but the data matrix is not a timeseries. I suppose that label is a function of not just one data sample, but more older samples, which are naturally ordered in time (not sampled randomly), in other words, data samples are dependent with one another.
I have a batch of examples, say, 16.
With that I want to understand how I can design an RNN/LSTM model which will memorize all 16 examples from the batch to construct the internal state. I am especially confused with the seq_len parameter, which as I understand is specifically about the length of the timeseries used as an input to a network, which is not case.
Now this piece of code (taken from a timeseries example) only confuses me because I don't see how my task fits in.
rm(symbol)
symbol <- rnn.graph.unroll(seq_len = 5,
num_rnn_layer = 1,
num_hidden = 50,
input_size = NULL,
num_embed = NULL,
num_decode = 1,
masking = F,
loss_output = "linear",
dropout = 0.2,
ignore_label = -1,
cell_type = "lstm",
output_last_state = F,
config = "seq-to-one")
graph.viz(symbol, type = "graph", direction = "LR",
graph.height.px = 600, graph.width.px = 800)
train.data <- mx.io.arrayiter(
data = matrix(rnorm(100, 0, 1), ncol = 20)
, label = rnorm(20, 0, 1)
, batch.size = 20
, shuffle = F
)
Sure, you can treat them as time steps, and apply LSTM. Also check out this example: https://github.com/apache/incubator-mxnet/tree/master/example/multivariate_time_series as it might be relevant for your case.
An assignment has tasked us with creating a series of variables: normal1, normal2, normal3, chiSquared1 and 2, t, and F. They are defined as follows:
library(tibble)
Normal.Frame <- data_frame(normal1 = rnorm(5000, 0, 1),
normal2 = rnorm(5000, 0, 1),
normal3 = rnorm(5000, 0, 1),
chiSquared1 = normal1^2,
chiSquared2 = normal2^2,
F = sum(chiSquared1/chiSquared2),
t = sum(normal3/sqrt(chiSquared1 )))
We then have to make histograms of the distributions for normal1, chiSquared1 and 2, t, and F, which is simple enough for normal1 and the chiSquared variables, but when I try to plot F and t, the plot space is blank.
Our lecturer recommended limiting the range of F to 0-10, and t to -5 to 5. To do this, I use:
HistT <- hist(Normal.Frame$t, xlim = c(-5, 5))
HistF <- hist(Normal.Frame$F, xlim = c(0, 10))
Like I mentioned, this yields blank plots.
Your t and F are defined as sums; they will be single values. If those values are outside your range, the histogram will be empty. If you remove the sum() function you should get the desired results.
I've recently conducted an Independent Component Analysis using fastICA in R. I have obtained a matrix of independent components. How do I figure out what variables these components are made of? For example, component #4 (v4) has a value for each of the n = 400 observations. What is this? Which variables were used to create this v4?
This is the code that was used: ica_new<-fastICA (final,n.comp = 40, alg.typ = "parallel", fun = "logcosh", alpha = 1,
method = "C", row.norm = FALSE, maxit = 200, tol = 0.0001, verbose = TRUE)
I found this code for PCA: ## varimax with normalize = TRUE is the default
fa <- factanal( ~., 2, data = swiss)
varimax(loadings(fa), normalize = FALSE)
promax(loadings(fa))
EDIT: So thanks to #Hack-R I think the code I will need to use would look something like this ica_new<-fastICA (final,n.comp = 40, alg.typ = "parallel", fun = "logcosh", alpha = 1,
method = "C", row.norm = FALSE, maxit = 200, tol = 0.0001, verbose = TRUE, firstEig = 1, lastEig = nrow(final))
`
Is this accurate? EDIT: Doesn't Run