similar to: how to calculate the return?

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

Hi, I would like to estimate something like y = a + b*d2*y + c*d3*y where the dummies are created from some vector d with three (actually many more) levels using factor(). But either there is included the variable y or d1*y. How could I get rid of these? Example: x = c(1,2,3,4,5,6,7,8) y = c(3,6,2,8,7,6,2,4) d = c(1,1,1,2,3,2,3,3) fd = factor(d) lm(x ~ fd*y) gives: Coefficients: (Intercept)

In the summary of the output of arma, there's a number Pr(>|t|), however, I don't know what is its meaning - at least, it doesn't _seem_ to be a Student's t distribution. Reproducible test case: x <- c(0.5, sin(1:9)) reg <- arma(x, c(1,0)) summary(reg) <output> Call: arma(x = x, order = c(1, 0)) Model: ARMA(1,0) Residuals: Min 1Q Median 3Q

I am wondering how to interpret the parameter estimates that lm() reports in this sort of situation: y = round(rnorm(n=24,mean=5,sd=2),2) A = gl(3,2,24,labels=c("one","two","three")) B = gl(4,6,24,labels=c("i","ii","iii","iv")) # Make both observations for A=1, B=4 missing y[19] = NA y[20] = NA data.frame(y,A,B) nonadd = lm(y ~

Hi, I would consider the calculation of r-squared in the following to be a bug, but then, I've been wrong before. It seems that R looks to see if the model contains an intercept term, and if it does not, computes r-squared in a way I don't understand. To my mind, the following are two alternative parametrizations of the same model, and should yield the same r-squared. Any insight much

Hi, I want to test for a contrast from a regression where I am regressing the columns of a matrix. In short, the following. X <- matrix(rnorm(50),10,5) Y <- matrix(rnorm(50),10,5) lm(Y~X) Call: lm(formula = Y ~ X) Coefficients: [,1] [,2] [,3] [,4] [,5] (Intercept) 0.3350 -0.1989 -0.1932 0.7528 0.0727 X1 0.2007 -0.8505 0.0520

Hello, To indicate probability densities instead of counts on a histogram, I specify freq = FALSE. However, I expect that summing all top y coordinates over all the intervals of the histogram will provide 1. 1) v <- c(0.2885, 0.2988, 0.3139, 0.2615, 0.3179, 0.3163, 0.2583, 0.3052, 0.2527, 0.3147, 0.3235, 0.2408, 0.2480, 0.3108, 0.3577, 0.2829, 0.2694, 0.3275, 0.3314, 0.2639, 0.3076,

Hello everyone, Recently, I faced a problem on explanatory of *Interaction variable* in Linear Regression, could anyone give me some help on how to explain that? the response variable Y is significantly correlated with *Interaction variable X* which is consisted of Continuous predictor A and Categorical predictor B. The Categorical predictor B has two factors B1 (value=1) and B2 (value=0). The

Hi All, I have data about seed predation (SP) in fruits of three differents colors (yellow, motted, dark) and in two fruiting seasons (2007, 2008). I performed a GLMM (lmer function, lme4 package) and the outcome showed that the interaction term (color:season) was significant, and some combinations of this interaction have significant Pr(>|z|), but I don't think they are the right

I asked this before and I am going to try again in more applied terms. I am trying to use R to extract variance components for a two-factor random effects model with both factors crossed. It would also be nice to generate some confidence intervals as well. For example, a data set using SAS Proc Mixed is below followed by the four variance component estimates and the respective confidence

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on

Hello, I have a data set which I am using to find a model with the most significant parameters included and most importantly, the p-values. The full model is of the form: sad[,1]~b_1 sad[,2]+b_2 sad[,3]+b_3 sad[,4]+b_4 sad[,5]+b_5 sad[,6]+b_6 sad[,7]+b_7 sad[,8]+b_8 sad[,9]+b_9 sad[,10], where the 9 variables on the right hand side are all indicator variables. The thing I don't understand

I'm fitting nonlinear functions to some growth data but I'm getting radically different results in R to another program (Prism). Furthermore the values from the other program give a better fit and seem more realistic. I think there is a problem with the results from the r nls function. The differences only occur with weighted data so I think I'm making a mistake in the weighting.

Full_Name: Jim Breaux Version: 1.9.0 OS: WinXP Submission from: (NULL) (209.78.110.135) According to the help for 'round' it is supposed to round to the even digit. However, see the following examples: In the following, R is rounding down: > round(0.3645, 3) [1] 0.364 > round(0.3655, 3) [1] 0.365 > round(0.3665, 3) [1] 0.366 > round(0.3675, 3) [1] 0.367 > round(0.3685,

Dear all, Even after a couple of hours looking at old messages I still haven't found a solution for my problem. I'm trying to fit an additive linear regression model with 2 effects, both fixed, to some dataset. The function contrasts(effectA) <- contr.sum can gaurantee that the coefficients per parameter sum to one, and the function dummy.coef provices the estimates of all

http://bugs.freedesktop.org/show_bug.cgi?id=14264 Summary: flash ad that kills your machine Product: swfdec Version: git Platform: Other OS/Version: All Status: NEW Severity: critical Priority: medium Component: library AssignedTo: swfdec at lists.freedesktop.org ReportedBy: riccardo at

I am trying to show some techniques to my graduate regression class. The textbook mentioned using bootstrap samples of regression coefficients for assessing variability. I decided to show them reasonably effective ways of doing the resampling. The following is a function I wrote to create bootstrap samples of coefficients from a fitted linear regression model. bsCoefSample <- ##

Dear R Users, Is the p-value reported in a two-tailed binomial exact test in error or is it a feature? If it is a feature, could someone provide a reference for its two-tailed p-value computations? Using Blaker's (2000 - Canad. J. Statist 28: 783-798) approach,the p-value is the minimum of the two-tailed probabilities $P \left(Y\geq y_{obs}\right)$ and $P\left(Y\leq y_{obs}\right)$

You can use htmlTreeParse and xpathApply from the XML library. something like: xpathApply( htmlTreeParse("http://blabla", useInt=T), "//td", function(x) xmlValue(x)) should do it. Gamma wrote: > > anyone care to explain how to read a html table, it's streaming data > (updated every second) and i am looking for a suitable function. > > The imported html

Dear R-help This is a follow-up to my previous post here: http://groups.google.com/group/r-help-archive/browse_thread/thread/d9b6f87ce06a9fb7/e9be30a4688f239c?lnk=gst&q=dobomode#e9be30a4688f239c I am working on developing an open-source automated system for running batch-regressions on very large datasets. In my previous post, I posed the question of obtaining VIF's from the output of