search for: sumsquares

I need to calculate a large number of t statistics, and would like to do so via matrix operations. So far I have figured out a way to calculate the mean of each row of the matrix: d <- matrix(runif(100000,1,10), 1000, 10) # some test data s <- rep(1,ncol(d)) # a sum vector to use for matrix multiplication means <- (d%*%s)/ncol(d) This is at least 1 order of magnitude faster than

...set the "twopass" default to FALSE, because in finance noise is always bigger than signal. I am cc'ing to R-help, as this is really an R question. -- -- David Brahm (brahm at alum.mit.edu) colVars <- function(x, na.rm=FALSE, dims=1, unbiased=TRUE, SumSquares=FALSE, twopass=TRUE) { if (SumSquares) return(colSums(x^2, na.rm, dims)) N <- colSums(!is.na(x), FALSE, dims) Nm1 <- if (unbiased) N-1 else N if (twopass) {x <- if (dims==length(dim(x))) x - mean(x, na.rm=na.rm) else sweep(x, (dims+1):length...

Hi people, If there is a fn "colMeans" why isn't there a "colSDs" etc Thanks, Phil. -- Philip Rhoades Pricom Pty Limited (ACN 003 252 275) GPO Box 3411 Sydney NSW 2001 Australia Mobile: +61:0411-185-652 Fax: +61:2:8923-5363 E-mail: pri at chu.com.au -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi all! I 'm beginner and i develop a bio-application with VB and i need some statistic functions! could i calculate StdError, CoeffOfVariance, SumSquared with R langage? if yes, what are functions to use? I need also to use ANOVA and t-test... Thanks for your help! Laurent Houdusse Analyste Programmeur

I am using the R 2.2.1 in a Windows XP environment. I have a dataframe with 12 columns and 1,000 rows. (Some of the rows have 1 or fewer values.) I am trying to use rowVars to calculate the variance of each row. I am getting the following message: ?Error in na.remove.default(x) : length of 'dimnames' [1] not equal to array extent? Is there a good work-around?

...mymod <- y ~ 1/(Beta1*x1 + Beta2*x2 + Beta3*x3) > library(nlsr) > strt<-c(Beta1=1, Beta2=2, Beta3=3) > trysol<-nlxb(formula=mymod, data=mydata, start=strt, trace=TRUE) > trysol > # or pshort(trysol) > > > Output is > > residual sumsquares =? 1.5412e-05? on? 20 observations > ? ? ?after? 29? ? Jacobian and? 43 function evaluations > ? ?name? ? ? ? ? ? coeff? ? ? ? ? SE? ? ? ?tstat? ? ? pval? ? ? gradient? ? JSingval > Beta1? ? ? ? ?0.00629212? ? ?5.997e-06? ? ? ?1049? 2.425e-42? ?4.049e-08? ? ? ?721.8 >...

...list(theta1 = 0.37, theta2 = exp(-1.8), theta3 = -0.05538), data=mod14data2_random) summary(nlregmod2) Object has try-error or missing parameters nlregmod2 And I get some NA values when retrieving the statistics for the fitted model: residual sumsquares = 0.0022973 on 15 observations after 2235 Jacobian and 2861 function evaluations name coeff SE tstat pval gradient JSingval theta1 9330.89 NA NA NA 5.275e-11 967470 theta2 9330.41 NA...

Is it possible to compute the variance of every column in a matrix without a loop? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch

...x1, x2, x3, y) > mydata > mymod <- y ~ 1/(Beta1*x1 + Beta2*x2 + Beta3*x3) > library(nlsr) > strt<-c(Beta1=1, Beta2=2, Beta3=3) > trysol<-nlxb(formula=mymod, data=mydata, start=strt, trace=TRUE) > trysol > # or pshort(trysol) > > > Output is > > residual sumsquares = 1.5412e-05 on 20 observations > after 29 Jacobian and 43 function evaluations > name coeff SE tstat pval gradient > JSingval > Beta1 0.00629212 5.997e-06 1049 2.425e-42 4.049e-08 > 721.8 > Beta2...

Dear friends, This is the dataset I am currently working with: >dput(mod14data2_random) structure(list(index = c(14L, 27L, 37L, 33L, 34L, 16L, 7L, 1L, 39L, 36L, 40L, 19L, 28L, 38L, 32L), y = c(0.44, 0.4, 0.4, 0.4, 0.4, 0.43, 0.46, 0.49, 0.41, 0.41, 0.38, 0.42, 0.41, 0.4, 0.4 ), x = c(16, 24, 32, 30, 30, 16, 12, 8, 36, 32, 36, 20, 26, 34, 28)), row.names = c(NA, -15L), class =

...theta2 = exp(-1.8), > theta3 = -0.05538), data=mod14data2_random) > summary(nlregmod2) > Object has try-error or missing parameters > nlregmod2 > And I get some NA values when retrieving the statistics for the fitted > model: > residual sumsquares = 0.0022973 on 15 observations > after 2235 Jacobian and 2861 function evaluations > name coeff SE tstat pval gradient > JSingval > theta1 9330.89 NA NA NA 5.275e-11 > 967470 > theta2...

...p(-1.8), >> theta3 = -0.05538), data=mod14data2_random) >> summary(nlregmod2) >> Object has try-error or missing parameters >> nlregmod2 >> And I get some NA values when retrieving the statistics for the fitted >> model: >> residual sumsquares = 0.0022973 on 15 observations >> after 2235 Jacobian and 2861 function evaluations >> name coeff SE tstat pval gradient >> JSingval >> theta1 9330.89 NA NA NA 5.275e-11 >> 9...

...theta3 = -0.05538), data=mod14data2_random) >>> summary(nlregmod2) >>> Object has try-error or missing parameters >>> nlregmod2 >>> And I get some NA values when retrieving the statistics for the fitted >>> model: >>> residual sumsquares = 0.0022973 on 15 observations >>> after 2235 Jacobian and 2861 function evaluations >>> name coeff SE tstat pval gradient >>> JSingval >>> theta1 9330.89 NA NA NA 5.275e-...

Dear friends, Hope you are all doing well and having a great weekend. I have data that was collected on specific gravity and spectrophotometer analysis for 26 mixtures of NG (nitroglycerine), TA (triacetin), and 2 NDPA (2 - nitrodiphenylamine). In the dataset, x1 = %NG, x2 = %TA, and x3 = %2 NDPA. The response variable is the specific gravity, and the rest of the variables are the predictors.

I got starting values as follows: Noting that the minimum data value is .38, I fit the linear model log(y - .37) ~ x to get intercept = -1.8 and slope = -.055. So I used .37, exp(-1.8) and -.055 as the starting values for theta0, theta1, and theta2 in the nonlinear model. This converged without problems. Cheers, Bert On Sun, Aug 20, 2023 at 10:15?AM Paul Bernal <paulbernal07 at

I've seen a number of problems like this over the years. The fact that the singular values of the Jacobian have a ration larger than the usual convergence tolerances can mean the codes stop well before the best fit. That is the "numerical analyst" view. David and Jeff have given geometric and statistical arguments. All views are useful, but it takes some time to sort them all out and

I am not as expert as John, but I thought it worth pointing out that the variable substitution technique gives up one set of constraints for another (b=0 in this case). I also find that plots help me see what is going on, so here is my reproducible example (note inclusion of library calls for completeness). Note that NONE of the optimizers mentioned so far appear to be finding the true best