Displaying 20 results from an estimated 10000 matches similar to: "prediction intervals for (mcgv) gam objects"
2008 Jun 11
1
mgcv::gam error message for predict.gam
Sometimes, for specific models, I get this error from predict.gam in library
mgcv:
Error in complete.cases(object) : negative length vectors are not allowed
Here's an example:
model.calibrate <-
gam(meansalesw ~ s(tscore,bs="cs",k=4),
data=toplot,
weights=weight,
gam.method="perf.magic")
> test <- predict(model.calibrate,newdata)
Error in
2011 Apr 19
1
Prediction interval with GAM?
Hello,
Is it possible to estimate prediction interval using GAM? I looked through
?gam, ?predict.gam etc and the mgcv.pdf Simon Wood. I found it can
calculate confidence interval but not clear if I can get it to calculate
prediction interval. I read "Inference for GAMs is difficult and somewhat
contentious." in Kuhnert and Venable An Introduction to R, and wondering why
and if that
2013 Jul 08
1
error in "predict.gam" used with "bam"
Hello everyone.
I am doing a logistic gam (package mgcv) on a pretty large dataframe
(130.000 cases with 100 variables).
Because of that, the gam is fitted on a random subset of 10000. Now when I
want to predict the values for the rest of the data, I get the following
error:
> gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1,
+
2005 Mar 24
1
Prediction using GAM
Recently I was using GAM and couldn't help noticing
the following incoherence in prediction:
> data(gam.data)
> data(gam.newdata)
> gam.object <- gam(y ~ s(x,6) + z, data=gam.data)
> predict(gam.object)[1]
1
0.8017407
>
predict(gam.object,data.frame(x=gam.data$x[1],z=gam.data$z[1]))
1
0.1668452
I would expect that using two types of predict
arguments
2012 Oct 10
2
GAM without intercept
Hi everybody,
I am trying to fit a GAM model without intercept using library mgcv.
However, the result has nothing to do with the observed data. In fact
the predicted points are far from the predicted points obtained from the
model with intercept. For example:
#First I generate some simulated data:
library(mgcv)
x<-seq(0,10,length=100)
y<-x^2+rnorm(100)
#then I fit a gam model with
2007 Aug 08
1
prediction using gam
I am fitting a two dimensional smoother in gam, say junk =
gam(y~s(x1,x2)), to a response variable y that is always positive and
pretty well behaved, both x1 and x2 are contained within [0,1].
I then create a new dataset for prediction with values of (x1,x2) within
the range of the original data.
predict(junk,newdata,type="response")
My predicted values are a bit strange
2011 Mar 28
2
mgcv gam predict problem
Hello
I'm using function gam from package mgcv to fit splines. ?When I try
to make a prediction slightly beyond the original 'x' range, I get
this error:
> A = runif(50,1,149)
> B = sqrt(A) + rnorm(50)
> range(A)
[1] 3.289136 145.342961
>
>
> fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE)
> predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE)
Error
2008 Apr 09
1
mgcv::predict.gam lpmatrix for prediction outside of R
This is in regards to the suggested use of type="lpmatrix" in the
documentation for mgcv::predict.gam. Could one not get the same result more
simply by using type="terms" and interpolating each term directly? What is
the advantage of the lpmatrix approach for prediction outside R? Thanks.
--
View this message in context:
2007 Feb 13
1
Missing variable in new dataframe for prediction
Hi,
I'm using a loop to evaluate several models by taking adjacent variables from my dataframe.
When i try to get predictions for new values, i get an error message about a missing variable in my new dataframe.
Below is an example adapted from ?gam in mgcv package
library(mgcv)
set.seed(0)
n<-400
sig<-2
x0 <- runif(n, 0, 1)
x1 <- runif(n, 0, 1)
x2 <- runif(n, 0, 1)
x3 <-
2008 Apr 06
0
mgcv::gam prediction using lpmatrix
The documentation for predict.gam in library mgcv gives an example of using
an "lpmatrix" to do approximate prediction via interpolation. However, the
code is specific to the example wrt the number of smooth terms, df's for
each,etc. (which is entirely appropriate for an example)
Has anyone generalized this to directly generate code from a gam object (eg
SAS or C code)? I wanted to
2012 Mar 10
0
Help with confidence intervals for gam model using mgcv
Hi,
I would be very grateful for advice on getting confidence
intervals for the ordinary (non smoothed) parameter
estimates from a gam.
Motivation
I am studying hospital outcomes in a large data set. The
outcomes of interest to me are all binary variables. The one
in the example here, Dead30d, is death within 30 days of
admission. Sexf is gender (M or F), Age is age in years at
the start
2005 Feb 27
1
prediction, gam, mgcv
I fitted a GAM model with Poisson distribution
using the function gam() in the mgcv package.
My model is of the form:
mod<-gam(y~s(x0)+s(x1)+s(x2),family=poisson).
To extract estimates at a specified set of covariate
values I used the gam `predict' method.
But I want to get
estimate and standard error of the difference of two fitted values.
Can someone explain what should I do?
Thank
2007 Oct 05
2
question about predict.gam
I'm fitting a Poisson gam model, say
model<-gam(a65tm~as.factor(day.week
)+as.factor(week)+offset(log(pop65))+s(time,k=10,bs="cr",fx=FALSE,by=NA,m=1),sp=c(
0.001),data=dati1,family=poisson)
Currently I've difficulties in obtaining right predictions by using
gam.predict function with MGCV package in R version 2.2.1 (see below my
syntax).
2013 Mar 21
1
[mgcv][gam] Odd error: Error in PredictMat(object$smooth[[k]], data) : , `by' variable must be same dimension as smooth arguments
Dear List,
I'm getting an error in mgcv, and I can't figure out where it comes
from. The setup is the following: I've got a fitted GAM object called
"MI", and a vector of "prediction data" (with default values for
predictors). I feed this into predict.gam(object, newdata = whatever)
via the following function:
makepred = function(varstochange,val){
for
2008 Jul 26
0
gam() of package "mgcv" and anova()
R-users
E-mail: r-help@r-project.org
Hi! R-users.
A simple object as below was created to see how gam() of
package "mgcv" and anova() work.
function()
{
library(mgcv)
set.seed(12)
nd <- 100
xx1 <- runif(nd, min=1, max=10)
xx1 <- sort(xx1)
yy <- sin(xx1)+rnorm(nd, mean=5, sd=5)
data1 <- data.frame(x1=xx1, y=yy)
fit1 <- gam(y~s(x1, k=5),
2006 Feb 05
1
how to extract predicted values from a quantreg fit?
Hi,
I have used package quantreg to estimate a non-linear fit to the
lowest part of my data points. It works great, by the way.
But I'd like to extract the predicted values. The help for
predict.qss1 indicates this:
predict.qss1(object, newdata, ...)
and states that newdata is a data frame describing the observations
at which prediction is to be made.
I used the same technique I used
2008 Nov 14
1
negative prediction by gam (mgcv package)
Hi
Gam in mgcv package is predicting negative values which should not be
the case despite all the predictors and response variables are positive.
Tried to use log link function but it did not help. Please help
sunil
--
View this message in context: http://www.nabble.com/negative-prediction-by-gam-%28mgcv-package%29-tp20494965p20494965.html
Sent from the R help mailing list archive at
2012 Feb 17
1
Standard errors from predict.gam versus predict.lm
I've got a small problem.
I have some observational data (environmental samples: abiotic explanatory variable and biological response) to which I've fitted both a multiple linear regression model and also a gam (mgcv) using smooths for each term. The gam clearly fits far better than the lm model based on AIC (difference in AIC ~ 8), in addition the adjusted R squared for the gam is
2003 Jun 03
3
gam questions
Dear all,
I'm a fairly new R user having two questions regarding gam:
1. The prediction example on p. 38 in the mgcv manual. In order to get
predictions based on the original data set, by leaving out the 'newdata'
argument ("newd" in the example), I get an error message
"Warning message: the condition has length > 1 and only the first element
will be used in: if
2010 Oct 21
1
gam plots and seWithMean
hello
I'm learning mgcv and would like to obtain numerical output corresponding
to plot.gam.
I can do so when seWithMean=FALSE (the default)
but only approximately when seWithMean=TRUE.
Can anyone show how to obtain the exact values?
Alternatively, can you clarify the explanation in the manual
"Note that, if seWithMean=TRUE, the confidence bands include
the uncertainty about the