similar to: Generalized Additive Models: How to create publication-ready regression tables

Hi, I have a number of multilevel models and I would like some Latex outputs of them. I usually use the "apsrtable" package, but it does not accept "lme" outputs. Neither does the "mtable" function in the "memisc" package. Is there any good alternative that I am missing? Thanks, Jonas

Hello Everyone (Quick) question: Does anyone know a package/command or simply a way of creating a table of different panel data estimations (estimated using /*plm()*/ ) just as *mtable()* does for models estimated with /*lm()*/? It seems *mtable* (and *apsrtable* equally) only support /*lm*/ and some other classes but unfortunately not /*plm*/. I am pretty sure others must have encountered this

Hello, I've got a question concerning the usage of robust standard errors in regression using lm() and exporting the summaries to LaTeX using the memisc-packages function mtable(): Is there any possibility to use robust errors which are obtained by vcovHC() when generating the LateX-output by mtable()? I tried to manipulate the lm-object by appending the "new" covariance

Hi all, I have used the apsrtable package to generate tables (using LaTeX and Sweave) for quite some. However, suddenly the notes option in the package appears to have stopped working (or I am doing something wrong). So, when I try to run the following commands (in the R console): > attach(NatExp.df) > model1.lm <- lm((1-schoolspend) ~ jadif) > model2.lm <- lm((1-nohousdisc) ~

Hi, I want to export the result of glht in R into a LaTeX table, such as that result: Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Group1 - Group2 == 0 -0.14007 0.01589 -8.813 <0.001 "***" Group1 - Group3 == 0 -0.09396 0.01575 -5.965 <0.001 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05

Is it in bad form to double post to StackOverflow and R-help? Apologies if so. Here's my task: I've got a matrix of means like so means<-matrix(1:10,nrow=2) colnames(means)<-c("a","b","c","d","e") and a matrix of standard deviations like so sds<-matrix(seq(0.1,1,by=0.1),nrow=2)

Greetings R world, I know some version of the this question has been asked before, but i need to save the output of a loop into a data frame to eventually be written to a postgres data base with dbWriteTable. Some background. I have developed classifications models to help identify problem accounts. The logic is this, if the model classifies the record as including variable X and it turns out

Hi, I am fairly new to GAM and started using package mgcv. I like the fact that optimal smoothing is automatically used (i.e. df are not determined a priori but calculated by the gam procedure). But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result

Hello all, I wish to extract the terms from an rpart object. Specifically, I would like to be able to know what is the response variable (so I could do some manipulation on it). But in general, such a method for rpart will also need to handle a "." case (see fit2) Here are two simple examples: fit1 <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) fit1$call fit2 <-

Greetings All, I am looking to write a parametrized Rscript that will accept a variable name(that also is the name of the flat file), transform the data into a data frame and preform various modeling on the structure and save the output and plot of the model. In this example i am using a rpart decision tree. The only problem i am having is integrating the parameter into the internal object name

Dear All, What is the preferred way to get Bayesian analysis results (such as those from MCMCpacki, MCMCglmm, and DPpackage) into LaTeX table automatically? I have been using the "apsrtable" package and similar functions in "memisc" package, but neither seems to handle MCMC output directly. Many thanks. Shige

Philipp Benner reported a Debian bug report against r-cran-rpart aka rpart. In short, the issue has to do with how rpart evaluates a formula and supporting arguments, in particular 'weights'. A simple contrived example is ----------------------------------------------------------------------------- library(rpart) ## using data from help(rpart), set up simple example myformula <-

I am using the rpart package and seem to have trouble with data sets that have columns with no data. I look at the column data in R and all values are NA. When this occurs, I get nothing back from the rpart function. Is there a way to get the rpart package to ignore these columns, without knowing what columns are empty? I have tried the na.action=na.omit and na.action=na.exclude, but neither one

Hi R-helpers. I have the following problem: I would like to apply my function gain(df,X,A) to a list of arguments. df is a data frame X,A are the varibales od data frame. When I do > gain(kyphosis,"Kyphosis",c("Start","Number")) [1] "Start" "Number" I get the following error... Error in unique.default(x) : unique() applies only to vectors I

I am working with a non-parametic smoothing operation using a Generalized Additive Model. It is a bivariate data set. I know how to do the smooth, and out comes a nice smooth curve. Now I want to find the value of the smoothed curve for several values of x (the abscissa). This can be done (please correct me if I am wrong) by using the predict.gam function. You feed the predict.gam function a

Hi, Suppose i have generated an object using the following : fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) And when i print fit, i get the following : n= 81 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 81 17 absent (0.7901235 0.2098765) 2) Start>=8.5 62 6 absent (0.9032258 0.0967742) 4) Start>=14.5 29 0 absent (1.0000000

I run the following code: library(rpart) data(kyphosis) fit <- rpart(Kyphosis ~ ., data=kyphosis) plot(fit) text(fit, use.n=TRUE) The text labels represent the count of each class at the leaf node. Unfortunately, the numbers are rounded and in scientific notation rather than the exact number of examples sorted by that node in each class. The plot is supposed to look like

Dear R-Users, I want to use mtable from package "memisc" to produce Latex-style estimation output. However, mtable() only gives me a p-value and not the corresponding test-statistic. Does anyone know how to extract it, either from a glm/anova object or mtable? Here is a short example: # Run this #################### install.packages("memisc") library(memisc) set.seed(1)

Dear R-users I am using RPART package to get regression trees. However having trouble getting the text function to put the full splitting rule number on the plot, instead to puts it in scientific notation. When a covariate has 1e4 or greater number of digits then the splitting rule number displayed on the plot is in scientific notation. But print.rpart displays the splitting rules in full.

Hello, Due to exploration of the JIT capabilities offered through the {compiler} package, I came by the fact that using enableJIT(2) can *slow* the rpart function (from the {rpart} package) by a magnitude of about 10 times. Here is an example code to run: library(rpart) require(compiler) enableJIT(0) # just making sure that JIT is off # We could also use enableJIT(1) and it would be fine fo