similar to: INSTRUMENTAL VARIABLES WITH BINARY OUTCOMES

Hi everyone, Does anybody knows if there is an equivalent R function that gives the same outcome as in "Explore" function in SPSS ? (Analize->Descriptive Statistics->Explore) It does a categorical vs quantitative variables analysis. ( But not linear regression) I need to compare intragroup (categorical variable with 4 values) means and confidence intervals of a quantitative

I will like to analyse a binary cross over design using the random effects model. The probability of success is assumed to be logistic. Suppose as an example, we have 4 subjects undergoing a crossover design, where the outcome is either success or failure. The first two subjects receive treatment "A" first followed by treatment "B". The remaining two subjects receive

Dear R user, I have been setting up the models for predicting the volume based on the price information of own product and competitive products. one option is to use instrumental variable to break price into two parts: one part that might be correlated with error term, and the another part that is not. But now I met the problem of choosing instumental variables. I have searched many papers. it

Hi there, I hope to build a model Y ~ X1 + X2 + X3 + X4 with X1 has two instrumental variable A and B, and X2 has one instrumental variable A. I have searched the R site and mailling list, and known that the tsls() from sem package and ivreg() from AER package can deal with instrumental variable regression, however, I don't know how to formula the model. Any suggestion will be really

Greetings does anyone know of an R package that can do quantile regression with instrumental variables. I've found 'sem' for IV estimation and 'quantreg' for quantile regression but I would like to find something that can do a quantile regression with instrumental variables. Cheers, Neil ============================================= Neil Hepburn, Economics Instructor

Hi, I am simulating an event that has 15 possible outcomes and I have a vector 'pout' that gives me the probability of each outcome - different outcomes have different probabilities. Does anyone know a simple way of simulating the outcome of my event? If my event had only two possible outcomes (0/1) I would pick a uniform random number between 0 and 1 and use it to choose between the two

Dear all, I would like to implement a spatial quantile regression using instrumental variable estimation (according to Su and Yang (2007), Instrumental variable quantile estimation of spatial autoregressive models, SMU economics & statistis working paper series, 2007, 05-2007, p.35 ). I am applying the hedonic pricing method on land transactions in Luxembourg. My original data set contains

Dear friends, I am trying to understand and implement instrumental variables regression using R. I found a small (simple) example here which purportedly illustrates the mechanics (using 2-stage least-squares): http://www.r-bloggers.com/a-simple-instrumental-variables-problem/ Basically, here are the R commands (reproducible example) from that site: # ------ begin R library(AER)

I have been using generalized linear models in SPSS 18, in order to build models and to calculate the P values. When I was building models in Excel (using the intercept and Bs from SPSS), I noticed that the graphs differed from my expectations. When I ran the dataset again in R, I got totally different outcomes for both the P values as well as the Bs and the intercepts. The outcomes of R seem much

Hi Using R 2.10.1 on a mac os 10.6.2, I have have a problem with gdata package. When I use the command read.xls, I get this error-message: Erreur dans xls2sep(xls, sheet, verbose = verbose, ..., method = method, : Unable to read translated csv file '/var/folders/gb/gbzQ4sqTF-KK3D5m6v-IJE+++TI/-Tmp-//Rtmp3Hprw9/file10d63af1.csv'. Erreur dans file.exists(tfn) : argument 'file'

Dear all, I am using the Instrumental Variable approach to estimate the causal effects of TWO endogenous variables in a Mendelian Randomization study. As long as point estimation is concerned, I have no problem: both "ivreg" in library "AER" and "tsls" in library "sem" do the job perfectly. The problems begin when I try to obtain confidence intervals for

Dear List: I have searched the archives and my R books and cannot find a method to transform a continuous variable into a binary variable. For example, I have test score data along a continuous scale. I want to create a new variable in my dataset that is 1=above a cutpoint (or passed the test) and 0=otherwise. My instinct tells me that this will require a combination of the transform

http://biomet.oxfordjournals.org/cgi/reprint/86/3/677 biometrika1999 http://biomet.oxfordjournals.org/cgi/reprint/94/4/1006 biometrika2000 Hi All: I just want to try some luck. I am currenly working on my project,one part of my project is to reanalysis the kenward cattle data by using the method in Mohsen's paper,but I found I really can get the same or close output as he did,so,any

Hi all, I want to make a histogram like the one show http://nar.oxfordjournals.org/content/39/suppl_1/D1011/F1.expansion.html here , but I did not figure out how to add the red marks at the bottom of the bars. Could anybody help? Thank you very much -- View this message in context: http://r.789695.n4.nabble.com/annotate-histogram-tp3963960p3963960.html Sent from the R help mailing list archive

Hello, In a study exploring transgenerational transmission of anxiety disorder we investigate whether infants react to experimentally induced mood changes of their mothers. We measured the time that an infant needed to cross a cliff (=crossing time) depending on whether his mother had previously undergone a mood induction (treatment) or not (control). The treatment is thus a

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) =

Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic. I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible

Given this example #start code a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) wilcox.test(a, b, paired=FALSE) #sum of rank for first sample sum.rank.a <-

Dear All I recently proposed a simple modification to Wedderburn's 1974 estimate of overdispersion for count and binomial data, which is used in glm for the quasipoisson and quasibinomial families (see the reference below). Although my motivation for the modification arose from considering sparse data, it will be almost identical to Wedderburn's estimate when the data are not sparse.

Hi, Please could somebody has any information about the following package: IlluminaGUI, published here: http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btm101v1 The link given in the article is dead and authors doesn't reply ! Is there someone who uses it ? Thank you very much for help -- View this message in context: