variance

来源： https://stackoverflow.com/questions/63698650/iterating-through-a-generator-of-itertools-combinations-object-takes-forever

来源： https://stackoverflow.com/questions/63698650/iterating-through-a-generator-of-itertools-combinations-object-takes-forever

问题 I've followed the advice laid out here for calculating the average of circular data: https://en.wikipedia.org/wiki/Mean_of_circular_quantities But I'd also like to calculate standard deviation as well. #A vector of directional data (separated by 20 degrees each) Dir2<-c(350,20,40) #Degrees to Radians D2R<-0.0174532925 #Radians to Degrees Rad2<-Dir2 * D2R Sin2<-sin(Rad2) SinAvg<-mean(Sin2) Cos2<-cos(Rad2) CosAvg<-mean(Cos2) RADAVG<-atan2(SinAvg, CosAvg) DirAvg<-RADAVG * R2D The above gives me

问题 I need more information about variance in generics and delegates. The following code snippet does not compile: Error CS1961 Invalid variance: The type parameter 'TIn' must be covariantly valid on 'Test.F(Func)'. 'TIn' is contravariant. public interface Test<in TIn, out TOut> { TOut F (Func<TIn, TOut> transform); } The .net Func definition is as follows: public delegate TResult Func<in T, out TResult> (T arg); Why the compiler complains about TIn being contravariant and TOut - covariant while

问题 I assume that the random effects variances in my mixed effect model will be different for different levels of the fixed factor BTyp . Here is my model fm2 <- lme(CA ~ 1 + pF+Tiefe+BTyp+Tiefe:pF+BTyp:pF, data=data2, random = list(~ 1 + pF|Probe)) fm2_Btyphet<-update(fm2, weights=varIdent(form=~1|BTyp)) I managed to incorporate Btyp -specific variances for random effects using lmer function, but this function does not allow to consider variance heterogeneity of the within group error (which is

问题 The Exact rules for variance validity are a bit vague and not specific. I'm going to list the rules for what makes a type valid-covariantly, and attach some queries and personal annotations to each of those rules. A type is valid covariantly if it is: 1) a pointer type, or a non-generic type. Pointers and non-generic types are not variant in C#, except for arrays and non-generic delegates. Generic classes, structs and enums are invariant. Am I right here? 2) An array type T[] where T is valid

问题 The Exact rules for variance validity are a bit vague and not specific. I'm going to list the rules for what makes a type valid-covariantly, and attach some queries and personal annotations to each of those rules. A type is valid covariantly if it is: 1) a pointer type, or a non-generic type. Pointers and non-generic types are not variant in C#, except for arrays and non-generic delegates. Generic classes, structs and enums are invariant. Am I right here? 2) An array type T[] where T is valid

问题 How can I calculate the population variance of my data using R? I read there is a package called popvar but I have the Version 0.99.892 and I don't find the package 回答1: The var() function in base R calculate the sample variance, and the population variance differs with the sample variance by a factor of n / n - 1 . So an alternative to calculate population variance will be var(myVector) * (n - 1) / n where n is the length of the vector, here is an example: x <- 1:10 var(x) * 9 /10 [1] 8.25

问题 @Jerry Coffin I get the logic, while(File>>value)//while input just taken from file is true .... do computation. Yet when I implemented this the counter only went to 1 & it's value was very high. Sometime is wrong, but I have no idea what. The file is valid File.open(FileName, ifstream::in); while(File>>value){ ++counter; sum += value; sumsqr+= value * value; } average=sum/counter; variance = sumsqr/counter - average*average; File.close(); here's the contents of the input file I am using

问题 I am using the gls function from the nlme package. You can copy and paste the following code to reproduce my analysis. library(nlme) # Needed for gls function # Read in wide format tlc = read.table("http://www.hsph.harvard.edu/fitzmaur/ala2e/tlc.dat",header=FALSE) names(tlc) = c("id","trt","y0","y1","y4","y6") tlc$trt = factor(tlc$trt, levels=c("P","A"), labels=c("Placebo","Succimer")) # Convert to long format tlc.long = reshape(tlc, idvar="id", varying=c("y0","y1","y4","y6"), v.names="y",