Displaying 20 results from an estimated 800 matches similar to: "Again some questions about multilevelanalysis"
2004 Mar 21
1
Multilevel analysis with package lme
Dear list,
i am a student of psychology and have to do a multilevelanalysis on some data.
About that i have one general and one specific question.
This is what i have copied from the help-file on lme:
data(bdf)
fm <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen, data = bdf,
random = ~ IQ.ver.cen | schoolNR)
summary(fm)
after summary(fm) i get the following error:
2004 Feb 05
5
(Novice-) Problem with the plot-function
Hello,
i have written this little function to draw different normal distributions:
n.Plot <- function(x,my,sigma) {
e <- exp(1)
names(x) <- x
f.x <- (1/(sigma*sqrt(2*pi)))*e^(-1*(((x-my)^2)/2*(sigma^2)))
plot(f.x,type="l",xlim=c(-5,5))
return(f.x)
}
if i define x like this:
x <- seq(-5,5,0.01)
Now
n.Plot(x,0,1)
DOES draw the correct plot, but the x-axis is labeled
2004 May 08
3
Getting the groupmean for each person
Hello list !
I have a huge data.frame with several variables observed on about 3000
persons. For every person (row) there is variable called GROUP which indices
the group the person belongs to. There is also another variable AV for each
person. Now i want to create a new variable which holds the group mean of AV
as a value for each person.
With tapply(AV,GROUP,mean) i get the means for each
2007 Jun 15
2
Problem with workspace loading after languageR use
Hello R,
To analyze multi-level data, I started learning and using lmer. So far
so wonderful. I then found some useful functions in package languageR.
But then the following problem ocurred: Whenever I load and use the
languageR package, then save the workspace - or quit R with saving the
workspace - I am unable to reload that workspace in a later session.
That is, R doesn't start at all
2006 Feb 27
2
singular convergence in glmmPQL
I am using the 'glmmPQL function in the 'MASS' library to fit a mixed effects logistic regression model to simulated data. I am conducting a series of simulations, and with certain simulated datasets, estimation of the random effects logistic regression model unexpectedly terminates. I receive the following error message from R:
Error in lme.formula(fixed=zz + arm.long,random=~1 |
2009 Jul 09
1
apcsmart and dual environmental sensors
Hi,
We have a several AP9612TH environmental cards (they have 2 probe
connectors) inserted into our APC UPS devices which monitor temperature
and humidity. The apcsmart nut module knows how to query the
ambient.temperature and ambient.humidity using the 't' and 'h' commands
of the UPS (refer to apcsmart.h). The results of the 't' and 'h'
commands are from probe 1.
2008 Nov 14
3
Change Confidence Limits on a plot
Hi,
I am attempting to set the confidence limits on a ls means plot as follows:
mult<-glht(lm(effectModel, data=statdata, na.action = na.omit),
linfct=mcp(mainEffect="Means"))
meanPlot <- sub(".html", "meanplot.jpg", htmlFile)
jpeg(meanPlot)
plot(mult, main=NA, xlab=unlist(strsplit(Args[4],"~"))[1])
This produces 95% CIs by default but I would
2004 Feb 04
3
Various newbie questions
Hello,
1) What is the difference between a "data frame" (J H Maindonald, Using
R, p. 12) and a "vector"?
In Using R, the author asks the reader to enter the following data in a
data frame, which I will call "mydata":
year snow.cover
1970 6.5
1971 12.0
1972 14.9
1973 10.0
1974 10.7
1975 7.9
...
mydata=data.frame(year=c(1970,...),snow.cover=c(6.5,...))
2) How to
2001 May 06
1
legend/text in time series plot
hi,
i need help on placing legend/text in a time series plot. here is what
i am doing (i am using rw1022 on windoze 2000):
#read data file
gdpn <- scan("jngdpsa.dat", list(year=0, qtr=0, gdp=0));
gdpr <- scan("jrgdpsa.dat", list(year=0, qtr=0, gdp=0));
#convert to time series object
gdpn <- ts(gdpn$gdp, frequency=4, start=c(1955,2));
gdpr <- ts(gdpr$gdp,
2008 May 27
3
How to test significant differences for non-linear relationships for two locations
Hi List,
I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question.
I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I
2012 Jan 17
1
MuMIn package, problem using model selection table from manually created list of models
The subject says it all really.
Question 1.
Here is some code created to illustrate my problem, can anyone spot where I'm going wrong?
Question 2.
The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request,
2004 Apr 09
3
include/exclude bug in rsync 2.6.0/2.6.1pre1
As mentioned on the rsync home page, the --files-from=FILE option in rsync
version 2.6.0 is a useful option that allows one to "specify a list of
files to transfer, and can be much more efficient than a recursive descent
using include/exclude statements (if you know in advance what files you want to
transfer)".
However, --files-from does not help one implement the --rsync-exclude=FILE
2009 Apr 15
2
AICs from lmer different with summary and anova
Dear R Helpers,
I have noticed that when I use lmer to analyse data, the summary function
gives different values for the AIC, BIC and log-likelihood compared with the
anova function.
Here is a sample program
#make some data
set.seed(1);
datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y'
))))
id=rep(1:120,2); datx=cbind(id,datx)
#give x1 a
2011 Feb 06
1
anova() interpretation and error message
Hi there,
I have a data frame as listed below:
> Ca.P.Biomass.A
P Biomass
1 334.5567 0.2870000
2 737.5400 0.5713333
3 894.5300 0.6393333
4 782.3800 0.5836667
5 857.5900 0.6003333
6 829.2700 0.5883333
I have fit the data using logistic, Michaelis?Menten, and linear model,
they all give significance.
> fm1 <- nls(Biomass~SSlogis(P, phi1, phi2, phi3), data=Ca.P.Biomass.A)
2017 Jun 17
3
Prediction with two fixed-effects - large number of IDs
Dear all,
I am running a panel regression with time and location fixed effects:
###
reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 ,
data=mydata, na.action="na.omit")
###
My goal is to use the estimation for prediction. However, I have 8,500 IDs,
which is resulting in very slow computation. Ideally, I would like to do
the following:
###
reg2 <-
1997 Feb 05
0
bliss version 0.4.0
[mod: Forwarded by Jeff Uphoff. I tried to mangle the headers that
it appears as the original post: with an invalid return address. -- REW]
A few months back, a very alpha version of bliss got posted. That shouldn''t
have happened, but, it was pretty much ignored so I didn''t worry about it.
But now it seems there''s a bit of a fuss about this. I''ll post the
2009 Nov 01
1
package lme4
Hi R Users,
When I use package lme4 for mixed model analysis, I can't distinguish
the significant and insignificant variables from all random independent
variables.
Here is my data and result:
Data:
Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9),
Variety=rep(rep(c("A1","A2","A3"),each=3),3),
2017 Jun 17
0
Prediction with two fixed-effects - large number of IDs
I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf.
Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors.
Also, your use of I() is
2008 Apr 13
2
prediction intervals from a mixed-effects models?
How can I get prediction intervals from a mixed-effects model?
Consider the following example:
library(nlme)
fm3 <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1)
df3.1 <- with(Orthodont, data.frame(age=seq(5, 20, 5),
Subject=rep(Subject[1], 4),
Sex=rep(Sex[1], 4)))
predict(fm3, df3.1, interval='prediction')
# M01 M01
2012 Nov 15
1
Stepwise regression scope: all interacting terms (.^2)
Dear Gurus,
Thank you in advance for your assistance. I'm trying to understand scope better when performing stepwise regression using "step." I have a model with a binary response variable and 10 predictor variables. When I perform stepwise regression I define scope=.^2 to allow interactions between all terms. But I am missing something. When I perform stepwise regression (both