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Greetings. For R gurus this may be a no brainer, but I could not find pointers to efficient computation of this beast in past help files. Background - I wish to implement a Cramer-von Mises type test statistic which involves double sums of max(X_i,Y_j) where X and Y are vectors of differing length. I am currently using ifelse pointwise in a vector, but have a nagging suspicion that there is a more efficient way to do this. Basically, I require three sums: sum1: \sum_i\sum_j max(X_i,X_j) sum2: \sum_i\sum_j max(Y_i,Y_j) sum3: \sum_i\sum_j max(X_...

...i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with Y_j=X_j+Z, where Z is an N(0,sigma) variable with small sigma. Then Y_j is a probability mixture of the normal variables N(x_j,sigma), where the x_j runs over all values of X_j, and has a highly oscillatory density. The density of Y=\sum Y_j is the convolution of the densities of the Y_j. I need this...

...probabilidad discreta {p_j} mediante kernel gamma. En el siguiente código F es la función Keg: # Distribución empírica suavizada # Valores que toma la variable y su probabilidad puntual y <- c(1, 1.3, 1.5, 2.1, 2.8) k <- length(y) # Frecuencia o número de veces de cada observación y_j s <- c(1, 1, 2, 3, 1) n <- sum(s) # Número de observaciones # Conjunto de riesgo. Número de observaciones mayores o iguales a cada valor # que toma la variable aleatoria (a cada y_j) r <- sort(cumsum(s), decreasing = TRUE) # Kernel gamma # Probabilidades discretas a suavizar p...

...Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta j = y_j - y_(j-1). In order to estimate y-values, I'm assuming that delta j is approximately equal to kj**u, such that my regression model should be something like this: ^y_1 = a1 ^y_2 = a1 + k2**u ^y_3 = a1 + k2**u + k3**u ... ^y_m = a1 + k2**u + k3**u + ... + km**u or, generically ^yi = a1 +...

...ave been answered elsewhere, and I have looked on the web, but nothing helps. I am trying to do the following: X<-matrix(c(1:15),nrow=3,byrow=T) Y<-matrix(c(2,4,6,8,10),ncol=1) I need to sum the product of each row of X by the remaining j rows multiplied by j y values (i.e sum( t(x_i) x_j y_j) ) Hope this makes sense. Thanks in advance. Ps: how do I reference all the help that I have had from the R: team? [[alternative HTML version deleted]]

...ulating the sample correlation coefficient cor(x_t,y_t) between say two variables, x_t and y_t t=1,.....n ( one can assume that the variables are in time but I don't think this really matters for the question ), does someone know where I can find any piece of literature that says that each (x_j,y_j) pair has To be independent from the other (x_i,y_i) pairs (j doesn't equal i ) in order for the calculation to have any reasonable meaning. This makes perfect sense to me but I need it official writing so I can show it to someone else because I don't know how to explain it. Obviously, th...

...Basically, I have a dataset which I would like to model in terms of successive increments, i.e. (y denote empirical values of y) y_1 = y1, y_2 = y1 + delta1, y_3 = y1 + delta1 + delta2. ... y_m = y1 + sum_2^m delta j where delta_j donote successive increments in the y-values, i.e. delta j = y_j - y_(j-1). In order to estimate y-values, I'm assuming that delta j is approximately equal to kj**u, such that my regression model should be something like this: ^y_1 = a1 ^y_2 = a1 + k2**u ^y_3 = a1 + k2**u + k3**u ... ^y_m = a1 + k2**u + k3**u + ... + km**u or, generically ^yi = a1 +...

hello, I want to do a test for spatial correlation. I tried it with geary.test() but I don't understand the required input. x= a numeric vector the same length as the neighbours list in listw (my sampled data, I assume) listw= a listw object created for example by nb2listw (well when I check nb2listw() I get to "neighbours - an object of class nb" - but I couldn't figure

...ng you need for any > > other view. It's knowing you need to that's the problem. > > Good idea, but perhaps not phrased sharply enough to catch the user's > eye. How about something like this: > > "Notice that image() interprets a matrix as a table of f(x_i, y_j), so > the x axis corresponds to row number and the y axis to column number > (bottom to top)." > > and then use Brian's one-liner in the examples section? > > -- > O__ ---- Peter Dalgaard Blegdamsvej 3 > c/ /'_ --- Dept. of Biostatistics...