similar to: logistic regression in R - changing defaults

I have been trying to run a logistic regression using a number of studies. Below is the syntax, error message & data. Any advice regarding what I am doing wrong or solutions are appreciated, regards Bob Green > logreg <- read.csv("c:\\logregtest.csv",header=T) > attach(logreg) > names(logreg) [1] "medyear" "where" "who"

I am hoping for some advie regarding the following scenario. I have data from 26 studies that I wanted to analyse using logistic regression. Given the data was from studies and not individuals I was unsure how I would perform this in R. When analysed in SPSS, weighting was used so that each study was included twice. Where "use" occurred, a value of 1 was assigned and was weighted

A basic question, but one that eludes me. I have created a new variable $numurder, which I have rounded off. I want to save the rounded off version of this variable to an existing datafile called 'ngri.csv' . numurder <-c((murder*no.of.cases)/100) [[1]] [1] 48.952 112.073 182.160 974.610 122.140 663.432 150.856 18.988 137.925 198.045 68.930 203.148 30.056 100.955

I am looking into using the multinomial logistic regression option in the nnet library and have two questions about formatting the data. 1. Can data be analysed in the following format or does it need to be transformed into count data, such as the housing data in MASS? Id Crime paranoia hallucinate toc disorg crimhist age 1 2 1 0 1 0 1 25 2 2 0 1 1 1 1 37 3 1 1 0 1 1 0 42 4 3 0

Hello, Below is the code for a basic bar graph. I was seeking advice regarding the following: (a) For each time period there are values from 16 people. How I can change the colour value so that each person has a different colour, which recurs across each of the three graphs/tie epriods? (b) I have seen much more sophisticated examples using lattice (e.g each person has a separate

I was hoping for some advice regarding possible explanations for the fitted probability values I obtained for a multinomial logistic regression. The analysis aims to predict whether Capgras delusions (present/absent) are associated with group (ABH, SV, homicide; values = 1,2,3,), controlling for previous violence. What has me puzzled is that for each combination the fitted probabilities are

Hello everyone, I have a variable with several categories and I want to convert this into dummy variables and do logistic regression on it. I used model.matrix to create dummy variables but it always picked the smallest one as the reference. For example, model.matrix(~.,data=as.data.frame(letters[1:5])) will code 'a' as '0 0 0 0'. But I want to code another category as

Dear All, I am trying to reproduce the example that I found online here http://bit.ly/11VG4ha However, when I run my script (pasted at the end of the email), I notice that there is a factor 2 between the values for the coefficients for the categorical variable female calculated by my script and in the online example. Any idea about where this difference comes from? Besides, how can I

Hello useRs, I have a problem at hand which I'd think is fairly common amongst groups were R is being adopted for Analytics in place of SAS. Users would like to obtain results for logistic regression in R that they have become accustomed to in SAS. Towards this end, I was able to propose the Design package in R which contains many functions to extract the various metrics that SAS reports.

?s 23:06 de 25/07/2023, Bob Green escreveu: > Hello, > > I am seeking advice as to how I can download the 833 files from this > site:"http://home.brisnet.org.au/~bgreen/Data/" > > I want to be able to download them to perform a textual analysis. > > If the 833 files, which are in a Directory with two subfolders were on > my computer I could read them

An earlier post had posed the question: "Does anybody know what is relation between 'T' value calculated by 'wilcox_test' function (coin package) and more common 'W' value?" I found the question interesting and ran the commands in R and SPSS. The W reported by R did not seem to correspond to either Mann-Whitney U, Wilcoxon W or the Z which I have more

I am hoping for some advice regarding ordering a dataframe, by date. The dataframe is in the format below. $story $datepub story10 1 April 1999 story 90 1 March 2002 story 37 10 July 1985 I want to reorder the entire dataframe so the earliest story is first, and save the reordered dataframe. The command, 'class' (datepub) reveals $datepub is a factor variable. I tried

I am hoping for some advice as to how to modify the following syntax, so that instead of saving all records which refer to Farrah, I select all instances that do not include Farrah, or the word Coolum. test <- read.csv("c:\\newdat.csv", as.is=TRUE, header=T) sure <- test[grep('Farrah', paste(test$V3.HD, test$V3.LP, test$V3.TD)),]

Hello, I am hoping for some advice as to how I can prorate a number of scale items that comprise a score. At least 69 of 159 cases have at least 1 value missing (65 cases have H7 missing). The maximum number of missing is 5. I want to compute a total score, a score for the H items, the R items and C items. H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, C1, C2, C3, C4, C5, R1, R2, R3, R4, R5 I am

I have to create a plot with a print density of 600 dpi . Within R, is there a way to determine the print density of a plot? Any assistance is much appreciated, regards Bob

hello, I am hoping for some advice regarding comparing variables from 3 versions of a spreadsheet which have been combined into a single dataframe. The aim is to identify which rows have been changed. The dataframe contains 177 rows of data (each cell contains text). 'intersect' produced a file with 35 rows, 'union' a file with 303 rows and 'setdiff' a file with 130

Dear list, I'm having second thoughts after solving a very trivial problem: I want to extend the relevel() function to reorder an arbitrary number of levels of a factor in one go. I could not find a trivial way of using the code obtained by getS3method("relevel","factor"). Instead, I thought of solving the problem in a recursive manner (possibly after reading

Can anyone help me with the necessary code to relevel a numeric*factor interaction term in a linear model? I would like to report the estimate, std. error and t-value for the reference factor. First, I estimated a linear model with dummy variables and was able to retrieve model estimates for the reference factor using relevel. for example: > summary(update(mod.mod, . ~ . - dummy + +

I have a frustrating issue which I am hoping someone may have a suggestion about. I am running XP and R 2.12.0 and saved an EXCEL file that I was sent as a csv file. The initial code I ran follows. dec <- read.csv("g://FMH/FO30122010.csv",header=T) dec.open <- subset (dec, Status == "Open") table(dec.open$AMHS) I was checking the output and noticed a difference

I am hoping for some advice regarding the difficulties I have been having recoding variables which are contained in a csv file. Table 1 (below) shows there are two types of blanks - as reported in the first two columns. I am using windows XP & the latets version of R. When blanks cells are replaced with a value of n using syntax: > affect [affect==""] <- "n" there