Displaying 20 results from an estimated 5000 matches similar to: "problem with intervals in mixed model"
2004 Jul 16
1
specifying a function in nls
Hello. I am trying to understand the syntax of the nonlinear least
squares function (nls) when the function definition is made outside of
the call. Here is the context.
1. If I specify the following command, it works fine:
> fit2<-nls(
+ A~Am*(1-exp(-alpha*(I-LCP))),data=dat1,
+ start=list(Am=3.6,alpha=0.01,LCP=20))
2. Now, I want to be able to specify the function definition
2005 Feb 14
1
testing equality of variances across groups in lme?
Hello. I am fitting a two-level mixed model which assumes equality of
variance in the lowest-level residuals across groups. The call is:
fit3<-lme(CLnNAR~CLnRGR,data=meta.analysis,
+ na.action="na.omit",random=~1+CLnRGR|study.code)
I want to test the assumption of equality of variances across groups at
the lowest level. Can someone tell me how to do this? I know that one
2009 Apr 24
2
prediction intervals (alpha and beta) for model average estimates from binomial glm and model.avg (library=dRedging)
Hi all,
I was wondering if there is a function out there, or someone has written code for making confidence intervals around model averaged predictions (y~á+âx). The model average estimates are from the dRedging library?
It seems a common thing but I can't seem to find one via the search engines
Examples of the models are:
fit1 <- glm(y~ dbh, family = binomial, data = data)
fit2 <-
2010 Jan 19
1
A model-building strategy in mixed-effects modelling
Dear all,
Consider a completely randomized block design (let's use data(Oats)
irrespoctive of the split-plot design it was arranged in). Look:
library(nlme)
fit <- lme(yield ~ nitro, Oats, random = ~1|Block, method="ML")
fit2 <- lm(yield ~ nitro + Block, Oats)
anova(fit, fit2)
gives this:
Model df AIC BIC logLik Test L.Ratio p-value
fit 1 4 624.3245
2007 Jun 05
1
lme vs. SAS proc mixed. Point estimates and SEs are the same, DFs are different
R 2.3
Windows XP
I am trying to understand lme. My aim is to run a random effects regression in which the intercept and jweek are random effects. I am comparing output from SAS PROC MIXED with output from R. The point estimates and the SEs are the same, however the DFs and the p values are different. I am clearly doing something wrong in my R code. I would appreciate any suggestions of how I can
2013 Apr 09
2
R crash
I have a generalized linear model to solve. I used package "geepack". When
I use the correlation structure "unstructured", I get a messeage that- R
GUI front-end has stopped working. Why this happens? What is the solution?
The r codes are as follows:
a<-read.table("d:/bmt.txt",header=T")
2009 May 12
1
questions on rpart (tree changes when rearrange the order of covariates?!)
Greetings,
I am using rpart for classification with "class" method. The test data is
the Indian diabetes data from package mlbench.
I fitted a classification tree firstly using the original data, and then
exchanged the order of Body mass and Plasma glucose which are the
strongest/important variables in the growing phase. The second tree is a
little different from the first one. The
2005 Aug 29
1
Different sings for correlations in OLS and TSA
Dear list,
I am trying to re-analyse something. I do have two time series, one
of which (ts.mar) might help explaining the other (ts.anr). In the
original analysis, no-one seems to have cared about the data being
time-series and they just did OLS. This yielded a strong positive
correlation.
I want to know if this correlation is still as strong when the
autocorrelations are taken into account.
2005 Apr 23
1
question about about the drop1
the data is :
>table.8.3<-data.frame(expand.grid( marijuana=factor(c("Yes","No"),levels=c("No","Yes")), cigarette=factor(c("Yes","No"),levels=c("No","Yes")), alcohol=factor(c("Yes","No"),levels=c("No","Yes"))), count=c(911,538,44,456,3,43,2,279))
2009 Jul 28
2
A hiccup when using anova on gam() fits.
I stumbled across a mild glitch when trying to compare the
result of gam() fitting with the result of lm() fitting.
The following code demonstrates the problem:
library(gam)
x <- rep(1:10,10)
set.seed(42)
y <- rnorm(100)
fit1 <- lm(y~x)
fit2 <- gam(y~lo(x))
fit3 <- lm(y~factor(x))
print(anova(fit1,fit2)) # No worries.
print(anova(fit1,fit3)) # Likewise.
print(anova(fit2,fit3)) #
2009 May 22
1
bug in rpart?
Greetings,
I checked the Indian diabetes data again and get one tree for the data with
reordered columns and another tree for the original data. I compared these
two trees, the split points for these two trees are exactly the same but the
fitted classes are not the same for some cases. And the misclassification
errors are different too. I know how CART deal with ties --- even we are
using the
2018 Jan 17
1
Assessing calibration of Cox model with time-dependent coefficients
I am trying to find methods for testing and visualizing calibration to Cox
models with time-depended coefficients. I have read this nice article
<http://journals.sagepub.com/doi/10.1177/0962280213497434>. In this paper,
we can fit three models:
fit0 <- coxph(Surv(futime, status) ~ x1 + x2 + x3, data = data0) p <-
log(predict(fit0, newdata = data1, type = "expected")) lp
2005 Jun 15
1
anova.lme error
Hi,
I am working with R version 2.1.0, and I seem to have run into what looks
like a bug. I get the same error message when I run R on Windows as well as
when I run it on Linux.
When I call anova to do a LR test from inside a function, I get an error.
The same call works outside of a function. It appears to not find the right
environment when called from inside a function. I have provided
2010 Jul 31
2
Is profile.mle flexible enough?
Hi the list,
I am experiencing several issues with profile.mle (and consequently with
confint.mle) (stat4 version 2.9.2), and I have to spend a lot of time to
find workarounds to what looks like interface bugs. I would be glad to
get feedback from experienced users to know if I am really asking too
much or if there is room for improvement.
* Problem #1 with fixed parameters. I can't
2004 Dec 20
2
problems with limma
I try to send this message To Gordon Smyth at smyth at vehi,edu.au but it bounced
back, so here it is to r-help
I am trying to use limma, just downloaded it from CRAN. I use R 2.0.1 on Win XP
see the following:
> library(RODBC)
> chan1 <- odbcConnectExcel("D:/Data/mgc/Chips/Chips4.xls")
> dd <- sqlFetch(chan1,"Raw") # all data 12000
> #
> nzw <-
2009 Dec 06
1
R + Hull-White model using nonlinear least squares
Hi guys
I have data that contains the variances vt of the yields of 1, 2, 3, 4,
5,10, 20 year bonds. Assuming the Hull-White model for the yield of a t-year
zero-coupon bond, I have to estimate the ? of the Hull-White model using
nonlinear least squares and give a 95% con?dence interval for each
parameter. Please can you guys tell how to find out ? using R. Any
suggestion regarding what functions
2011 Sep 12
1
coxreg vs coxph: time-dependent treatment
Dear List,
After including cluster() option the coxreg (from eha package)
produces results slightly different than that of coxph (from survival)
in the following time-dependent treatment effect calculation (example
is used just to make the point). Will appreciate any explaination /
comment.
cheers,
Ehsan
############################
require(survival)
require(eha)
data(heart)
# create weights
2008 Jan 05
1
Likelihood ratio test for proportional odds logistic regression
Hi,
I want to do a global likelihood ratio test for the proportional odds
logistic regression model and am unsure how to go about it. I am using
the polr() function in library(MASS).
1. Is the p-value from the likelihood ratio test obtained by
anova(fit1,fit2), where fit1 is the polr model with only the intercept
and fit2 is the full polr model (refer to example below)? So in the
case of the
2009 Apr 15
2
inbound filed
i create inbound confi my confi is:
[incoming]
exten=> 18888246463,,1,Dial(SIP/8003,60,rT)
exten=> 6463,1,Dial(SIP/8003,60,rT)
exten=> 18888246463,,n,Wait(5)
exten=> 18888246463,,n,Hangup
but y calling and send this error in my CLI:
[Apr 15 09:58:48] NOTICE[26985]: chan_sip.c:14383 handle_request_invite:
Call from '101396_procall' to extension '8888246463' rejected
2012 Nov 08
2
Comparing nonlinear, non-nested models
Dear R users,
Could somebody please help me to find a way of comparing nonlinear, non-nested
models in R, where the number of parameters is not necessarily different? Here
is a sample (growth rates, y, as a function of internal substrate
concentration, x):
x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48)
y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,