similar to: data frame method for as.table()

> On May 23, 2016, at 11:46 AM, Ernest Adrogu? <eac at openmailbox.org> wrote: > > Hello, > > Currently it's possible to convert an object of class table to a data frame > with as.data.frame.table(), but there's no ready-made function, AFAIK, to do > the reverse operation, i.e. conversion of a data frame to a table. > > Do you think it would be a good

Suppose I have a table constructed from structable or simply just an object of class table. How can I convert it to a latex object? I looked in RSiteSearch, but only found info about matrices or data frames. Steve For example, here is a table t2 > str(t2) table [1:2, 1:2, 1:2] 6 8 594 592 57 ... - attr(*, "dimnames")=List of 3 ..$ Hospital : chr [1:2] "A"

Dear all, I haven't received any feedback so far on my proposal to make "justify" argument available in stats:::format.ftable Is this list the appropriate place for this kind of proposal? I hope this follow-up to my message won't be taken as rude. Of course it's not meant to be, but I'm not used to the R mailing lists... Thank you in advance for your comments, Best,

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

I am quite new to R, so please bear over with me if I have problems with the R terminology. I want to (try to) use R for some analyses within vegetation ecology, using the vegan package. I have my data in a postgresql database, and I manage to get them into R as a dataframe with columns for respectively: Name of the analysed m2, Name of the species, coverage of species in the square in %. I

I apologize in advance if this is the wrong forum for this report/request, and for the fact that I have not read the code for ftable.formula in any detail. >From reading the documentation for ftable.formula, I expected that the following two calls to ftable would produce the same results: data(UCBAdmissions) ftable(UCBAdmissions, row.vars = "Dept", col.vars = c("Gender",

Hi all, I want to know how to perform the test Breslow-Day test for homogeneity of odds ratios (OR) stratified for svytable. This test is obtained with the following code: epi.2by2 (dat = daty, method = "case.control" conf.level = 0.95, units = 100, homogeneity = "breslow.day", verbose = TRUE) where "daty" is the object type table svytable consider it, but

Hi all, I know this issue has been discussed a few times in the past already, but Martin Maechler suggested in a bug report [1] that I raise it here. Basically, there is no (easy) way of printing NAs for all variables when calling xtabs() on factors. Passing 'exclude=NULL, na.action=na.pass' works for character vectors, but not for factors. > test <-

I'm looking for an R method to produce latex versions of tables for table/array objects of 3 or more dimensions, which, of necessity is flattened to a 2D display, for example with ftable(), or vcd::structable, as shown below. I'd be happy to settle for a flexible solution for the 3D case. > UCB <- aperm(UCBAdmissions, c(2, 1, 3)) > ftable(UCB) Dept A B

Hi I haven't found a way to produce a tabulation from factor data with NA values using xtabs. Please find a minimal example below, it's also on R-pubs [1]. Tested with R 3.1.2 and R-devel r67720. It doesn't seem to be documented explicitly that it's not supported. From reading the code [2] it looks like the relevant call to table() doesn't set the "useNA"

Hi everyone, I am trying to build a table putting standard errors horizontally. I haven't been able to do it. library(memisc) berkeley <- aggregate(Table(Admit,Freq)~.,data=UCBAdmissions) berk0 <- glm(cbind(Admitted,Rejected)~1,data=berkeley,family="binomial") berk1 <- glm(cbind(Admitted,Rejected)~Gender,data=berkeley,family="binomial") berk2 <-

Hi all, I have one array of predictors, one observation per row, and one array of responses, also arranged one observation per row. I arrange these into a data.frame and call lm() with a pasted-together formula. I would like to call lm() with a number of responses in excess of 100, but for some reason, 39 seems to be a limit. Why do I get an "invalid variable names" error from

The quirk as in table(1:3, exclude = 1, useNA = "ifany") is actually somewhat documented, and still in R devel r71104. In R help on 'table', in "Details" section: It is best to supply factors rather than rely on coercion. In particular, ?exclude? will be used in coercion to a factor, and so values (not levels) which appear in ?exclude? before coercion will be mapped to

>>>>> Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Mon, 15 Aug 2016 11:07:43 +0200 writes: >>>>> Suharto Anggono Suharto Anggono <suharto_anggono at yahoo.com> >>>>> on Sun, 14 Aug 2016 03:42:08 +0000 writes: >> useNA <- if (missing(useNA) && !missing(exclude) && !(NA %in%

Hi everyone, I am trying to build a table putting standard errors horizontally. I haven't been able to do it. library(memisc) berkeley <- aggregate(Table(Admit,Freq)~.,data=UCBAdmissions) berk0 <- glm(cbind(Admitted,Rejected)~1,data=berkeley,family="binomial") berk1 <- glm(cbind(Admitted,Rejected)~Gender,data=berkeley,family="binomial") berk2 <-

Hi, I have written a package that contains many s4 generic functions and associated methods. I am having a lot of trouble getting R to build the help pages for these generic functions without reporting, "missing link(s): ~~fun~~, which means that it cannot find the appropriate function when code in the example section of the help is run. Right? After some playing around I can get it to

I'm having a problem reading data to set control totals for a dataframe. I want to adjust a dataframe based on a 2-d table of values, which I get by using : > CurrentX1Sums <- as.matrix(xtabs(~tripid_nu+lineon, data=SurveyData)) > CurrentX2Sums <- apply(CurrentX1Sums, 1, sum) I've created a .csv file with new (target) sums that looks like this: tripid_nu Warner

> On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > Hi Bogdan, > Kinda messy, but: > > N <- data.frame(N=c("n1","n2","n3","n4")) > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > C <-

Hi all, Could somebody be so kind to explain to me what is the saturated model on which deviance and degrees of freedom are calculated when fitting a binomial glm? Everything makes sense if I fit the model using as response a vector of proportions or a two-column matrix. But when the response is a factor and counts are specified via the "weights" argument, I am kind of lost as far as

Hello I try to find probability values of some predictor combinations using logistic reg. in response level. Firstly I found coefficients by glm function. Then I followed two ways to get probability values: 1- probility <- exp(X0+bX1+cX2+...)/((1+exp(X0+bX1+cX2+...)) 2- probility <- predict(glm.obj,type="resp") Should have these two given same result ? if so, I did not have. Why