similar to: partial plots for logistic regression using glm

I have dataframe with 17factors variables (for example every factor have 3levels) I have maybe 5000 observation. And i need to do table where is in every raw 1 of possible combination of this factors and the numbur how many time is this combination in my dataset. I wrote one code, but this is very slow and dumb. it looks like this: i<-0 for(i1 in levels(hivdat$pohl)){

Hello R-helpers, I would like to calculate least square means after having built a GLM with quasipoisson errors. In my model the dependent variable is continuous, I have one continuous independent variable and one categorical independent variable (that is the variable for which I would like to calculate the least square means). I've looked around for the command to calculate the least

Dear list, Is there a function (or an equivalent way) in R resembling the lsmeans command in SAS? The objective is to get the (adjusted) means for design models. Thanks in advance. Regards, Washington Santos da Silva.

I have used glm and stepAIC to choose a best model. I can use termplot to assess the contribution of each explanatory variable in the glm. However the final model after running stepAIC includes interaction terms, and when I do termplot I get "Error in `[.data.frame`(mf, , i) : undefined columns selected". I also see the termplot detail saying "Nothing sensible happens for

Hi list! I did the following regression: reg1 <- glm(alti~sp + ovent + vivent + nuage, family=gaussian, data=meteo1) I was interested in knowing the effect of the species (sp) in reg1 and so I used the function «effect»: effect.sp <- effect ("sp", reg1, se=TRUE) with this output: sp AK BW NH OS RT SS 2.730101 2.885363 2.753774 2.750311

Dear all, I am quite new with R and I have a problem with plotting a binomial regression line in a plot. This is what I type in: > model<-glm(Para~Size,binomial) > par(mfrow=c(1,1)) > xv<-seq(3.2,4.5,0.01) > yv<-predict(model,list(area=xv),type="response") > plot(Size,Para) > lines(xv,yv) The error message that I get is: > Error in xy.coords(x, y) :

Dear all, How does the effect() function in the effects package calculate effects and standard errors for glm quasipoisson models? I was using effect() to calculate the impact of increasing x to e + epsilon, and then finding the expected percent change. I thought that this effect (as a percentage) should be exp(beta*epsilon), where beta is the appropriate coefficient from the model, but

Hi all I'm doing some things with a colleague comparing different sorts of models. My colleague has fitted a number of glms in Genstat (which I have never used), while the glm I have been using is only available for R. He has a spreadsheet of fitted means from each of his models obtained from using the Genstat "predict" function. For example, suppose we fit the model of the type

Hi, my data looks this: id forma program kod obor rocnik 1 10001 kombinovan? Matematika M1101 matematika 1 2 10002 prezen?n? Informatika N1801 teoretick? informatika 1 3 10002 prezen?n? Informatika B1801 obecn? informatika 3 4 10003 prezen?n? Informatika M1801 softwarov?

Hi, does anyone has a bar plot function that produces something like this (I hope attachments work) ? If not, I simply want to produce 3d bar plots. Thanks in advance, Jan

Hi, I'd like to execute simple commands and functions in R through a website, is there any service like this somewhere? I only found http://www.osvisions.com/r-online/ but it does not work (last update 2003) and the links to releated websites only give errors (if I calculate 7+3). Thanks for help & hints, Thomas

I'm trying to plot an extrapolated logistic sigmoid curve using glm(..., family=binomial) as follows, but neither the fitted() points or the predict()ed curve are plotting correctly: > year <- c(2003+(6/12), 2004+(2/12), 2004+(10/12), 2005+(4/12)) > percent <- c(0.31, 0.43, 0.47, 0.50) > plot(year, percent, xlim=c(2003, 2007), ylim=c(0, 1)) > lm <- lm(percent ~ year)

Hi All, I try to do a scatterplot for a bunch of variables. I plot a dependent variable against a bunch of independent variables: -- cut -- graphics::plot( v01_r01 ~ v08_01_up11, data = dataset, xlab = "Dependent", ylab = "Independent #1" ) -- cut -- It is tedious to repeat the statement for all independent variables. Found an alternative, i.e. : -- cut -- mu

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Hello! When I am analyzing proportion data, I usually apply logistic regression using a glm model with binomial family. For example: m <- glm( cbind("not realized", "realized") ~ v1 + v2 , family="binomial") However, sometimes I don't have the number of cases (realized, not realized), but only the proportion and thus cannot compute the binomial model. I just

Dear R-list members, I would like to pose a question about the use and results of the glm() function for logistic regression calculations. The question is based on an example provided on p. 229 in P. Dalgaard, Introductory Statistics with R, 2nd. edition, Springer, 2008. By means of this example, I was trying to practice the different ways of entering data in glm(). In his book, Dalgaard

Hi All , Can anybody tell me if there's any way to get the summarized anova values.Now i will explain what i mean , when i say "*summarized*". Below you can see the anova table of recmeanC1 with rest* all* i.e from recmeanC2 to i15(predictors),as shown in table. Df Sum Squares Mean Square F value Significance [Pr(>F)] recmeanC2 1 89.272 89.272

Hi LLVM-Devs, I have managed to complete updating our sources from LLVM v4.0 to v5.0, but I am getting selection errors for 'callseq_end'. I am aware that the 'ADJCALLSTACKUP' and 'ADJCALLSTACKDOWN' patterns have changed, and have added an additional argument to the TD descriptions for these. There are interactions with 'ISD::CALL' and 'ISD::RET_FLAG',

Hi all, hope someone can help me out with this. Background Introduction I have a data set consisting of data collected from a questionnaire that I wish to validate. I have chosen to use confirmatory factor analysis to analyse this data set. Instrument The instrument consists of 11 subscales. There is a total of 68 items in the 11 subscales. Each item is scored on an integer scale between 1 to 4.

Problems with predict and lines in plotting binomial glm Dear R-helpers I have found quite a lot of tips on how to work with glm through this mailing list, but still have a problem that I can't solve. I have got a data set of which the x-variable is count data and the y-variable is proportional data, and I want to know what the relationship between the variables are. The data was