search for: sum_y

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

Hi, I would 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

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

I check the Mips backend for the following C code fragment compile result. It seems not correct. Is it my misunderstand or it's a bug. //ch8_3.cpp #include <stdarg.h> int sum_i(int amount, ...) { int i = 0; int val = 0; int sum = 0; va_list vl; va_start(vl, amount); for (i = 0; i < amount; i++) { val = va_arg(vl, int); sum += val; } va_end(vl);

Dear R users, I'm trying to fit a simple random intercept model with a fixed intercept. Suppose I want to assign a weight w_i to the i-th contribute to the log-likelihood, i.e. w_i * logLik_i where logLik_i is the log-likelihood for the i-th subject. I want to maximize the likelihood for N subjects Sum_i {w_i * logLik_i} Here is a simple example to reproduce

Which part of the generated code do you think is not correct? Could you be more specific? I compiled this program with clang and ran it on a mips board. It returns the expected result (21). On Tue, Feb 19, 2013 at 4:15 AM, Jonathan <gamma_chen at yahoo.com.tw> wrote: > I check the Mips backend for the following C code fragment compile result. > It seems not correct. Is it my

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

I didn't have Mips board. I compile as the commands and check the asm output as below. 1. Question: The distance of caller arg[4] and arg[5] is 4 bytes. But the the callee get every arg[] by 8 bytes offset (arg_ptr1+8 or arg_ptr2+8). I assume the #BB#4 and #BB#5 are the arg_ptr which is the pointer to access the stack arguments. 2. Question: Stack memory 28($sp) has no initial value. If

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

Dear All, Apologies if this is too simple for this list. Let us assume that you have an instrument measuring particle distributions. The output is a set of counts {n_i} corresponding to a set of average sizes {d_i}. The set of {d_i} ranges from d_i_min to d_i_max either linearly of logarithmically. There is no access to further detailed information about the distribution of the measured sizes, but

Does it make a difference if you give the "-target" option to clang? $ clang -target mips-linux-gnu ch8_3.cpp -o ch8_3.bc -emit-llvm -c The .s file generated this way looks quite different from the one in your email. On Tue, Feb 19, 2013 at 5:06 PM, Jonathan <gamma_chen at yahoo.com.tw> wrote: > I didn't have Mips board. I compile as the commands and check the asm >

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

Hello! I am using for logistic regression in survey data the svyglm procedure. I wondered how does the strata effect estimates SE (in addition to the weights given proportional to population size). I know that for simple regression measurements of each strata is assumed to have different variance. But in a logistic model this is not the case. Can anyone help me here? Thank you Ron [[alternative

Dear All, I am evaluating the value of loglikelihood and it ends up with the sum of tiny numbers. Below is an example: suppose I would like to calculate sum_i (log (sum_j x [i, j] )), the index of log (x) is in the range, say (-2000, 0). I am aware that exp(-744.5) will be expressed as 0 in 32 bit R and exp Is there a way to improve the result? R example: powd <- sample(-2000:0, 100,

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

I want to fit some p-values to a beta distribution. But the problem is some of the values have 0s and 1's. I am getting an error if I use the MASS function to do this. Is there anyway to get around this? -- Thanks, Jim. [[alternative HTML version deleted]]

I use the lme function from the nlme library (or alternatively from the Matrix library) to estimate a random effects model. Both functions return the covariance matrix of the estimated parameters. I have the following question: Is it possible to retrieve the information matrix of such a model (ie from the fitted object)? In particular, the information matrix can be computed as a sum of individual

Hi, In fitting GAMs to assess environmental preferences, I use the part of the fit where the lower confidence interval is above zero as my criterion for positive association between the environmental variable and species abundance. However I like to plot this on the original scale of species abundance. To do so I extract the fit and SE using predict.gam. Lately I compared more

TLDR - The proposal below is intended to allow inlining of larger callees when such inlining is expected to reduce the dynamic instructions count. Proposal ------------- LLVM inlines a function if the size growth (in the given context) is less than a threshold. The threshold is increased based on certain characteristics of the called function (inline keyword and the fraction of vector

Hello, I have some parameters from Mclust function. The parameters are in the form *parametersDf * * mu_1 mu_2 var_mc1 var_mc2 c1 c2 * *2 1.357283 2.962736 0.466154 0.1320129 0.5258975 0.4741025 * *21 8.357283 9.962736 0.466154 0.1320129 0.5258975 0.4741025 * Each row in the above data frame