similar to: Question On CrossTable function in gmodels package

Good evening R-users! I have the following problem: i want to get a weighted crosstable along with the adjusted standardized residuals test Example: a<-c(1,1,1,1,1,2,2,2,3,3) b<-c(10,10,23,24,33,45,46,70,21,66) c<-c(3,3,2,3,4,1,1,1,3,3) d<-c("a", "b","b","c","a","a","a", "b",

i have a data frame(dat) which has many variables.and i use the following script to get the crosstable. >danx2<-c("x1.1","x1.2","x1.3","x1.4","x1.5","x2","x4","x5","x6","x7","x8.1","x8.2","x8.3","x8.4","x11",

Hi all: My data has 3 variables: age(3levels : <30y=1 30-50y=2, >50y=3) gender(Male=0, Female=1) CD4 cell count(raw lab measurement) y(1:death 0:alive) I perform logistic regression to find out the factors that influence y. result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial) >From the result,I can get OR(Odds Ratio) of gender via exp(Estimate of Female,

Is the code below showing a bug in Crosstable()? My expectation was that the values produced by xtabs were rounded instead of truncated: library(gmodels) abc <- c("a", "a", "b", "b", "c", "c") def <- c("d", "e", "f", "f", "d", "e") wgt <- c(0.8, 0.6, 0.4, 0.5, 1.4, 1.3)

Library gmodels include a function CrossTable that is useful for crosstabulation. In the help, it is indicated that one can call this function as CrossTable(data), were data is a matrix. However, when I try to use this option, it doesn't help. Any idea? Is there a bug? Thanks for your help. Prof. Albert Sorribas Grup de Biomatem?tica i Bioestad?stica Departament de Ci?ncies M?diques B?siques

Dear R Helpers, First, I apologize for asking for help on the first of my topics. I have been looking at the posts and pages for apply, tapply etc, and I know that the solution to this must be ridiculously easy, but I just can't seem to get my brain around it. If I want to produce a set of tables for all the variables in my data, how can I do that without having to type them into the table

Hi, I am playing with clogit(case~spontaneous+induced+strata(stratum),data=infert) from clogit help file. This line works. 1. But, why strata(stratum) doesn't have a coefficient like spontaneous and induced? 2. When I remove strata(stratum) from the command, this function seems to keep running forever. Why? 3. I think the equation for clogit looks like P=1/(1+

Dear R-helpers, I am very new to R and trying to run the conditional logit model using "clogit " command. I have more than 4000 observations in my dataset and try to predict the dependent variable from 14 independent variables. My command is as follows clmtest1 <- clogit(Pin~Income+Bus+Pop+Urbpro+Health+Student+Grad+NE+NW+NCC+SCC+CH+SE+MRD+strata(IDD),data=clmdata) However, it

Dear List, With the R 2.12.0 addition of table methods for points(), dotchart() struggles with tables. I found several possible solutions, but it is beyond my skill to decide what is "best". Here is a small example: ############################################# x <- table(infert$education) y <- 1:3L dotchart(x) # error about incorrect plot type ## moving closer to the cause,

Full_Name: John Hendrickx Version: 1.6.0 OS: Windows 98 Submission from: (NULL) (137.224.174.216) CrossTable in the "gregmisc" package fails when the fisher.exact test produces an error (I suspect this is because the number of cases is too large). This can be fixed using "FTt <- try(fisher.test(t, alternative = "two.sided"))" or by making the test optional.

I am attempting to produce crosstabulations between two variables for subgroups defined by a third factor variable. I'm using by() and CrossTable() in package gmodels. I get the printing of the tables first and then a printing of each level of the INDICES. For example: library(gmodels) by(warpbreaks, warpbreaks$tension, function(x){CrossTable(x$wool, x$breaks > 30,

Hi, I am using the CrossTable function from library(gmodels). x=unique(data[,c("L1","L1_Revenues","RIC")]) L1_Classification=CrossTable(x$L1,x$L1_Revenues,exclude = c("NA","","0%","0"),prop.r=FALSE,prop.c=FALSE,prop.t=FALSE,prop.chisq=FA LSE,dnn=c("L1_Classification","Revenue")) What I would like

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--begin inclusion -- I have a matched-case control dataset that I'm using conditional logistic regression (clogit in survival) to analyze. I'm trying to conduct k-folds cross validation on my top models but all of the packages I can find (CVbinary in DAAG, KVX) won't work with clogit models. Is there any easy way to do this in R? -end inclusion -- The clogit funciton is simply a

i use the CrossTable (frome gregmic package) function to get such a table as below. but the percentage of the non-NA levels(here 1,2,3,4,5) is not totally 100%. is there any way to get a table that percentage of the non-NA levelsis totally 100%,as the SPSS' valid percentage.thank you! Cell Contents |-------------------------| | Count | | Row Percent |

Hi, I have a dataframe with multiple (appr. 20) columns containing vectors of different values (different distributions). Now I'd like to create a crosstable where I compare the distribution of each vector (df-column) with each other. For the comparison I want to use the ks.test(). The result should contain as row and column names the column names of the input dataframe and the cells should

Hi All, I am using "glm" function to build logistic regression. I noticed that glm function glm function is computing many other statistics which are not required for our analysis. As our dataset is very big and we have to run logistic regression on several samples the run time drastically increases if all those statistics are computed. Is these any way to skip computation in glm

I have survey data that I am working on. I need to make some multi-way tables and regression analyses on the data. After attaching the data, this is the code I use for tables for four variables (sweight is the weight variable): > a <- xtabs(sweight~research.area + gender + a2n2 + age) > tmp <- ftable(a) Is this correct? I don't think I need to use the strata and cluster

I receive the following meteo dataset regularly, containing the average daily temperatures (tMedia) of a certain month for 24 selected meteo-stations (COD_WMO) whose human-readable names are in (NOME). str(tabella) `data.frame': 1038 obs. of 4 variables: $ COD_WMO: int 16045 16045 16045 16045 16045 16045 16045 16045 16045 16045 ... $ NOME : Factor w/ 24 levels

I would like to know how R computes the probability of an event in a logistic model (P(y=1)) from the score s, linear combination of x and beta. I noticed that there are differences (small, less than e-16) between the fitting values automatically computed in the glm procedure by R, and the values "manually" computed by me applying the reverse formula p=e^s/(1+e^s); moreover I noticed