similar to: Why predicted values are fewer that the real?

i am trying to run stepwise regression for two models(lower,upper), family=gamma and i get the same error despite the models i use. Error in UseMethod("extractAIC") : no applicable method for 'extractAIC' applied to an object of class "formula" In addition: Warning message: In nobs.default(object, use.fallback = TRUE) : no 'nobs' method is available Any

Hello I carried out a logistic regression and found predicted values. Then I want to see both predictors (var1,var2..) and predicted values in same matrix. In other words, I need to know each combinations and predicted values. I used: cbind(var1,var2,var3,var4,predict(glm.obj,type="resp")) I got a somewhat strange result: var1 var2 var3 var4 var5 var6 predicted vals ------

Dear All, I am working with linear mixed-effects models using the lme4 package in R. I created a model with the lmer function including some main effects, a two-way interaction and a random effect. Now I am searching for a way to save the predicted values for this model. As far as I can see, there is no command in lme4 to save the predicted values (like the predict(model) function in e.g.

Hi all, I have some questions about the predicted HR in coxph function including psline covariate. If just fitting covariate as linear form, by default, the reference value for each of predictions type (linear predictor, risk and terms) is the mean covariate within strata. If the psline is specified for the covariate, what's the reference value for the predictions? I did some test code

I have a WLS regression with 1 dependent variable and 3 independent variables. I wish to constrain the predicted values (the fitted values) so that they are greater than zero (i.e. they are positive). I do not know how to impose this constraint in R. Please respond if you have any suggestions. There are some previous postings about constraining the coefficients, but this won't accomplish

Hi, is there a function in R that will give me the variances of a predicted values obtained using predict.lm(). If no function is available I would need to calculate them myself - which involves taking the inverse of X'X (' indicating transpose) where X is my model matrix. I know that calculating an inverse directly is not a good idea in general - could anybody suggest a way around

Question: after fitting a gamma function to some data, how do I get predicted values? I'm a SAS programmer, I new R, and am having problems getting my brain to function with the concept of "object as class ...". The following is specifics of what I am doing: I'm trying to determine the pdf from data I have created in a simulation. I have generated frequency counts

Dear all I generated predicted risk of death for each subject in the study by means of Cox proportional hazards model at 8 year of follow-up, a time point at which follow-up was more than 90% complete. It is possible to extrapolate to 10-year the predicted risk of each subjet by assuming an exponential distribution? Any help would be greatly appreciated. Thanks for your consideration.

Dear lattice wizards, I am trying to figure out how to plot predicted values in xyplot, where the intercept, but not the slope, varies among conditioning factor levels. I am sure it involves the groups, but I have been unsuccessful in my search in Pinhiero and Bate, in the help files, or in the archive, or in my attempts on my own. My example follows: FACT is a factor with levels a,b,c

I tried to use the effect() to get predicted marginals for multinomial logistic as I did for general logistic regression, but failed. Is there anyway to do that? Thx! -- View this message in context: http://www.nabble.com/How-to-get-predicted-marginal-%28aka-predicted-mean%29-after-multinomial-logistic--tp17200114p17200114.html Sent from the R help mailing list archive at Nabble.com.

Hi all- I fit a zero-inflated Poisson model to model bycatch rates using an offset term for effort. I need to apply the fitted model to a datasets of varying levels of effort to predict the associated levels of bycatch. I am seeking assistance as to the correct way to code this. Thanks in advance! Laura [[alternative HTML version deleted]]

Full_Name: Allan McRae Version: 1.6.0 OS: Win 2000 P Submission from: (NULL) (129.215.190.229) When using predict.Arima in library ts(), it appears differencing is only accounted for in the first step of prediction and so any trend is not apparent in the predictions. The example shows the difference between the predictions of an arima(1,1,1) model and the backtransformed predictions of an

Dear R helpers, running the following code of a glm model of the family poisson, gives predicted values < 0. Why? library(MASS) library(stats) library(mvtnorm) library(pscl) data(bioChemists) poisson_glm <- glm(art ~ fem + mar + kid5 + phd + ment, data = bioChemists, family = poisson) predicted.values = predict(poisson_glm) range(predicted.values) Thank you in advance for any hints.

Dear gurus, I've analyzed a (fake) data set ("data") using logistic regression (glm): logreg1 <- glm(z ~ x1 + x2 + y, data=data, family=binomial("logit"), na.action=na.pass) Then, I created a data frame with 2 fixed levels (0 and 1) for each predictor: attach(data) x1<-c(0,1) x2<-c(0,1) y<-c(0,1) newdata1<-data.frame(expand.grid(x1,x2,y))

Simple question. For a simple linear regression, I obtained the "standard error of predicted means", for both a confidence and prediction interval: x<-1:15 y<-x + rnorm(n=15) model<-lm(y~x) predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="confidence")$se.fit 1 2 0.2708064 0.7254615

I've been trying to plot the predicted values, as a line, over the data for a simple nonlinear fit with the following commands: plot( hg ~ ht ) ... define some function hg ~ ht + junk ... ... blah, blah, obtain parameter estimates and predicted values, blah... ... then... lines( sort( $predicted ) ~ sort( ht ) ) which results in a line that isn't smooth (which I knew would happen).

Hello R-helpers, I would like to calculate the standard error for the predicted value from geeglm. As an example, I would like to calculate the GEE mean of treatments and their standard error. I first specified the model as mod <- geeglm(resp ~ trt, data=dat,id=id,family=Gaussian,corstr="ar1",weights=weight) Then I predicted the GEE mean and se using the following code

Is there a way to estimate the standard error for the difference in predicted probabilities obtained from a logistic regression model? For example, this code gives the difference for the predicted probability of when x2==1 vs. when x2==0, holding x1 constant at its mean: y=rbinom(100,1,.4) x1=rnorm(100, 3, 2) x2=rbinom(100, 1, .7) mod=glm(y ~ x1 + x2, family=binomial) pred=predict(mod,

Here is my model with interaction terms and control variables (I changed variables names for easy read): reg1 <- lm(y ~ x1*x2*x3 +control1 + control2 + control3) x1 ranges from 0 to 6; x2 from 0 to 5; and x3 from 0 to 4. All three are discrete ordinal variables; but I will treat them as continuous variables. (a) How can I see the predicted values of y for each of these scenarios (210

Hey, all. I had a quick question about fitting new glm values and then looking at the error around them. I'm working with a glm using a Gamma distribution and a log link with two types of treatments. However, when I then look at the predicted values for each category, I find for the one that is close to 0, the error (using se.fit=T with predicted) actually makes it overlap 0.