dear members,
I have a data frame which contains, among others, a
date object of monthly frequency which is not regular, i.e some months are
omitted, and the main variable to be forecast, among others. Its name is
vesselB.
I did the following code:
vesselBR <-
Dear members,
WHy is the following code returning NA instead of
the date?
> as.Date("2022-01-02", origin = "1900-01-01", format = "%y%d%m")
[1] NA
Thanking you,
Yours sincerely,
AKSHAY M KULKARNI
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HI Bert,
Thank you for extra help!!
Yes, exactly, your interpretation is perfectly correct and your R code is what
I should look for.
after generated all those negative values of correlation,
I thought about the extremely small p values associated with those negative
correlation, which is not
Your expanded explanation helps clarify your intent. Herewith some
comments. Of course, feel free to ignore and not respond. And, as
always, my apologies if I have failed to comprehend your intent.
1. I would avoid any notion of "statistical significance" like the
plague. This is a purely
Hi Richard,
Nice to know you had similar experience.
Yes, your understanding is right. all correlations are negative after removing
double-zero rows.
It is consistent with a heatmap we generated.
1 is for a cancer patient with a specific mutation. 0 is no mutation for the
same mutation type
В Sat, 27 Jul 2024 11:00:34 +
akshay kulkarni пишет:
> My question is : how to plot the final model on the actual data
> points?
Have you been able to obtain the predictions? What happens if you call
predict() on the model object returned to you by train()?
Once you have both the data and
Thanks!
For some reason I am getting an error when I run your code with my
variables. It works fine with Martin's x and y variables.
So far as I know variable lengths are equal.
> o <-selgmented(lnCpc, ~lnGdpc, Kmax=20, type="bic", msg=TRUE)
Error in model.frame.default(formula = y ~ x,
Curses, my laptop is hallucinating again. Hope I can get through this.
So we're talking about correlations between binary variables.
Suppose we have two 0-1-valued variables, x and y.
Let A <- sum(x*y) # number of cases where x and y are both 1.
Let B <- sum(x)-A # number of cases where x is 1
Let's go back to the original posting.
> >
> >> in each column, less than 10% values are 1, most of them are 0;
> >
> >
> >
> >> so I want to remove a row with value of zero in both columns when
> >> calculate correlation between two columns.
> >
So we're talking about correlations between
dear members,
I want to mention that I am using the neural
network model in caret. I forgot to mention it in the previous mail to you
people
THanking you,
Yours sincerely,
AKSHAY M KULKARNI
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Dear members,
I am using caret for modelling my data. It is a
regression problem. My question is : how to plot the final model on the actual
data points? The output of the model will be a nonlinear form of the activation
function; I want to plot it on the data
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