similar to: Calculating signicance value

Hi All, I want to create empty vectors inside an empty data frame.The name of the vectors has to come dynamically. For example if record_mean is my empty data frame,and i have say 4 categories,the category names for record mean data frame has to recmeanC1,recmeanC2,recmeanC3,recmeanC4,which will be dynamically created and which will again be inserted in my data frame's as column values.Each

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

*Problem-1* CASE-I---------(works fine) > var1<-"tom" > var1 [1"tom" > var1<-as.character(var1) > var1 [1] "tom" > var2<-c("tom","harry","kate") > logc<-(var1 %in% var2) > logc [1] TRUE > typeof(var1) [1] "character" > typeof(var2) [1] "character"

R-help, I'm trying to test the significance of two regression lines , i.e. the significance of the slopes from two samples originated from the same population. Is it correct if I fit a liner model for each sample and then test the slope signicance with 'anova'. Something like this: lm1 <- lm(Y~ a1 + b1*X) # sample 1 lm2 <- lm(Y~ a2 + b2*X) # sample 2 anova(lm1, lm2)

two ARIMA models, both have several bars signicant in ACF and PACF plots of their residuals, but when run Ljung Box tests, both don't show any significant correlations... however, one model has p-value that is larger than the other model, based on the p-values, can I say the model with larger p-values should be better than the model with smaller p-values? [[alternative HTML version

This is more of a statistics question with implementation implications. I have used R to calculate logistic regressions for various characterstics. I would now like to verify that the difference between a particular subset is significantly different from the logistic regression of the entire set. Example. I have a logistic regression containing every running back drafted between 1980-2000. I

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

Hi guys, As I mentioned in a previous email I''ve refactored ntp and released a 1.0.0 release candidate. There''s one outstanding "flaw" remaining that''s bothering me and I wanted to solicit opinions on the list. We currently maintain a template per distribution that is close to the stock distribution provided ntp configuration. This leads to massive