similar to: Extracting 'SOME' values from a linear model.

Good day everyone, I fitted a GARCH model to a time series and R estimated the model and provide me with the estimates. However, when I tried to extract the fitted values from the estimated model I got the following error message: "Error in .local(object, ...) : object "fit" not found"   I used the following to extract the fitted values fitted_TASI <- fitted(garchFit(~

Hello I am trying to get out of an lm model the fstatistics, however after I run the model I write > names(Model) and the fstatistic does not appear only these. names(Model) [1] "coefficients" "residuals" "effects" "rank" "fitted.values" [6] "assign" "qr" "df.residual"

Thanks. I wonder if there is a general way of extracting var(u) and var(e), which would be needed to simulate u and e. Clearly, one can get the estimated parameters, but is there a clever way of 'setting up' the matrices?? Best S??ren On 6/19/05, S??ren H??jsgaard <Soren.Hojsgaard at agrsci.dk> wrote: > I would like to draw a sample from a linear mixed model y=Xb+Zu+e which has

Hi folks, I've a basic question concerning missing data. I'm running mixed effects analyses using nlme. I've a sizable chunk of missing data on the outcome being modeled, and am using "na.action=na.omit" when running the models. After fitting the models, I'm then trying to extract and use the fitted values and/or residuals for additional analysis, but keep hitting

Hi, I fitted a linear model: fit <- lm(y ~ a * b + c - 1 , na.action='na.omit') Now I want to extract only the a * b effects with confidence intervals. Of course, I can just add the coefficients by hand, but I think there should an easier way. I tried with predict.lm using the 'terms' argument, but I didn't manage to do it. Any hints are appreciated, best, joerg

Dear all, Hope this question is not too trivial... In order to correct standard errors from an estimation of a fixed effects regression model y need to extract the vector of standard errors of the coefficients of a simple linear model estimated using lm(var1~var2+var3+var4) Unfortunately I can't find out the corresponding function. Thank you very much for your help. Uli -- Uli

Dear all, I have a question about the output of linear mixed model fitted in R using nlme package. In particular, what are the t-values that are given in an output, how are they calculated and based on what test? I guess it cannot be a simple Student t-test, otherwise how can the simple Student t-test test for significance of interactions, right? I cannot find this information in any of R help

Hello: I want to use values from the output of linear models done using permuted data to construct a random distribution. The problem I am having is the extraction of a value, say the p-value or the regression coefficient, from the summary of a linear model. When summarizing a linear model I get this: Call: lm(formula = fitness ~ mm) Residuals: Min 1Q Median 3Q Max

Hi, To obtain estimates for some missing values in my data I fitted a linear regression and then used the command fitted(model) to get the fitted values from the model, but R doesn't return any values for the NA's. I can calculate the fitted values from the estimates obtained from the summary of model, but that's not very handy. Is there a way to include the missing values in the

Hi R-helpers, My real data is a panel (unbalanced and with gaps in years) of thousands of firms, by year and industry, and with financial information (variables X, Y, Z, for example), the number of firms by year and industry is not always equal, the number of years by industry is not always equal. #reproducible example firm1<-sort(rep(1:10,5),decreasing=F) year1<-rep(2000:2004,10)

Dear All,   I have fitted generalized linear models with the glm() function from the package lme4. The statistical values it returns are the z-value and the p-value. Now I am searching for a way to get the F-values for my models  to use for reporting - as to my knowledge reporting z-values is not very useful and the F-values together with the p-values would be desired for the report. I tried the

Hi there, I've got a question which is really trivial for sure but still I have to ask as I'm not making any progress solving it by myself (please be patient with an undergraduate student): I've got a linear model (lm and lmer fitted with method="ML"). Now I want to compare the coefficients (slope, intercept, not the random effects) of both models with a given value (e.g.

Dear R users Is there a way to obtain the residuals from a model fitted by quantile regression? Thank you. Thanaset -- View this message in context: http://r.789695.n4.nabble.com/Quantile-Regression-Extracting-Residuals-tp3225423p3225423.html Sent from the R help mailing list archive at Nabble.com.

Dear R-Users, I have fitted a cross-classified generalized linear mixed model using the lmer package with the following code. Mod<-lmer(y~x+(1|a)+(1|b)+ (1|c), family=binomial) In this case, only including a covariate (x) as a fixed effect. The fitted values, using fitted(mod), correspond to the raw data nicely, and the mean of the fitted values is equal to the mean of the raw data. In

I have applied the same linear model to several different subsets of a dataset. I recently read that in R, code should never be repeated. I feel my code as it currently stands has a lot of repetition, which could be condensed into fewer lines. I will use the mtcars dataset to replicate what I have done. My question is: how can I use fewer lines of code (for example using a for loop, a function or

Dear All, Let have 10 pair of observations, as shown below. ###################### x <- 1:10 y <- c(1,3,2,4,5,10,13,15,19,22) plot(x,y) ###################### Two fitted? models, with ranges of [1,5] and [5,10],?can be easily fitted separately by lm function as shown below: ####################### mod1 <- lm(y[1:5] ~ x[1:5]) mod2 <- lm(y[5:10] ~ x[5:10]) #######################

I'm using R for Mac OS X Aqua GUI version 2.0 When using plot() with a linear model object, the plot of the LSR line does not appear. I get the residual plots, the Normal Q-Q plots and Cook's Differnce plot, just not the first plot with the fitted line. I just get an empty Quartz window, with no graph displayed. I've even tried to use plot.lm() and plotting a glm object, but I get the

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

I am new to mmlcr and am working on a latent class mixture model attempting to identify the trajectory and number of classes that best describes my data. I am able to find model parameters such as degrees of freedom, loglikelihood, and BIC. For example, here is a cubic 3-class model I am using. mmlcr1 <- mmlcr(outer = ~ 1 | ID, components = list(list(formula = Score ~ poly(wave,3), class =

Hello R help I have a dataframe, with 71 samples (rows) and 30 variables. I got linear models for some of the variables,? and I want to join fitted and residuals of these models to the data frame. Sometimes, these vectors have the same length of the dependant variable, but in a few cases, NA values can be found on my data, and therefore, both fitted and residuals have a few rows less than the