similar to: which() vs. just logical selection in df

Inline. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Oct 14, 2020 at 3:23 PM 1/k^c <kchamberln at gmail.com> wrote: Is which() invoking c-level code by chance, making it slightly faster > on average? > You do not need

What do you *mean* "when you want to use the kernels". WHICH kernels? Use to do WHAT? In your browser, visit cran.r-project.org then select "Packages" from the list on the left. Then pick the alphabetic list. Now search for 'kernel'. You will find dozens of matches. On Wed, 14 Oct 2020 at 05:15, PIKAL Petr <petr.pikal at precheza.cz> wrote: > Hm. Google tells

Hm. Google tells me that kernel function is in stats package which comes with base installation and is invoked when you start R. search() [1] ".GlobalEnv" "package:stats" "package:graphics" [4] "package:grDevices" "package:utils" "package:datasets" [7] "package:methods" "Autoloads"

Good morning dear administrators, Please help me to code this code in R. I use in this file the redescription function ? which by making a scalar product gives a . You can also choose instead of the redescription function ? a kernel k(x,x). Sincerely [[alternative HTML version deleted]]

Dear Frauke, Thank you very much for taking the time to respond. You explanation was very helpful, and I now have that part figured out! Best Wishes, Dan Frauke Message: 3 Date: Mon, 12 Oct 2020 08:33:44 +0200 (CEST) From: =?UTF-8?Q?Frauke_G=C3=BCnther?= <guenther at leibniz-bips.de> To: "r-help at r-project.org" <r-help at r-project.org> Cc: William Michels <wjm1

Dear all, the exclude and constant.weights options are used as follows: exclude: A matrix with n rows and 3 columns will exclude n weights. The the first column refers to the layer, the second column to the input neuron and the third column to the output neuron of the weight. constant.weights: A vector specifying the values of the weights that are excluded from the training process and treated

Good evening dear administrators, It is with pleasure that I am writing to you to ask for help to finalize my R programming algorithm. Indeed, I attach this note to my code which deals with a case of independence test statistic . My request is to introduce the kernels using the functional data for this same code that I am sending you. So I list the lines for which we need to introduce the

Hi R-helpers, Does anyone know why adding which() makes the select call more efficient than just using logical selection in a dataframe? Doesn't which() technically add another conversion/function call on top of the logical selection? Here is a reproducible example with a slight difference in timing. # Surrogate data - the timing here isn't interesting urltext <-

Hello, Please, I want to know how the functional data are defined in programming code R. If possible an illustrative example of code can help me to understand better. Yours sincerely. [[alternative HTML version deleted]]

Hi all! The following code (executed in console)... somevar <- data.frame(v1 = 1:5, somestring = 6:10, v3 = 11:15, v4 = 16:20); somevar %>% gather(key = var, value = val, which(names(somevar) == "somestring"):length(somevar)) %>% head(2); throws... Error in which(names(somevar) == "somestring") : could not find function "which" if I change

Dear list, I am trying to look at the function inside a package. I know that methods() would do the trick, but what if the function is hidden? I have a problem displaying the hidden function. Say, for example the MCMC package. How do you view the code of that function? something like this: > which function (x, arr.ind = FALSE, useNames = TRUE) { wh <- .Internal(which(x)) if

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

good night Again I ask for help to the community, as I am new at this, I have some basic questions. I am looking for packages on neural networks and so you can search found these two that I think are the most used, neuralnet, nnet. So you can test, and correct me if I'm wrong the neuralnet only accepts as input values ??nomer, did a little test data (iris) library (neuralnet)

Hi R, Here's a question/problem on the 'neuralnet' package for neural networks. I have more than 50 factors in each of my independent variables. When I apply the command 'neuralnet', I get the below warning message, > net.sum <- neuralnet( Sum~Var1+Var2+Var3, b, hidden=0,linear.output=TRUE) Warning message: 'predictions' will not be calculated, as at

Hi, Trying to learn Search::Xapian and be better at perl at the same time, I'm stuck, at the DB_CREATE_OR_OPEN error. Perl says this: ~/dev/sandbox/Xapian-perl$ ./Index1-Xap.pl 100-objects-v1.csv db "db" is not exported by the Search::Xapian module Can't continue after import errors at ./Index1-Xap.pl line 7. BEGIN failed--compilation aborted at ./Index1-Xap.pl line 7. What I

Hi All, I am currently conducting a mixed model. I have 7 repeated measures on a simulated clinical trial. If I understand the model correctly, the outcome is the measure (as a factor) the predictors are clinical group and trial (1-7). The fixed factors are the measure and group. The random factors are the intercept and id and group. I tried using 2 functions to calculate mixed effects.

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

Dear List, Let's say I have a list whose components are 2 matrices (as exemplified in the "mylist" object below). I'd like to create a list with components being 4 matrices based on an logical index vector. is there a way to simplify what I'm doing to obtain the results in "mylist2"? I'd like something that would work on an arbitrary number of elements in

Hola a todos, en primer lugar quería agradecer la ayuda recibida desde el foro con respecto a la creación de una red neuronal. Estoy utilizando el paquete Neuralnet, que me parece que es bastante bueno, pero tengo el problema que soy incapaz de hacer las predicciones del modelo. Sé que se hace con el comando "compute", pero no encuentro ningún ejemplo de cómo hacerlo. ¿Alguien me puede