similar to: How to generate variance of a new observation based on a linear regression model

Hi, folks, Here are the codes: ############## y=1:10 x=c(1:9,1) lin=lm(log(y)~x) ### log(y) is following Normal distribution x=5:14 prediction=predict(lin,newdata=x) ##prediction=predict(lin) ############### 1. The codes do not work, and give the error message: Error in eval(predvars, data, env) : numeric 'envir' arg not of length one. But if I use the code after the pound sign, it

Hi, folks, As I understand, Least-squares Estimate (second-moment assumption) and the Method of Maximum Likelihood (full distribtuion assumption) are used for linear regression. I do >?lm, but the help file does not tell me the model employed in R. But in the book 'Introductory Statistics with R', it indicates R estimate the parameters using the method of Least-squares. However it

Hi, folks, As seen in the following codes: x1=rlnorm(10) x2=rlnorm(10,mean=2) y=rlnorm(10,mean=10)### Fake dataset linmod=lm(log(y)~log(x1)+log(x2)) After the regression, I would like to know the mean of y. Since log(y) is normal and y is lognormal, I need to know the mean and variance of log(y) first. I tried mean (y) and mean(linmod), but either one is what I want. Any tips? Thanks in

Hi, everyone, Night. I have three questions about multiple linear regression in R. Q1: y=rnorm(10,mean=5) x1=rnorm(10,mean=2) x2=rnorm(10) lin=lm(y~x1+x2) summary(lin) ## In the summary, 'Residual standard error: 1.017 on 7 degrees of freedom', 1.017 is the estimate of the constance variance? Q2: beta0=lin$coefficients[1] beta1=lin$coefficients[2] beta2=lin$coefficients[3]

I would like to prepare the data for barplot. But I only have the data frame now. x1=rnorm(10,mean=2) x2=rnorm(20,mean=-1) x3=rnorm(15,mean=3) data=data.frame(x1,x2,x3) If there a way to put data within a specific range? The expected result is as follows: range x1 x2 x3 -10-0 2 5 1 (# points in this

Hi, this question may be off topic: the unbiased estimator of the variance of the errors in a linear regression moedel with p coefficients is: sigma2=sum((y-yi)^2)/(length(y)-p-1) But what if i estimate transformations of the dependent an independent variables (e.g. Box-Cox) too? May I calculate the variance using sigma2=sum((y-yi)^2)/(length(y)-2*p-1) or should I use the first formula

Apologize for not being clearer earlier. I would like to ask again. Thank Joris and Markleeds for response. Two examples: 1. Function 'var'. In R, it is the sum of square divided by (n-1) but not by n. (I know this in R class) 2. Function 'lm'. In R, it is the residual sum of square divied by (n-2) not by n, the same as in the least squares estimate. But the assumption following

Hi, folks, I would like to copy the output of summary(lm) and anova (lm) in R to my word file. But the output will be a mess if I just copy after I call summary and anova. ##################### x=rnorm(10) y=rnorm(10,mean=3) lm=lm(y~x) summary(lm) Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -1.278567 -0.312017 0.001938 0.297578 1.310113

Dear all, Suppose my data frame is as follows: id price distance 1 2 4 1 3 5 ... 2 4 8 2 5 9 ... n 3 7 n 8 9 I would like to calculate the rank-order correlation between price and distance for each id. cor(price,distance,method = "spearman") calculate a correlation for all. Then I tried to use apply(data,list='id',cor(price , distance , method =

I noticed that there is a '-run-pass' argument for llc. I am wondering if I can do a similar approach with machine level optimisations/passes for llc. Are those passes optional (so I can turn them off)? And how can I get MIR format as llc expects with '-run-pass'? Thanks a lot. Cheers, Yi On 22/9/17 15:10, Craig Topper wrote: > Have -O0 on your clang command line causes

Hi, folks, runif (n,min,max) is the typical code for generate R.V from uniform dist. But what if we need to fix the mean as 20, and we want the values to be integers only? Thanks [[alternative HTML version deleted]]

Hi Yi, The precompiler is in charge of translating the most common classes of the Java runtime library to llvm code (typically, java.lang.Object), in order to natively compile these classes and decrease the bootstrap time. So, if you have a segmentation fault at this stage, it means that you have broken something in the interface between MMTk and VMKit. If it can helps you: * during a first

My code looks like this: lin = NA for(i in 1:15){ lin[i] = lm(reservesub[,3]~ reservesub[,i+3]) } For which I'm given 15 warning messages which say : "1: In lin[i] = lm(reservesub[, 3] ~ reservesub[, i + 3]) : number of items to replace is not a multiple of replacement length" I'm am able to generate the 15 different models and get the coefficients. I am able to access

Have -O0 on your clang command line causes all functions to get marked with an 'optnone' attribute that prevents opt from being able to optimize them later. You should also add "-Xclang -disable-O0-optnone" to your command line. ~Craig On Thu, Sep 21, 2017 at 10:04 PM, Yi Lin via llvm-dev < llvm-dev at lists.llvm.org> wrote: > Hi all, > > I am trying to

Hi All, I'm accustomed to performing an ANOVA to aid in choosing between linear models (for example y~x or y~x+x^2), however with different models I can't seem to do it. I'm trying to fit an exponential model of the form ye^(bt). Below is a code snippet that highlights what I'm trying to do s = rnorm(100, sd = 1, mean=10) s = s + seq(0.1,10,0.1) x = 1:length(s) model.lin = lm(s

Dear All, have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

Hello R community, I am looking for a robust test for equality of variances that can take observation weights. I realize I can do the F-test with weighted variances, but I've read that this test is not very robust. So I thought about maybe adding a "weights" argument to John Fox's code for the Levene Test (in the "car" library, "levene.test"), substituting

Dear R users, I'm using the function coxme of the package coxme in order to build Cox models with complex random effects. Unfortunately, I sometimes get surprising estimations of the variances of the random effects. I ran models with different fixed covariates but always with the same 3 random effects defined by the argument varlist=coxmeMlist(list(mat1,mat2,mat3), rescale = F, pdcheck = F,

Hi, folks linmod=y~x+z summary(linmod) The summary of linmod shows the standard error of the coefficients. How can we get the sd of y and the robust standard errors in R? Thanks! [[alternative HTML version deleted]]

Hi, folks, Please first look at the codes: plan_a=c('apple','orange','apple','apple','pear','bread') plan_b=c('bread','bread','orange','bread','bread','yogurt') value=1:6 data=data.frame(plan_a,plan_b,value) library(plyr) library(reshape) mm=melt(data, id=c('plan_a','plan_b'))