Displaying 20 results from an estimated 59 matches for "untransformed".
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2012 Jan 07
2
glm or transformation of the response?
...lp>
801.408.8111
I am not using R at the moment (working in SPSS, have to love the GUI) but
my question is quite related:
I am running a generalized linear model on data highly skewed to the right
with a bunch of zeroes, so I decided to use the Tweedie distribution. In the
model I ran both untransformed data (with link=log) as well as log(x+1)
transformed data (with link=identity). The latter model had a much smaller
(more negative) AICc value than the untransformed data with link=log.
Is it valid to run the GLM with log(x+1) transformed data if link=identity?
Or am I violating some kind of assu...
2006 Mar 15
2
comparing AIC values of models with transformed, untransformed, and weighted variables
Hi there, I have a question regarding model comparisons that seems
simple enough but to which I cannot find an answer. I am interested in
developing a predictive model relating some measure of a tree's stem to
the total leaf area (TLA) of the tree. Predictor variables might
include, for example, the total cross-sectional area of the tree
(commonly referred to as basal area) or the amount
2017 Oct 23
3
Linear regression with tranformed dependant variable
...ity assumption was not verified...).?I have realised a sqrt(variable) transformation...?The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in the original untransformed dependant variable ??Thank you very much for your help!No?mie?
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2009 Dec 04
2
curve fitting to data
...3,42.83,46.53,14.79,49.18,48.08)
If I plot the data, it looks somehow that a logistic function would
render good results.
My questions are:
How do I use
nls and/or SSlogis (or other)
to fit the curve?
How can I see the summary statistics of the fit?
How do I finally draw the line to my x,y (untransformed data) plot?
Any help would be highly appreciated.
Thank you and cheers
Pascale
--
____________________________________..___________________
Dr. Pascale Weber
Swiss Federal Research Institute WSL
Zuercherstrasse 111
CH-8903 Birmensdorf
Switzerland
2009 Jun 09
2
Isolating a single plot from plots produced simultaneously
...key must be pressed after
each graph appears (Click or hit ENTER for next page).
I'd like to isolate the second plot produced (the estimated functional
form of the influence of age on the log relative hazard) so that I can
use the 'points' function to add the linear predictors for the
untransformed and the log-transformed models. In the usual situation
one would produce a plot and then type:
coxfitu <- coxph(Surv(rem.Remtime,rem.Rcens)~age+strata(rpa),data=nearma)
points(coxfitu$linear.predictor,col=2)
coxfitl <- coxph(Surv(rem.Remtime,rem.Rcens)~log(age)+strata(rpa),data=nearma)
poin...
2017 Oct 23
0
Linear regression with tranformed dependant variable
...ity assumption was not verified...). I have realised a sqrt(variable) transformation... The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in the original untransformed dependant variable ? Thank you very much for your help!No?mie
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2010 Jul 09
3
apply is slower than for loop?
I thought the "apply" functions are faster than for loops, but my most
recent test shows that apply actually takes a significantly longer than a
for loop. Am I missing something?
It doesn't matter much if I do column wise calculations rather than row wise
## Example of how apply is SLOWER than for loop:
#rm(list=ls())
## DEFINE VARIABLES
mu=0.05 ; sigma=0.20 ; dt=.25 ; T=50 ;
2004 Aug 06
2
Status.xml
...located? Is there any
> way I can store status.xml or status.xsl on a remote HTTP/FTP (not an
> Icecast2 relay) server?
tatus.xml does not exist as a real file. It is generated by the icecast2
server on demand, and served after going through an XSL transformation (it
may also be available untransformed - it was a while ago, but I'm not certain
that's still available).
tatus.xsl must live in the local filesystem for icecast2 to find it.
Mike
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2009 Jun 23
0
Fractional Polynomials in Competing Risks setting
Dear All,
I have analysed time to event data for continuous variables by
considering the multivariable fractional polynomial (MFP) model and
comparing this to the untransformed and log transformed model to
determine which transformation, if any, is best. This was possible as
the Cox model was the underlying model. However, I am now at the
situation where the assumption that the competing risks are
independent is no longer true and therefore I cannot use the Cox
model....
2012 May 15
1
Lattice: Add abline to Single Value qqmath() Plot
The data are not normally distributed when untransformed and I'm trying
various transformations to see if any would be appropriate to use. The
lattice book (fig. 3.10) shows a 2-sample Q-Q plot with an abline but the
code for the figure does not include the line.
I'd appreciate a pointer to a reference on how to add an abline to a
one-sample...
2001 Feb 05
1
clipped lines have wrong slope in log plot (PR#839)
Hi,
I'm using R version 1.2.0 (2000-12-15), on RedHat Linux 6.2 (kernel 2.2.14).
The following command ought to plot a straight-line on a log-log
graph but instead plots two line segments, both with the wrong
slope:
plot(c(1,10,100),c(100,10,1),type="l",xlim=c(1,20),ylim=c(1,20),log="xy")
Only clipped lines seem to have this problem. Changing to type "b"
or
2010 Feb 12
1
using mle2 for multinomial model optimization
...prod<-(1-h[1])*S[1]
for (i in 2:(l-1)){
p[i]<-prod*h[i]
prod<-prod*(1-h[i])*S[i]
}
p[l]<-1-sum(p[-l]) #last cell probability
-dmultinom(exp(d),prob=p,log=TRUE) #exp(d)->backtransform the estimates
}
c<-c(cohort,100) #untransformed initial values for the
optimization,->100=N-x1-x2-x3
nvec<-c(rep("x",l-1),"N") #names for the initial vector
nrs<-c(1:(l-1),1) #nrs for the initial vector
svec = log(c) #transformation of the data to avoid
constraints (x>0)
names(svec) &l...
2011 Sep 07
1
linear regression, log-transformation and plotting
..._point(aes(b1,a1,data=data1))+
geom_abline(aes(intercept=coef(model)[1],slope=coef(model)[2]))+
scale_y_log()+
scale_x_log()
# Plot with standard plot
plot(b1,a1,log="xy")
abline(model,untf=T)
abline(model,untf=F)
1) The regression lines are different for plot vs. ggplot(transformed or untransformed). So what is actually the correct line?
2) The regression line was calculated on basis of log(x+1), but the log scale on my axis is just simple log (without +1). So how are such cases usually treated? I thought about subtracting the value 1 from the intercept?
So my simple question: What is the b...
2012 Mar 08
5
uncompressed FLAC
Hi
i have seen that the dbPowerAmp ripping and encoding software supports a
new so-called "FLAC uncompressed" format, e.g.
http://www.audiostream.com/content/dbpoweramps-flac-lossless-uncompressed-wish-come-true
i know only the normal flac compression levels from 0 to 8. have i
missed an option on the flac comamnd line tool or how could i achieve
that on the linux command line flac
2010 Nov 30
3
Outlier statistics question
I have a statistical question.
The data sets I am working with are right-skewed so I have been
plotting the log transformations of my data. I am using a Grubbs Test
to detect outliers in the data, but I get different outcomes depending
on whether I run the test on the original data or the log(data). Here
is one of the problematic sets:
fgf2p50=c(1.563,2.161,2.529,2.726,2.442,5.047)
2010 Nov 30
1
researcher with highly skewed data set seeks help finding practical GLMM tutorial
Hi!
I am a psychologist who suspects that the only sensible way to analyse
a particular data set is to use generalised linear mixed models. I am
hoping that someone might be able to point me in the right direction
to find some very practical hands on documentation that might be able
to talk me through actually doing such an analysis?
So far in my searches the most useful document I have turned
2005 Jul 27
7
gamma distribution
Hi R Users
This is a code I wrote and just want to confirm if the first 1000 values are raw
gamma (z) and the next 1000 values are transformed gamma (k) or not. As I get
2000 rows once I import into excel, the p - values beyond 1000 dont look that
good, they are very high.
--
sink("a1.txt");
for (i in 1:1000)
{
x<-rgamma(10, 2.5, scale = 10)
y<-rgamma(10, 2.5, scale = 10)
2008 Jan 07
3
Seeking a more efficient way to find partition maxima
Hi.
Suppose I have a vector that I partition into disjoint, contiguous subvectors. For example, let v = c(1,4,2,6,7,5), partition it into three subvectors, v1 = v[1:3], v2 = v[4], v3 = v[5:6]. I want to find the maximum element of each subvector. In this example, max(v1) is 4, max(v2) is 6, max(v3) is 7. If I knew that the successive subvector maxima would never decrease, as in the example,
2011 Feb 08
1
which multivariate regression?
..., log(x+1), ln,
sqrt, fourth root).
What type of analyses have I tried?
(1) Regression trees.
Using categorical variables as categorical without changing into
numerical. This was coded with package rpart and is the preferred analyses
due to ease of interpretation. The response variable was untransformed and
the distribution chosen Poisson. Result was a tree with immediately
increasing error (cp) which picked 0 splits as the best tree.
(2) Multiple regression
Tried using package relaimpo to obtain a classification on the
importance of explanatory variables. Used different transformations to
an...
2008 Feb 16
4
Weird SEs with effect()
Hi all,
Im a little bit confused concerning the effect() command, effects package.
I have done several glm models with family=quasipoisson:
model <-glm(Y~X+Q+Z,family=quasipoisson)
and then used
results.effects <-effect("X",model,se=TRUE)
to get the "adjusted means". I am aware about the debate concerning
adjusted means, but you guys just have to trust me - it