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What is the Linear Execution Model in Meteor?


What is the Linear Execution Model in Meteor?

By : user2949512
Date : November 17 2020, 01:00 AM
I wish this help you Node.js uses asynchronous event loops, which stops a function from blocking the entire server.
Meteor is build on top of Node Fibers, which are themselves built on top of the asynchronous event loop.
code :


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Linear Search and Reverse Linear Search dont have same execution time

Linear Search and Reverse Linear Search dont have same execution time


By : Justin West
Date : March 29 2020, 07:55 AM
it fixes the issue It completely depends on your computer architecture and operating system. However, an overview can be done.
CPU run lots of processes in a time and CPU is only capable of executing commands. Most basic operations are ALU operations inside CPU.
p-values of linear combination of coefficients in linear mixed model

p-values of linear combination of coefficients in linear mixed model


By : user2010463
Date : March 29 2020, 07:55 AM
Any of those help It would be nice to have a reproducible example. It's not very hard to set up your own test for this:
code :
ct <- c(0,1,3)        ## set up contrast (linear comb. of coefficients)
m <- fixef(m) %*% ct  ## mean of contrast
v <- c(t(ct) %*% vcov(m) %*% ct)   ## variance of contrast
stder <- sqrt(v)      ## standard error
tstat <- m/stder      ## t statistic
2*pt(abs(tstat),df=594,lower.tail=FALSE)
loop for linear model execution with changing vars names and data sets

loop for linear model execution with changing vars names and data sets


By : user3521535
Date : March 29 2020, 07:55 AM
To fix the issue you can do If I understand correctly, the lm objects can be kept in a list as following. The column that is not included in the formula is removed to keep it simple.
code :
# way 1 with loop    
f <- list()
dat <- list(dat1, dat2)
for(i in 1:2) f[[i]] <- lm(target ~ ., data = dat[[i]][,-3])

# way 2 with lapply
dat <- list(dat1[,-3], dat2[,-3])
lapply(dat, lm, formula = target ~ .)

[[1]]

Call:
FUN(formula = ..1, data = X[[1L]])

Coefficients:
(Intercept)      birds_1     snakes_1  
     4.3333      -0.6667      -0.3333  


[[2]]

Call:
FUN(formula = ..1, data = X[[2L]])

Coefficients:
(Intercept)      birds_2     snakes_2  
     -3            0            1  
R: Fit curve to points: what linear/non-linear model to use?

R: Fit curve to points: what linear/non-linear model to use?


By : igotsqledbythelastad
Date : March 29 2020, 07:55 AM
hope this fix your issue I personally think this question a dupe of this: `nls` fails to estimate parameters of my model but I would be cold-blooded if I close it (as OP put a bounty). Anyway, bounty question can not be closed.
So the best I could think of, is to post a community wiki answer (I don't want to get this bounty).
code :
fit <- lm(log(y) ~ log(x))
Estimating bias in linear regression and linear mixed model in R simulation

Estimating bias in linear regression and linear mixed model in R simulation


By : Kapil Jain
Date : March 29 2020, 07:55 AM
To fix the issue you can do There are a couple of methods of simulating bias. I'll take an easy example using a linear model. A linear mixed model could likely use a similar approach, however i am not certain it would go well for a generalized linear mixed model (I am simply not certain).
A simple method for estimating bias, when working with a simple linear model, is to 'choose' which model to estimate ones bias from. Lets say for example Y = 3 + 4 * X + e. I have chosen beta <- c(3,4), and as such i need to only simulate my data. For a linear model, the model assumptions are
code :
set.seed(1)
xseq <- seq(-10,10)
xlen <- length(xseq)
nrep <- 100
#Simulate X given a flat prior (uniformly distributed. A normal distribution would likely work fine as well)
X <- sample(xseq, size = xlen * nrep, replace = TRUE)
beta <- c(3, 4) 
esd = 1
emu <- 0
e <- rnorm(xlen * nrep, emu, esd)
Y <- cbind(1, X) %*% beta + e
fit <- lm(Y ~ X)
bias <- coef(fit) -beta

>bias
 (Intercept)            X 
0.0121017239 0.0001369908 
#Simulate linear model many times
model_frame <- cbind(1,X) 
emany <- matrix(rnorm(xlen * nrep * 1000, emu, esd),ncol = 1000)
#add simulated noise. Sweep adds X %*% beta across all columns of emany
Ymany <- sweep(emany, 1, model_frame %*% beta, "+")
#fit many models simulationiously (lm is awesome!)
manyFits <- lm(Y~X)
#Plot density of fitted parameters
par(mfrow=c(1,2))
plot(density(coef(manyFits)[1,]), main = "Density of intercept")
plot(density(coef(manyFits)[2,]), main = "Density of beta")
#Calculate bias, here i use sweep to substract beta across all rows of my coefficients
biasOfMany <- rowMeans(sweep(coef(manyFits), 1, beta, "-"))

>biasOfMany
  (Intercept)             X 
 5.896473e-06 -1.710337e-04 
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