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Hello, I need to integrate the absolute difference between two lines measured on different points. # For example : x <- seq(0, 1, 1/100) f_x <- runif(101) + x y <- seq(0, 1, 1/23) f_y <- runif(24) + (1 - y) plot(x, f_x, type="l") lines(y, f_y) Then I would like to compute Integral( | f_x - f_y | )dx. (This is not the same as | Integral(f_x)dx - Integral(f_y)dx |.)

Dear R-list members, I've had a hard time trying to solve a non-linear system (nls) of equations which structure for the equation i, i=1,...,4, is as follows: f_i(d_1,d_2,d_3,d_4)-k_i(l,m,s) = 0 (1) In the expression above, both f_i and k_i are known functions and l, m and s are known constants. I would like to estimate the vector d=(d_1,d_2,d_3,d_4) which is solution of (1). Functions in R to estimate f_i-k_i are at the end of this message. Any help/s...

Hi, I am solving a projection pursuit regression problem, of the form y = \sum_i f_i (a_i^T x), where a_i are unknown directions, while f_i are unknown univariate link functions. The following is known about each f_i: 1. f_i (0) = 0  (that is, each f_i passes through the origin) 2. f_i is monotonic. Is there a way to ensure that the function ppr() in R produces solutions tha...

...t and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to provide point estimates only. The predicted value for new observation of the i_th subject, with E_i and F_i as regressor matrices for fixed- and random-effects respectively, is y_i hat = E_i * beta hat + F_i * b_i hat which can also be written as y_i hat = [ E_i, F_i ] * [ beta hat ', b_i hat' ]' , where prime denotes transpose. Therefore, the variance of y_i hat is Var(y_i hat) = [ E_i, F_...

Hello, I want to solve a nonlinear 3SLS problem with "nlsystemfit()". The equations are of the form y_it = f_i(x,t,theta) The functions f_i(.) have to be formulated as R-functions. When invoking "nlsystemfit()" I get the error Error in deriv.formula(eqns[[i]], names(parmnames)) : Function 'f1' is not in the derivatives table Isn't it possible to provide equations in...

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables 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

Hi all, I am doing some experiment studies... It seems to me that with different combination of 5 parameters, the end results ultimately converged to two scalars. That's to say, some combinations of the 5 parameters lead to one end result and some other combinations of the 5 parameters lead to the other end result (scalar). I am thinking of this is sort of something like clustering or

List: Thank you for the replies to my post yesterday. Gabor and Phil also gave useful replies on how to improve the function by relying on mapply rather than the explicit for loop. In general, I try and use the family of apply functions rather than the looping constructs such as for, while etc as a matter of practice. However, it seems the mapply function in this case is slower (in terms of CPU

Hello, i got an warning message in the following code: f<-1:100 t<-1:100 b<-100 ll2 <- function(b,f,t) { t<-cumsum(t) tn<-t[length(t)] i<-seq(along=f) s1<-(tn*exp(-b*tn)*sum(f[i]))/(1-exp(-b*tn)) s2<-sum((f[i]*(t[i]*exp(-b*t[i])-t[i-1]*exp(b*t[i-1])))/(exp(-b*t[i-1])-exp(-b*t[i]))) s1-s2 } ll2(b,f,t) i think, the problem here is, that t[0] doesn't

...ould appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other letters represent values given from sample observations. The equations involve counts of the number of fulfilled conditions and therefore non-linearities. Furthermore, since I have many observations i, the equatio...

...ls. Let s and t be the Bernoulli parameters and p the mixing parameter. There are 5 possible outcomes f0 = p*(1-s)^4 + (1-p)*(1-t)^4; f1 = 4*p*s*(1-s)^3 + 4*(1-p)*t*(1-t)^3; f2 = 6*p*s^2*(1-s)^2 + 6*(1-p)*t^2*(1-t)^2; f3 = 4*p*s^3*(1-s) + 4*(1-p)*t^3*(1-t); f4 = p*s^4 + (1-p)*t^4; The polynomial f_i represents the probability of seeing i successes. Suppose we repeat this experiment 1000 times, and u_i is the number of times we saw i successes. The likelihood of this event is f_0^u_0*f_1^u_1*f_2^u_2*f_3^u_3*f_4^u_4, and we seek to find those parameter values for s,t,p which maximize the likeli...

Spencer Thanks for your thoughts on this. I did a bit of work and did end up with a method (more a trick), but it did work. I am certain there are better ways to do this, but here is how I resolved the issue. The integral I need to evaluate is \begin{equation} \frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta} {\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta} \end{equation} Where

...individual in the study given by h_i(t) = > exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that > predict(mwa, type='linear') is \beta'x_i. No, it's beta'x_i for the accelerated failure parametrization of the Weibull. In terms of the CDF F_i(t) = F_0( exp((t+beta'x_i)/scale) ) So you need to multiply by the scale parameter and change sign to get the log hazard ratios. > b) since I need the coefficient intercept from the model to obtain the scale > parameter to obtain the base hazard function as defined in Collett > (h_...

...www.lse.ac.uk/collections/statistics/documents/researchreport34.pdf 'Diagnosing Shocks in TIme Series', de Jong and Penzer construct a statistic (tau) which can be used to locate potential shocks. [p15, Theorem 6.1 and below]. They also state that all the components of that statistic (v_i, F_i, r_i, N_i) 'are computed with Kalman Filter Smoother applied to the null model'. Also, as I understand, that part has been implemented in one of the S packages , SsfPack, as the book on that states on p 531 'the standardized smoothed disturbances may be interpreted as t-statistics for...

Hi out there, imagine you have a dataset (x,y) with errors f, so that each y_i is y_i +- f_i. This is the normal case for almost all measurements, that one quantity y can only be measured with a certain accuracy. > x<-c(1,2,3) > y<-c(1.1,0.8,1.3) > f<-c(0.2,0.2,0.2) > plot(x,y) #whereas every y has the uncertainty of f If I now perform a nls-fit (and force the...

Hi out there, imagine you have a dataset (x,y) with errors f, so that each y_i is y_i +- f_i. This is the normal case for almost all measurements, since one quantity y can only be measured with a certain accuracy f. > x<-c(1,2,3) > y<-c(1.1,0.8,1.3) > f<-c(0.2,0.2,0.2) > plot(x,y) #whereas every y has the uncertainty of f If I now perform a nls-fit (and force the dat...

Dear R users, subject: How to filter XY pairs of inacurate gps position along track, taking into account the time index to not mix track from different days in one average track or subject as: How to filter XY pairs of inacurate gps position: (latitude, longtitude) to get estimated correct position before calculating track i.e. How to estimate (time series? )? How to filter XY pairs of

I wish to generate a vector of uniformly distributed data with a defined correlation to another vector The only function I have been able to find doing something similar is corgen from the library ecodist. The following code generates data with the desired correlation to the vector x but the resulting vector y is normal and not uniform distributed library(ecodist) x <- runif(10^5) y

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Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous