search for: class5

Hi, Please advice on what type of graph can be used to display the following data set. I have the following: Name Class a Class 1 a Class4 b Class2 b Class1 d Class3 d Class5 e Class4 e Class2 So each entry in name can belong to more than one class. I want to represent the data as to see where overlaps occur that is which names are in the same Class Name and also which names are unique to a Class. I tough a Venn Diagram would work but this can o...

...which this was taken, tablegen complains that the Name field of myImm6's ParserMatchClass is not a string. The reason is revealed by looking at the raw output from tablegen: ------------- Defs ----------------- def myImm5Class { // AsmOperandClass myAsmOperandClass string Name = "Class5"; } def myImm5 { // Operand AsmOperandClass ParserMatchClass = myImm5Class; } def anonymous_0 { // AsmOperandClass myAsmOperandClass string Name = !strconcat("Class", !cast<string>(myImmClass:n)); } def myImm6 { // Operand myImmClass AsmOperandClass ParserMatc...

...me")) setMethod("name", "Class1", function(object) object@name) setClass("Class2", representation("Class1")) setClass("Class3", representation("Class2")) setClass("Class4", representation("Class3")) setClass("Class5", representation("Class4")) getClass("Class3") # as I expected Class3Def <- getClassDef("Class3", where = topenv(parent.frame())) ## the following is called in setValidity() via resetClass() ## (further explanations, see below) completeClassDefinition("...

...ase can you help me? Class Levels: 1 2 3 4 5 coxph(formula = Surv(delai.etat, etat) ~ class, data = igr1) coef exp(coef) se(coef) z p class2 -0.093 0.911 0.245 -0.38 7.0e-01 class3 -0.749 0.473 0.286 -2.62 8.9e-03 class4 -0.600 0.549 0.343 -1.75 8.0e-02 class5 -0.874 0.417 0.197 -4.44 8.9e-06 Likelihood ratio test=24.9 on 4 df, p=5.28e-05 n=740 (1 observations deleted due to missing) Thanks for your help Bertrand

...data by states PUFnum <- subset(PUF, state==num) #Attach data set with state specific data attach(PUFnum) #Run our prediction regression #the variables class1 through e19700 are the 27 covariates I want to use regression <- lapply(PUFnum, function(z) lm(z ~ class1+class2+class3+class4+class5+class6+class7+xtot+e00200+e00300+e00600+e00900+e01000+p04470+e04800+e09600+e07180+e07220+e07260+e06500+e10300+e59720+e11900+e18425+e18450+e18500+e19700)) Beta <- lapply(regression, function(d) d<- coef(regression$d)) detach(PUFnum) } Thanks, Mike [[alternative HTML version deleted]]

Anyone have any experience using gr303? May have a need to interface * to a Siemens Class-5 CO for pstn trunking (inbound and outbound). Rich

Hey All, I'm just daydreaming here.. but what's the status of SIP-T in Asterisk? I haven't been able to find a whole lot of info on SIP-T but seems like just an extension of SIP. Right? Now if I had a PSTN Gateway (that is a SS7 gateway) that supported SIP-T, could I signal * with SIP-T from it and have asterisk utilize MGCP to sieze a particular DS0 on a remote DS1? Hmm.. What am

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >