Displaying 20 results from an estimated 7000 matches similar to: "Printing correlation matrices (lm/glm)"
2003 Jul 30
2
Comparing two regression slopes
Hello,
I've written a simple (although probably overly roundabout) function to
test whether two regression slope coefficients from two linear models on
independent data sets are significantly different. I'm a bit concerned,
because when I test it on simulated data with different sample sizes and
variances, the function seems to be extremely sensitive both of these. I am
wondering if
2005 Aug 12
1
as.formula and lme ( Fixed effects: Error in as.vector(x, "list") : cannot coerce to vector)
This is a continuing issue with the one on the list a long time ago (I
couldn't find a solution to it from the web):
--------------------------------------------------------------------------
> Using a formula converted with as.formula with lme leads
> to an error message. Same works ok with lm, and with
> lme and a fixed formula.
>
> # demonstrates problems with lme and
2010 Apr 08
2
Overfitting/Calibration plots (Statistics question)
This isn't a question about R, but I'm hoping someone will be willing
to help. I've been looking at calibration plots in multiple regression
(plotting observed response Y on the vertical axis versus predicted
response [Y hat] on the horizontal axis).
According to Frank Harrell's "Regression Modeling Strategies" book
(pp. 61-63), when making such a plot on new data
2001 Feb 23
1
as.formula and lme ( Fixed effects: Error in as.vector(x, "list") : cannot coerce to vector)
Using a formula converted with as.formula with lme leads
to an error message. Same works ok with lm, and with
lme and a fixed formula.
# demonstrates problems with lme and as.formula
demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10)))
demo.lm1<-lme(y~x,data=demo,random=~1|subj)
print(summary(demo.lm1))
newframe<-data.frame(x=1:5,subj=rep(1,5))
2012 Jan 13
2
Help needed in interpreting linear models
Dear members of the R-help list,
I have sent the email below to the R-SIG-ME list to ask for help in
interpreting some R output of fitted linear models.
Unfortunately, I haven't yet received any answers. As I am not sure if my
email was sent successfully to the mailing list I
am asking for help here:
Dear members of the R-SIG-ME list,
I am new to linear models and struggling with
2006 Mar 10
1
add trend line to each group of data in: xyplot(y1+y2 ~ x | grp...
Although this should be trivial, I'm having a spot of trouble.
I want to make a lattice plot of the format y1+y2 ~ x | grp but then fit a
lm to each y variable and add an abline of those models in different colors.
If the xyplot followed y~x|grp I would write a panel function as below, but
I'm unsure of how to do that with y1 and y2 without reshaping the data
before hand. Thoughts
2007 Aug 06
1
test the significances of two regression lines
R-help,
I'm trying to test the significance of two regression lines
, i.e. the significance of the slopes from two samples
originated from the same population.
Is it correct if I fit a liner model for each sample and
then test the slope signicance with 'anova'. Something like this:
lm1 <- lm(Y~ a1 + b1*X) # sample 1
lm2 <- lm(Y~ a2 + b2*X) # sample 2
anova(lm1, lm2)
2012 Jul 06
2
Anova Type II and Contrasts
the study design of the data I have to analyse is simple. There is 1 control group (CTRL) and 2 different treatment groups (TREAT_1 and TREAT_2).
The data also includes 2 covariates COV1 and COV2. I have been asked to check if there is a linear or quadratic treatment effect in the data.
I created a dummy data set to explain my situation:
df1 <- data.frame(
Observation =
2004 Aug 26
1
Why terms are dropping out of an lm() model
Hi all!
I'm fairly new to R and not too experienced with regression. Because
of one or both of those traits, I'm not seeing why some terms are being
dropped from my model when doing a regression using lm().
I am trying to do a regression on some experimental data d, which has
two numeric predictors, p1 and p2, and one numeric response, r. The aim
is to compare polynomial models in p1
2008 Nov 24
3
Is this correct?
I have to answer the following question for a homework assignment.
A researcher was interested in whether people taking part in sports at
university made more money after graduating, taking into account the
students' GPA. They sampled 200 alumni from a large university. The
variables are: income (income 10 years after graduating), sports (1 if they
did sports, 0 if they did not), and GPA (the
2014 Jun 13
3
p values con LMER
Hola Manuel
lo he tratado de hacer pero me sale
Error: unexpected string constante in:
"anova(a,as,test=Chisq")
no tengo ni idea de por qué...
Me resulta alucinante no poder contar ya con pvals.fnc. ¿Será imposible hacerse con ello?
Saludos,
Miguel
--------------------------------------------
El vie, 13/6/14, Manuel Azcárate <mazcarategarcia en gmail.com> escribió:
2003 Oct 28
1
error message in simulation
Dear R-users,
I am a dentist (so forgive me if my question looks stupid) and came across
a problem when I did simulations to compare a few single level and two
level regressions.
The simulations were interrupted and an error message came out like 'Error
in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1'.
My collegue suggested that this might be due to my codes
2013 Mar 18
1
try/tryCatch
Hi All,
I have tried every fix on my try or tryCatch that I have found on the
internet, but so far have not been able to get my R code to continue with
the "for loop" after the lmer model results in an error.
Here is two attemps of my code, the input is a 3D array file, but really
any function would do....
metatrialstry<-function(mydata){
a<-matrix(data=NA, nrow=dim(mydata)[3],
2005 Nov 17
1
anova.gls from nlme on multiple arguments within a function fails
Dear All --
I am trying to use within a little table producing code an anova
comparison of two gls fitted objects, contained in a list of such
object, obtained using nlme function gls.
The anova procedure fails to locate the second of the objects.
The following code, borrowed from the help page of anova.gls,
exemplifies:
--------------- start example code ---------------
library(nlme)
##
2014 Jun 13
3
p values con LMER
Existe discusión sobre el uso de los p-valores en modelos mixtos. Como se
ha dicho antes, para mi lo más adecuado es comparar modelos mediante la
función anova. Por Internet se puede encontrar un buen libro de Douglas
Bates y en español, busca modelos mixtos con R de Luis Cayuela, enfocado
hacia ecología, pero está muy bien
El 13/06/2014 14:00, "Jorge I Velez"
2008 Jun 04
1
Comparing two regression lines
Dear R users,
Suppose I have two different response variables y1, y2 that I regress separately on the same
explanatory variable, x; sample sizes are n1=n2.
Is it legitimate to compare the regression slopes (equal variances assumed) by using
lm(y~x*FACTOR),
where FACTOR gets "y1" if y1 is the response, and "y2" if y2 is the response?
The problem I see here is that the
2009 Jan 28
1
gls prediction using the correlation structure in nlme
How does one coerce predict.gls to incorporate the fitted correlation
structure from the gls object into predictions? In the example below
the AR(1) process with phi=0.545 is not used with predict.gls. Is
there another function that does this? I'm going to want to fit a few
dozen models varying in order from AR(1) to AR(3) and would like to
look at the fits with the correlation structure
2006 Mar 16
2
DIfference between weights options in lm GLm and gls.
Dear R-List users,
Can anyone explain exactly the difference between Weights options in lm glm
and gls?
I try the following codes, but the results are different.
> lm1
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
0.1183 7.3075
> lm2
Call:
lm(formula = y ~ x, weights = W)
Coefficients:
(Intercept) x
0.04193 7.30660
> lm3
Call:
2010 Jul 07
3
Large discrepancies in the same object being saved to .RData
Hi developers,
After some investigation I have found there can be large discrepancies in the same object being saved as an external "xx.RData" file. The immediate repercussion of this is the possible increased size of your .RData workspace for no apparent reason.
The function and its three scenarios below highlight these discrepancies. Note that the object being returned is exactly
2007 Jul 17
1
Speed up computing: looping through data?
Dear all,
Please excuse my ignorance, but I am having difficulty with this, and am
unable to find help on the website/Google.
I have a series of explanatory variables that I am aiming to get
parsimony out of.
For example, if I have 10 variables, a-j, I am initially looking at the
linear relationships amongst them:
my.lm1 <- lm(a ~ b+c+d+e+f+g+h+i+j, data=my.data)
summary(my.lm1)
my.lm2