search for: estimated

...not implemented for pass: 'Target Library Information'! Printing analysis 'Cost Model Analysis' for function 'main': Cost Model: Unknown cost for instruction: %1 = alloca i32, align 4 Cost Model: Unknown cost for instruction: %r = alloca i32, align 4 Cost Model: Found an estimated cost of 1 for instruction: store i32 0, i32* %1 Cost Model: Unknown cost for instruction: %2 = call i32 (...)* @rand() Cost Model: Found an estimated cost of 1 for instruction: store i32 %2, i32* %r, align 4 Cost Model: Found an estimated cost of 1 for instruction: %3 = load i32* %r, align...

...itable. Specifically: LV: Checking a loop in "quantum_sigma_x" LV: Found a loop: for.body LV: Found an induction variable. LV: Found a write-only loop! LV: We can vectorize this loop! LV: Found trip count: 0 LV: The Widest type: 64 bits. LV: The Widest register is: 128 bits. LV: Found an estimated cost of 0 for VF 1 For instruction: %indvars.iv = phi i64 [ 0, %for.body.lr.ph ], [ %indvars.iv.next, %for.body ] LV: Found an estimated cost of 0 for VF 1 For instruction: %state = getelementptr inbounds %struct.quantum_reg_node_struct* %2, i64 %indvars.iv, i32 1, !dbg !58 LV: Found an estimated...

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

Thanks to the help of many on this list, I am now an R user and have been able to write some functioning code to do matching estimation. I have two for loops (i in 1:3, and j in 0:2). Within the loops, I had been creating matrices of relevant estimation coefficents in order to make lots of LaTeX tables. Well, now I want to be able to combine the results of many different estimations from within

...t(z, z$n) res2 <-lapply(z, function(x){ ?????????????? m <- as.numeric(x$y) ?????????????? gevFit(m, block = 1, type = c("pwm")) ??????????????? }) > res2 $`1` Title: ?GEV Parameter Estimation Call: ?gevFit(x = m, block = 1, type = c("pwm")) Estimation Type: ? gev pwm Estimated Parameters: ?????? xi??????? mu????? beta 0.1033614 2.5389580 0.9092611 Description ? Wed Jun 29 23:07:48 2011 $`2` Title: ?GEV Parameter Estimation Call: ?gevFit(x = m, block = 1, type = c("pwm")) Estimation Type: ? gev pwm Estimated Parameters: ?????? xi??????? mu????? beta 0.340...

Dear R-list, I have made a simple kernel density estimation by x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10) kde <- density(x,n=100) Now I would like to know the estimated probability that a new observation falls into the interval 0<x<3. How can I integrate over the corresponding interval? In several R-packages for kernel density estimation I did not found a corresponding function. I could apply Simpson's Rule for integrating, but perhaps somebody knows a...

Dear R Users, Here is another probelm/question. I would like to run some panel regressions with R. Therefore I have combined several time periods of data for different individuals in my database. I have already run pooled OLS but I would need to calculate a Fixed Effects Estimator (within estimator). Unfortunately I couldn't find anything like that in the RSearch and I suppose that lme

This option prints the estimated size of the data that will be copied from the source disk. For interest, the test prints: 3747840 ../test-data/phony-guests/windows.img Estimate: 3710976 --- v2v/Makefile.am | 2 ++ v2v/cmdline.ml | 17 ++++++++++-- v2v/cmdline.mli | 2 ++ v2v/test...

Hello, I have a loop with 1000 repetitions which includes OR computation of an exposure factor and outcome. I compute OR like this: t<-table(exposure,outcome) oddsratio(t)$measure["estimate"] This gives me the estimates for exposure=0 and exposure=1 but exposure=0 is the reference group and i need only the estimate for exposure=1. I specified a matrix OR with 3 columns (for OR

Is it possible to simultaneously estimate mean and GARCH parameters in R? In other words, I would like to estimate the normal regression equation Y = b X + u and simultaneously do a garch process on the u's to correct the standard errors. I was thinking maybe something with systemfit(), but I can't quite come up with it. Thanks, Tobias --

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates derived using summary.rq's "nid" option. The outcome variable is severely skewed, and I am

...uniform instruction: %len.0288 = phi i32 [ %dec, %while.body320 ], [ %conv316, %while.body320.preheader ] LV: Found uniform instruction: %dec = add nsw i32 %len.0288, -1 LV: Found trip count: 0 LV: The Smallest and Widest types: 64 / 64 bits. LV: The Widest register is: 128 bits. LV: Found an estimated cost of 0 for VF 1 For instruction: %dl.0291 = phi i64* [ %incdec.ptr335, %while.body320 ], [ %73, %while.body320.preheader ] LV: Found an estimated cost of 0 for VF 1 For instruction: %ll.0290 = phi i64* [ %incdec.ptr332, %while.body320 ], [ %74, %while.body320.preheader ] LV: Found an est...

Hi all, I'm trying to estimate model parameters in R for a pretty simple system of equations, but I'm having trouble. Here is the system of equations (all derivatives): eqAlgae <- (u_Amax * C_A) * (1 - (Q_Amin / Q_A)) eqQuota <- (p_max * R_V) / (K_p + R_V) - ((Q_A-Q_Amin)*u_Amax) eqResource <- -C_A * (p_max * R_V) / (K_p + R_V) eqSystem <- list(C_A = eqAlgae, Q_A = eqQuota,

Dear All, we (Ott Toomet and I) would like to add functions for maximum likelihood (ML) estimations of generalized tobit models of type 2 and type 5 (*see below) in my R package for microeconomic analysis "micEcon". So far we have called these functions "tobit2( )" and "tobit5( )". Are these classifications well known? How are these functions called in other

Hi, I would like estimate a model for function of production's Coob-Douglas using maximum likelihood. The model is log(Y)= beta[1]+beta[2]*log(L)+beta[3]*log(K). I tried estimate this model using the tools nlm ( ) and optim ( ) using the log-likelihood function below: > mloglik <- function (beta, Y, L, K) { + n <- length(Y) + sum ( (log(Y)-

On Tue, Aug 14, 2018 at 8:29 PM Richard W.M. Jones <rjones@redhat.com> wrote: > This option prints the estimated size of the data that will be copied > from the source disk. > > For interest, the test prints: > > 3747840 ../test-data/phony-guests/windows.img > Estimate: 3710976 > Why not use qemu-img measure on the overlay? It gives a conservative estimate that will never fail, based on...

...rom a random_sample_from_a_population, whose formula you mentioned. Another thing, though related of course, is the standard error of a parameter_estimate from a model. The standard error of a parameter estimate from a model is a measure of the precision with which the parameter was estimated. The standard lognormal distribution is a model with two parameters (there is another with three parameters): the mean and the standard deviation. When you fit that model -the lognormal distribution- to a sample, you are estimating these two parameters. If you maximise the likelihood for your d...

This option prints the estimated size of the data that will be copied from the source disk. Currently this overestimates by the size of the qcow2 header, but for real disk images that doesn't matter much. For example: $ virt-builder fedora-27 $ virt-v2v -i disk fedora-27.img -o null --machine-readable --print-estimate [...]...

"help" We want to estimate the number of caribou in Jasper. We recently conducted an aerial survey and saw 70 uncollared caribou and 8 of 11 collared caribou. We want to estimate the number of caribou in this population with 95% confidence limits. Gary White uses the hypergeometric distribution and determines the population estimates using maximum likelihood and 95%CL as

.... Recently I need to do the Nelson-Aalen estimtor in R. I searched through the R help manual and internet, but could not find such a R function. I tried another way by calculating the Kaplan-Meier estimator and take -log(S). However, the function only provides the summary of KM estimator but no estimated values. Could you please help me with this? I would highly appreciate your great help! Thanks, Tongtong ---------------------------------------------------------- IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed,...