search for: discretized

Dear all, I get a strange error when I find polychoric correlations with the ML method, which I have been able to reproduce using randomly-generated data. What is wrong? I realize that the data that I generated randomly is a bit strange, but it is the only way that I duplicate the error message. > n<-100 > test.x<-rnorm(n, mean=0, sd=1) > test.c<-test.x + rnorm(n, mean=0,

Dear R users, I have a problem for which my objective function depends on both discrete and continuous arguments. The problem is that the number of combinations for the (multivariate) discrete arguments can become overwhelming (when it is univariate this is not an issue) hence search over the continuous arguments for each possible combination of the discrete arguments may not be feasible. Guided

Dear all, I have a continuous variable that can take on values between 0 and 100, for example: x<-runif(100,0,100) I also have a second variable that defines a series of thresholds, for example: y<-c(3, 4.5, 6, 8) I would like to convert my continuous variable into a discrete one using the threshold variables: If x is between 0 and 3 the discrete variable should be 1 If x is between 3

Is there a package that will allow me to fit Brownian motion and Ornstein?Uhlenbeck models of evolution for discrete traits? I know that geiger and ouch have commands for fitting these models for continuous traits, but these aren't suitable for discrete trait evolution, correct? -- View this message in context:

...interest rate can have 3--5 possible values, and its movement is characterized by a matrix of transition probabilities (ie, it is a Markov process). I would like to estimate this process from a given set of data. For example, let the interest rate time series be: 7 3 8 2 5 9 6 Assume that the discretized variable can take the following values: (3, 5, 8), then we find the nearest discrete point and give its index: 3 1 3 1 2 3 2 Then estimate the transition probabilities. I have the following questions: - how should I select the discrete set of values that the variable can assume? Eg simply get t...

Dear all, I am trying to simulate from discrete uniform distribution. But I could not find any in-built code in R. Could anyone help me please? Thanks in advance for the time and help. Cassie [[alternative HTML version deleted]]

Hi all, I am REALLY confused with the variance right now. for a discrete uniform distribution on [1,12] the mean is (1+12)/2=6.5 which is ok. y=1:12 mean(y) then var(y) gives me 13 1- on http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29 wiki the variance is (12^2-1)/12=143/12 2-

Hello everyone, I need to create and work with some big matrices that actually have somewhat over 2 million columns and 117 rows. To do some calculations on such big matrices R just needs too much memory for my PC (4GB installed). So I need a solution to work with large datasets. I'm trying to use the ff-package but I don't think I really understand the whole functionality of the

Hi R helpers, I'm preparing dataset to fir logistic regression model with lrm(). I have various cointinous and discrete variables and I would like to: 1. Optimaly discretize continous variables (Optimaly means, maximizing information value - IV for example) 2. Regroup discrete variables to achieve perhaps smaller number of level and better information value... Please suggest if there is

Hello, I am using the mda package and in particular the fda routine to classify in term of gear a set of 20 trips. I preformed a flexible discriminant analysis (FDA) using a set of 151 trips. FDAT1 <- fda(as.factor(gear) ~ . , data =matrizR) A total of 22 predictors were considered. 20 of the predictors are "numeric" and 2 are "factors/discrete". The resulting FDA

Hi r-help, I have been using your RHmm package for some time and have recently had to try using the package for a new dataset. Basically I have a dataset with a number of discrete observation variables that change over time, and I would love to try modeling them using a HMM. Basically I was wondering if RHmm can be used to model a multivariate discrete HMM, i.e., the observations are a vector

Dear Rgurus, To my knowledge the best way to visualize the distribution of a discrete variable X is plot(table(X)) The problem which I have is the following. I have to discrete variables X and Y which distribution I would like to compare. To overlay the distribution of Y with lines(table(Y)) gives not satisfying results. This is the same in case of using density or histogram. Hence, I am

...ot;,"C","C") and run: lm(result ~ x1 + group) The lm function has split the group into separate binary variables {0,1} before performing the regression. I now have: groupA groupB groupC Some of the other models that I want to try won't accept factors, so they need to be discretized this way. Is there a command in R for this, or some easy shortcut? (I tried digging into the lm code, but couldn't find where this is being done.) Thanks! -N

Full_Name: Robert Iquiapaza Version: 2.7.2 OS: Vista Submission from: (NULL) (69.127.35.170) In the help Examples for returns(fSeries) it is said that you can pass the method to compute ("continuous", "discrete", "compound", "simple") returns using 'type=' i.e. # Discrete Returns: returns(MSFT, type = "discrete") However when you use

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Dear useRs, I use dics.ef function from dprep package to discretize continuous variable using intervals of equal frequencies. Dataset to be discretized include 4 continuous and 2 discrete variables in the following order: Continuous Countinuous Countinuous Discrete Discrete Continuous The problem emerge when I try to discretize the last continuos variable: library(dprep) dics.ef(my.dataframe, 1:6,5) ## all 6 variables in data.frame I have no...

I'm trying to develop some graphic methods for glm objects, but they only apply for models where all predictors are discrete factors. How can I test for this in a function, given the glm model object? That is, I want something that will serve as an equivalent of is.discrete.glm() in the following context: myplot.glm <- function(model, ...) { if (!inherits(model,"glm"))

Hi Group, I have been working with the code below. Everything seems to work okay, except that the discrete values on the x-axis are far from each end of the graph. I've tried several things including changing the discrete values and playing with the limits, but can't get it to work. I tested this on simulated data and do not have the same problem, so I guessing it is how I'm

I want to create the mixture formulation of a discrete uniform ie, say f(x) = 1/10, for i = 1,2,3,4,5,6,7,8,9 and 10 and another discrete distribution which has the same values of x, but he probabilities can vary. Can this be done on any package in R? an if so, can the package estimate the 'probabilities' of each of the x value as well as the mixing proportion if I have the data?

Hello list: I generate by simulation (using different procedures) two sample vectors of size N, each corresponding to a discrete variable and I want to text if these samples can be considered as having the same probability distribution (which is unknown). What is the best test for that? I've read that Kolmogorov-Smirnov and Anderson-Darling tests are restricted to continuous data