Displaying 20 results from an estimated 20000 matches similar to: "Plotting a smooth curve from predict"
2012 Apr 19
2
Gls function in rms package
Dear R-help,
I don't understand why Gls gives me an error when trying to fit a
model with AR(2) errors, while gls (from nlme) does not. For example:
library(nlme)
library(rms)
set.seed(1)
d <- data.frame(x = rnorm(50), y = rnorm(50))
gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works
Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error
# Error in
2006 Mar 13
1
P-values in gls
When fitting a simple linear or polynomial regression using lm, R
provides a p-value for the whole model as well as for the individual
coefficients. When fitting the same models using gls (in order to
correct for autocorrelation), there doesn't seem to be a p-value
provided for the whole model, although LL, AIC and BIC statistics are
provided. Is it possible to obtain a p-value for the whole
2007 Apr 16
2
Plotting data with a fitted curve
Suppose you have a vector of data in x and response values in y. How
do you plot together both the points (x,y) and the curve that results
from the fitted model, if the model is not y ~ x, but a higher order
polynomial, e.g. y~poly(x,2)? (In other words, abline doesn't work
for this case.)
Thanks,
--Paul
--
Paul Lynch
Aquilent, Inc.
National Library of Medicine (Contractor)
2017 Jun 26
3
Jagged ROC curves?
Hi,
I was trying to draw some ROC curves (prediction of case/control status),
but seem to be getting a somewhat jagged plot. Can I do something that
would 'smooth' it somewhat? Most roc curves seem to have many incremental
changes (in x and y directions), but my plot only has 4 or 5 steps even
though there are 22 data points. Should I be doing something differently?
How can I provide a
2005 Dec 09
1
R-help: gls with correlation=corARMA
Dear Madams/Sirs,
Hello. I am using the gls function to specify an arma correlation during
estimation in my model. The parameter values which I am sending the
corARMA function are from a previous fit using arima. I have had some
success with the method, however in other cases I get the following error
from gls: "All parameters must be less than 1 in absolute value". None
of
2012 May 02
3
Consulta gráfica
Hola,
Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?
http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5
Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.
Muchas gracias.
Eva
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2010 Aug 23
1
Fitting a regression model with with ARMA error
Hi,
I want to fit a regression model with one independent variable. The error
part should be fitted an ARMA process.
For example,
y_t = a + b*x_t + e_t where e_t is modelled as an ARMA process.
Please let me know how do I do this in R. What code should I use?
TIA
Aditya
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2004 Mar 09
2
corARMA and ACF in nlme
Hi R-sters,
Just wondering what I might be doing wrong. I'm trying to fit a multiple
linear regression model, and being ever mindful about the possibilities of
autocorrelation in the errors (it's a time series), the errors appear to
follow an AR1 process (ar(ts(glsfit$residuals)) selected order 1). So,
when I go back and try to do the simultaneous regression and error fit with
gls,
2012 Mar 19
1
fitting a histogram to a Gaussian curve
Hello,
I am trying to fit my histogram to a smooth Gaussian curve(the data
closely resembles one except a few bars).
This is my code :
#!/usr/bin/Rscript
out_file = "irc_20M_opencl_test.png"
png(out_file)
scan("my.csv") -> myvals
hist(myvals, breaks = 50, main = "My Distribution",xlab = "My Values")
pdens <- density(myvals, na.rm=T)
plot(pdens,
2017 Jun 26
0
Jagged ROC curves?
> On Jun 26, 2017, at 11:40 AM, Brian Smith <bsmith030465 at gmail.com> wrote:
>
> Hi,
>
> I was trying to draw some ROC curves (prediction of case/control status),
> but seem to be getting a somewhat jagged plot. Can I do something that
> would 'smooth' it somewhat? Most roc curves seem to have many incremental
> changes (in x and y directions), but my plot
2006 Dec 06
1
Questions about regression with time-series
Hi,
I am using 2 times series and I want to carry out a regression of Seri1
by Serie2 using structured (autocorrelated) errors.
(Equivalent to the autoreg function in SAS)
I found the function gls (package nlme) and I made:
gls_mens<-gls(mening_s_des~dataATB, correlation = corAR1())
My problem is that I don’t want a AR(1) structure but ARMA(n,p) but the
execution fails :
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
2008 May 02
1
Errors using nlme's gls with autocorrelation
Hi,
I am trying out a generalized least squares method of forecasting that
corrects for autocorrelation. I downloaded daily stock data from Yahoo
Finance, and am trying to predict Close (n=7903). I have learned to use
date functions to extract indicator variables for Monday - Friday (and
Friday is missing in the model to prevent it from becoming full rank). When
I run the following code...
2009 Oct 27
3
how do I plot a regression curve with the data?
I have a data set of 6 or so ordered pairs, and I've been able to graph
them and have decided to use a high-order polynomial regression. I've
used the following piece of code:
regression <- function(x,y) {
x <- c(insert_numbers_here)
y <- c(insert_other_numbers_here)
fit <- lm(y ~ x + I(x^2) + I(x^3) + I(x^4) + I(x^5) + I(x^6) +
I(x^7) + I(x^8) + I(x^9))
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.
2006 Nov 06
1
question about function "gls" in library "nlme"
Hi:
The gls function I used in my code is the following
fm<-gls(y~x,correlation=corARMA(p=2) )
My question is how to extact the AR(2) parameters from "fm".
The object "fm" is the following. How can I extract the correlation parameters
Phi1 and Phi2 from "fm"? These two parametrs is not in the "coef" componenet of "fm".
Thanks a
2006 Aug 09
1
Joint confidence intervals for GLS models?
Dear All,
I would like to be able to estimate confidence intervals for a linear
combination of coefficients for a GLS model. I am familiar with John
Foxton's helpful paper on Time Series Regression and Generalised Least
Squares (GLS) and have learnt a bit about the gls function.
I have downloaded the gmodels package so I can use the estimable
function. The estimable function is very
2008 Jul 17
1
smooth.spline
I like what smooth.spline does but I am unclear on the output. I can see from the documentation that there are fit.coef but I am unclear what those coeficients are applied to.With spline I understand the "noraml" coefficients applied to a cubic polynomial. But these coefficients I am not sure how to interpret. If I had a description of the algorithm maybe I could figure it out but as it
2010 Nov 01
6
connecting points into a smooth curve
If I have, say, five scatter points and want to connect them together into a
smooth curve.
I did plot(x,y,type="l"), but the graph is five segments connecting with
each other, but not a smooth curve.
I wonder if there is a line type that is a curve. Thanks!
--
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2004 Jun 21
1
Novice question: Smooth interpolation of survival curve
Greetings,
How might one go about generating a smooth interpolation of a survival
curve generated by the survfit function in the survival package? I am
able to package my variables by standard methods
>x<-(survfit(Surv(time),data)
and plot the "step" curve, but would like to obtain a smooth estimation
curve for the purpose of approximating survival time between steps on
the