Displaying 20 results from an estimated 10000 matches similar to: "Bivariate linear regression"
2010 Aug 25
2
Comparing samples with widely different uncertainties
Hi
This is probably more of a statistics question than a specific R
question, although I will be using R and need to know how to solve the
problem in R.
I have several sets of data (ejection fraction measurements) taken in
various ways from the same set of (~400) patients (so it is paired data).
For each individual measurement I can make an estimate of the percentage
uncertainty in the
2009 May 26
2
(OT) Does pearson correlation assume bivariate normality of the data?
Dear all,
The other day I was reading this post [1] that slightly surprised me:
"To reject the null of no correlation, an hypothsis test based on the
normal distribution. If normality is not the base assumption your
working from then p-values, significance tests and conf. intervals
dont mean much (the value of the coefficient is not reliable) " (BOB
SAMOHYL).
To me this implied that in
2009 Nov 12
1
naive "collinear" weighted linear regression
Hi there
Sorry for what may be a naive or dumb question.
I have the following data:
> x <- c(1,2,3,4) # predictor vector
> y <- c(2,4,6,8) # response vector. Notice that it is an exact,
perfect straight line through the origin and slope equal to 2
> error <- c(0.3,0.3,0.3,0.3) # I have (equal) ``errors'', for
instance, in the measured responses
Of course the
2006 Apr 23
3
bivariate weighted kernel density estimator
Is there code for bivariate kernel density estimation?
For bivariate kernels there is
kde2d in MASS
kde2d.g in GRASS
KernSur in GenKern
(list probably incomplete)
but none of them seems to accept a weight parameter
(like density does since R 2.2.0)
--
Erich Neuwirth, University of Vienna
Faculty of Computer Science
Computer Supported Didactics Working Group
Visit our SunSITE at
2009 Jun 15
1
Linear Models: Explanatory variables with uncertainties
One of the assumptions, on which the (General) Linear Modelling is
based is that the response variable is measured with some
uncertainties (or weighted), but the explanatory variables are fixed.
Is it possible to extend the model by assigning the weights to the
explanatory variables as well? Is there a package for doing such a
model fit?
Thanks
2013 Jun 12
2
survreg with measurement uncertainties
Hello,
I have some measurements that I am trying to fit a model to. I also
have uncertainties for these measurements. Some of the measurements
are not well detected, so I'd like to use a limit instead of the
actual measurement. (I am always dealing with upper limits, i.e. left
censored data.)
I have successfully run survreg using the combination of well detected
measurements and limits,
2024 Jan 08
1
how to specify uncorrelated random effects in nlme::lme()
Dear professor,
I'm using package nlme, but I can't find a way to specify two uncorrelated random effects. For example, a random intercept and a random slope. In package lme4, we can specify x + (x ll g) to realize, but how in nlme?
Thanks!
????????????????????????
Zhen Wang
Graduate student, Department of Medical Statistics, School of Public Health, Sun Yat-sen
2003 Jun 13
5
covariate data errors
Greetings,
I would like to fit a multiple linear regression model in
which the residuals are expected to follow a multivariate normal
distribution, using weighted least squares. I know that the data in
question have biases that would result in correlated residuals, and I
have a means for quantifying those biases as a covariance matrix. I
cannot, unfortunately, correct the data for these biases.
2009 Jun 20
1
error ellipse
Dear All,
I have a data set with the following structure:
[A], [a], [B], [b]
where [A] and [B] are measurements and [a] and [b] are the associated
uncertainties. I produce [B]/[A] vs. [A] plots in R and would like to
show uncertainties as error ellipses (rather than error bars). Would
this be relatively easy to do in R?
I would appreciate any help on this
Thanks a lot
Tibi
2005 Nov 03
1
How to calculate errors in histogram values
Hi there,
I'm new to R but I thought this is the most likely place I could get advice or
hints w.r.t the following problem:
I have a series of measurements xi with associated uncertainties dxi. I would
like to construct the probability density histogram of this data where each
density estimate has an associated error that is derived from the dxi. In
other words, for large dxi the
2017 Jul 12
1
metRology package
I'm having trouble with a simple application with metRology. I need to
estimate the uncertainty of the density thickness of seven sheets of film.
This is calculated from measurements of mass, length and width of
rectangular samples of film.
It's not too hard to calculate the whole thing with a little Monte Carlo
loop. I get about 0.07 with this:
#sample area
2006 Aug 28
2
Help with Functions
Hello wizards, I need to convert the following functions (prestd,
poststd, prepca) of matlab to R. Does Somebody knows how to do it. A
description of the functions is:
prestd preprocesses the network training set by normalizing the inputs
and targets so that they have means of zero and standard deviations of
1.
poststd postprocesses the network training set which was preprocessed
by prestd. It
2004 May 13
2
please help with estimation of true correlations and reliabilities
Can someone point me to literature and/or R software to solve the following
problem:
Assume n true scores t measured as x with uncorrelated errors e , i.e.
x = t + e
and assume each true score to a have a certain amount of correlation with
some of the other true scores.
The correlation matrix cx of x will have its off-diagonal entries reduced by
measurement error compared to the true
2010 Jan 01
2
How to calculate density function of Bivariate binomial distribution
Am trying to do some study on bivariate binomial distribution. Anyone knows
if there is package in R that I can use to calculate the density function of
bivariate binomial distribution and to generate random samples of it.
Thanks,
--
View this message in context: http://n4.nabble.com/How-to-calculate-density-function-of-Bivariate-binomial-distribution-tp992002p992002.html
Sent from the R help
2004 Jan 13
3
How can I test if a not independently and not identically distributed time series residuals' are uncorrelated ?
I'm analizing the Argentina stock market (merv)
I download the data from yahoo
library(tseries)
Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close")
merv <- na.remove(log(Argentina))
I made the Augmented Dickey-Fuller test to analyse
if merv have unit root:
adf.test(merv,k=13)
Dickey-Fuller = -1.4645,
2012 Feb 06
1
Simple lm/regression question
I am trying to use lm for a simple linear fit with weights. The results
I get from IDL (which I am more familiar with) seem correct and
intuitive, but the "lm" function in R gives outputs that seem strange to me.
Unweighted case:
> x<-1:4
> y<-(1:4)^2
> summary(lm(y~x))
Call:
lm(formula = y ~ x)
Residuals:
1 2 3 4
1 -1 -1 1
Coefficients:
2008 Jan 06
2
how to get residuals in factanal
In R factanal output, I can't find a function to give me residuals e.
I mannually got it by using x -lamda1*f1 -lamda2*f2 - ... -lamdan*fn, but the e
I got are not uncorrelated with all the f's.
What did I do wrong? Please help.
Yijun
____________________________________________________________________________________
Be a better friend, newshound, and
2009 Jul 07
6
Uncorrelated random vectors
Hello,
is it possible to create two uncorrelated random vectors for a given distribution.
In fact, I would like to have something like the function "rnorm" or "rlogis" with the extra property that they are uncorrelated.
Thanks for your help,
Luba
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2010 Feb 10
3
Sampling from Bivariate Uniform Distribution
Hello all!!!
1) I am wondering is there a way to generate random numbers in R for Bivariate Uniform distribution?
2) Does R haveĀ built-in function for generating random numbers for any given bivariate distribution.
Any help would be greatly appreciated !!
Good day!
Haneef Anver
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2006 Mar 29
2
bivariate case in Local Polynomials regression
Hi:
I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The