Displaying 20 results from an estimated 700 matches similar to: "packages masking other objects"
2008 Nov 25
4
glm or transformation of the response?
Dear all,
For an introductory course on glm?s I would like to create an example to show the difference between
glm and transformation of the response. For this, I tried to create a dataset where the variance
increases with the mean (as is the case in many ecological datasets):
poissondata=data.frame(
response=rpois(40,1:40),
explanatory=1:40)
attach(poissondata)
However, I have run into
2009 Mar 09
1
lme anova() and model simplification
I am running an lme model with the main effects of four fixed variables (3
continuous and one categorical – see below) and one random variable. The
data describe the densities of a mite species – awsm – in relation to four
variables: adh31 (temperature related), apsm (another plant feeding mite)
awpm (a predatory mite), and orien (sampling location within plant – north
or south).
I have read
2010 Sep 29
1
Understanding linear contrasts in Anova using R
#I am trying to understand how R fits models for contrasts in a
#simple one-way anova. This is an example, I am not stupid enough to want
#to simultaneously apply all of these contrasts to real data. With a few
#exceptions, the tests that I would compute by hand (or by other software)
#will give the same t or F statistics. It is the contrast estimates that
R produces
#that I can't seem to
2006 Sep 12
4
variables in object names
Is there any way to put an argument into an object name. For example,
say I have 5 objects, model1, model2, model3, model4 and model5.
I would like to make a vector of the r.squares from each model by code
such as this:
rsq <- summary(model1)$r.squared
for(i in 2:5){
rsq <- c(rsq, summary(model%i%)$r.squared)
}
So I assign the first value to rsq then cycle through models 2 through
2005 Jul 15
1
nlme and spatially correlated errors
Dear R users,
I am using lme and nlme to account for spatially correlated errors as
random effects. My basic question is about being able to correct F, p, R2
and parameters of models that do not take into account the nature of such
errors using gls, glm or nlm and replace them for new F, p, R2 and
parameters using lme and nlme as random effects.
I am studying distribution patterns of 50 tree
2010 Oct 03
5
How to iterate through different arguments?
If I have a model line = lm(y~x1) and I want to use a for loop to change the
number of explanatory variables, how would I do this?
So for example I want to store the model objects in a list.
model1 = lm(y~x1)
model2 = lm(y~x1+x2)
model3 = lm(y~x1+x2+x3)
model4 = lm(y~x1+x2+x3+x4)
model5 = lm(y~x1+x2+x3+x4+x5)...
model10.
model_function = function(x){
for(i in 1:x) {
}
If x =1, then the list
2005 Aug 12
1
Problem with lme4
Hi,
I cannot seem to get lme4 to work. I have installed the lme4 and Matrix
package with apt-get. and both can be found in /usr/lib/R/site-library.
When I tried an example for lmer, R could not find the function lmer(),
> library(lme4)
Attaching package: 'lme4'
The following object(s) are masked from package:nlme :
getCovariateFormula getResponseFormula
2010 Mar 28
1
keeping track of who did what
hello everybody.
i need to log "who did what" on some models. i was reading recipe 59
of rails recipes and i created something like that:
class LogSweeper < ActionController::Caching::Sweeper
observe :model1, :model2, :model3, :model4, :model5, ...
after_save(model)
save_log(model, "save")
end
after_destroy(model)
save_log(model, "destroy)
end
2011 Sep 28
2
GAMs in R : How to put the new data into the model?
I have 5 GAMs ( model1, model2, model3, model4 and model5)
Before I use some data X(predictor -January to June data) to form a equation
and calculate the expected value of Y (predictand -January to June). After
variable selection, GAMs (Model 1)were bulit up! R-square :0.40
NOW, I want to use new X'( predictor -July - December data) and put into
Model 1, then get the expected value of Y'
2009 Feb 09
1
gee with auto-regressive correlation structure (AR-M)
Dear all,
I need to fit a gee model with an auto-regressive correlation structure and I faced some problems.
I attach a simple example:
#######################################################
library(gee)
library(geepack)
# I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS
set.seed(1)
y <- rpois(500,50)
x <- rnorm(500)
id <- rep(1:50,each=10)
# EXAMPLES FOR
2004 Dec 19
1
PBIB datataset
I'm looking at Pinheiro & Bates "Mixed-Effects Models in
S and S-PLUS" at the moment. Several datasets are used,
one of which is called "PBIB" (a partially balanced
incomplete block design).
All the other datasets can be found somewhere or other in R.
However, I cannot locate PBIB, and it does not seem to
be mentioned in the latest edition of the R Full Reference
2007 Oct 17
1
problem with anova() and syntax in lmer
Dear R user
I have 2 problems with lmer.
The statistical consultance service of my university has recomended to me to
expose those problems here.
Sorry for this quite long message.
Your help will be greatly appreciated...
Gilles San Martin
1) anova()
I fit a first model :
model1 <- lmer(eclw~1 + density + landsc + temp + landsc:temp + (1|region) +
(1|region:pop) + (1|region:pop:family),
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,
2011 Apr 14
1
mixed model random interaction term log likelihood ratio test
Hello,
I am using the following model
model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair),
data=MateChoice, REML=F)
1. After reading around through the R help, I have learned that the above
code is the right way to analyze a mixed model with the MatingPair as the
fixed effect, DrugPair as the random effect and the interaction between
these two as the random effect as well.
2009 Mar 31
1
using "substitute" inside a legend
Hello list,
I have a linear regression:
mylm = lm(y~x-1)
I've been reading old mail postings as well as the plotmath demo and I came
up with a way to print an equation resulting from a linear regression:
model = substitute(list("y"==slope%*%"x", R^2==rsq),
list(slope=round(mylm$coefficients[[1]],2),rsq=round(summary(mylm)$adj.r.squared,
2)))
I have four models and I
2010 Feb 09
1
Missing interaction effect in binomial GLMM with lmer
Dear all,
I was wondering if anyone could help solve a problem of a missing interaction effect!!
I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,
2002 Jun 20
2
scatterplot3d
Hello,
I am trying to replicate example 4 in the package 'scatterplot3d':
s3d.dat_data.frame(cols=as.vector(col(my.model4)),
rows=as.vector(row(my.model4)),
value=as.vector(my.model4))
scatterplot3d(s3d.dat, type="h", lwd=5, pch=" ",
x.ticklabs=colnames(my.model4), y.ticklabs=rownames(my.model4),
main="Conditional probabilities, Model 3")
Nice! but,
2001 Jul 31
4
nlme: bug in getCovariateFormula (PR#1038)
I found that predict.gnls failed with a wierd error message about a
non-numeric argument to a binary vector in one of three nearly identical
uses.
Error in Inh/Ki : non-numeric argument to binary operator
(Inh and Ki are arguments to the function used in the formula for the
object whose predictions were requested).
It turns out that the problem is in getCovariateFormula().
The final line in
2009 Dec 10
1
updating arguments of formulae
Dear R-Community,
I am relatively new with R, so sorry for things which for you might be
obvious...
I am trying to automatically update lmer formulae.
the variables of the model are:
depM= my dependent measure
Sb2= a random factor
OS = a predictor
VR= another predictor
So, I am building the first model with random intercept only:
model = lmer(depM ~ (1 |Sb2))
then I update the formula
2012 Mar 14
1
lme code help
Hi guys,
Got a few days left and I need to model a random effect of species on the
body mass (logM) and temperature (K) slopes. This is what i've done so far
that works:
model1<-lme(logSSP~logM + K,random=~1|species,data=data1)
model2<-lme(logSSP~logM + K,random=~K|species,data=data1)
model3<-lme(logSSP~logM + K,random=~logM|species,data=data1)
The one I now want is: