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Hi R Gurus! I conducted regression and anova followings : Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.07e-01 5.95e-02 10.19 < 2e-16 *** nemp 2.87e-06 1.04e-07 27.63 < 2e-16 *** as.factor(corridor1)A -8.81e-02 2.13e-02 -4.14 3.6e-05 *** as.factor(corridor1)B

Hi I have found a little problem with an R script. I am trying to merge some data and am finding something unusual going on. As shown below I am trying to assign (MatchedValues[Value2,Value]) to (ClusteredData[k,Value]) which are two separate dataframes. 1) By the following command you can see that the value im transferring is 481844.03 > MatchedValues[Value2,Value] [1] 481844.03 6618

Hi I'm having a simple issue with the order function. When I use the following code my data is not ordered correctly > output[order(output[,3]),] Name beta pval 881 9.09303277751237 0.000100253350483199 7402 6.40553461638365 0.00010228641631914 4879 -8.88509881106217 0.000103251645995887 However when I export the data and sort it in excel I

Hi, first I want to thank all of you for the quick aid which is provided here on the list during all times. Thanks a lot for that! Then, I have a problem using summaryBy which most probably is a problem of wrong use by me or the like: I use this command: summaryBy(total+total.inf~gr, aE, FUN=sum) where aE is a > str(aE) 'data.frame': 127880 obs. of 16 variables: $ gr

Howdy I apologize for duplicated posting. But I decided to correct my previous posting. I had the regression results using r <- lm(Y ~ nemp + as.factor(devt), data=d). First, there is the result of anova(r). Here I could not find regression coefficients. Response: Y Df Sum Sq Mean Sq F value Pr(>F) nemp 1 58.2 58.2 1233.23 < 2e-16 ***

I am having trouble understanding results I'm getting back from coxph doing a recurrent event analysis. I've included the model below and the summary. In some cases, with minor variations, the Robust variance and Wald tests are significant, but the individual covariates may or may not be significant. My main question is: If Wald and robust tests both take into account the

Hi, I am having a problem figuring out how to extract a subset of rows. I have a list with 68 similar data.frames. Each data.frame is 500 rows by 5 columns. I want to take one row from each data.frame based upon the data in a particular column (i.e. it matches a symbol). For example: > str(database) List of 68 $ X2011.01.11:'data.frame': 500 obs. of 5 variables: ..$ Symbol

I am getting the following errors when I am trying to plot the data below. I cannot figure out the error. Error in plot.window(...) : need finite 'xlim' values In addition: Warning messages: 1: In min(x) : no non-missing arguments to min; returning Inf 2: In max(x) : no non-missing arguments to max; returning -Inf 3: In min(x) : no non-missing arguments to min; returning Inf 4: In max(x) :

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable

Are the messages below to be expected from make check-all ? using the rc today, Aug 21, on Red Hat Enterprise Linux AS release 4 (Nahant Update 4) Kernel 2.6.9-42.0.8.ELsmp on an x86_64 Paul ________ .... checking package 'utils' .... * checking R code for possible problems ... NOTE install.packages: no visible global function definition for ?.install.winbinary? install.packages: no

Hi, I am having a hard time getting what the summary.lm-output for an ANOVA / ANCOVA means. Examples I find always seem to deal with simpler cases than what I meet in my data. My main problem is understanding the output when getting significant INTERACTION TERMS (what never occurs in examples :(). The following is the output after summary.lm(ancova) where "week" is continuous,

Hi everybody, I'm trying to fit a Garch(1,1) process to the DAX returns. My data consists of about 2300 10day-logreturns in chronologically descending order (see attachment). But if I use the garch function I get a very high alpha_1 and a quite low beta, which doesn't make that much sense. I think I am missing something, but have no idea what it might be. I'd appreciate it a lot

Hi, I am using the package "demography" from Rob Hyndman for the Lee-Carter-Model. It is an amazing powerful tool but I am struggling with one issue: I want to compute different percentiles of the survival probability distribution derived from the Lee-Carter-Forecast (e.g. the 50%tile, 60%tile, 75%tile and 99%tile) for each of the next 10 years. Is there any possibility to retrieve

Dear R users, Hopefully someone can help me, Maybe I just misunderstand the function in the package? I am working with a logistic regression model. Until now I always worked with the basic glm function, where for the model was: ¡§ glm( disease ~ test.value + cnct , family=binomial(link=¡¦logit¡¦) ¡¨. This works fine when test .value and concentration (cnct) are continuous vairables. However,