search for: zapsmall

I was quite suprised to discover that applying `zapsmall` to a scalar value has no apparent effect. For example: > y <- 2.220446e-16 > zapsmall(y,) [1] 2.2204e-16 I was expecting zapsmall(x)` to act like > round(y, digits=getOption('digits')) [1] 0 Looking at the current source code, indicates that `zapsmall` is expecting a vect...

Zapping a vector of small numbers to zero would cause problems when printing the results of summary(). For example, if zapsmall(c(2.220446e-16, ..., 2.220446e-16)) == c(0, ..., 0) then print(summary(2.220446e-16), digits = 7) would print Min. 1st Qu. Median Mean 3rd Qu. Max. 0 0 0 0 0 0 The same problem can also appear when printing the results of summary.g...

I think what's been missed is that zapsmall works relative to the absolute largest value in the vector. Hence if there's only one item in the vector, it is the largest, so its not zapped. The function's raison d'etre isn't to replace absolutely small values, but small values relative to the largest. Hence a vector of similar...

...your thoughts and you change the world. --Dr. Norman Vincent Peale > On Dec 17, 2023, at 12:11?AM, Steve Martin <stevemartin041 at gmail.com> wrote: > > ?Zapping a vector of small numbers to zero would cause problems when > printing the results of summary(). For example, if > zapsmall(c(2.220446e-16, ..., 2.220446e-16)) == c(0, ..., 0) then > print(summary(2.220446e-16), digits = 7) would print > Min. 1st Qu. Median Mean 3rd Qu. Max. > 0 0 0 0 0 0 > > The same problem can also appear when printing t...

Le 17/12/2023 ? 18:26, Barry Rowlingson a ?crit?: > I think what's been missed is that zapsmall works relative to the absolute > largest value in the vector. Hence if there's only one > item in the vector, it is the largest, so its not zapped. The function's > raison d'etre isn't to replace absolutely small values, > but small values relative to the largest. Hence...

>>>>> Serguei Sokol via R-devel >>>>> on Mon, 18 Dec 2023 10:29:02 +0100 writes: > Le 17/12/2023 ? 18:26, Barry Rowlingson a ?crit?: >> I think what's been missed is that zapsmall works relative to the absolute >> largest value in the vector. Hence if there's only one >> item in the vector, it is the largest, so its not zapped. The function's >> raison d'etre isn't to replace absolutely small values, >> but small values...

...e a function of x and the NA values of x? I can't think of a case where it would be used on anything but the non-NA values of x. I think it would be easier to specify a different mFUN() (and document this new argument) if the function has one argument and is applied to the non-NA values of x. zapsmall <- function(x, digits = getOption("digits"), mFUN = function(x) max(abs(x)), min.d = 0L ) { if (length(digits) == 0L) stop("invalid 'digits'") if (all(ina <- is.na(x))) return(x) mx <- mFUN(x[!ina]) round(x, digits = i...

Le 18/12/2023 ? 11:24, Martin Maechler a ?crit?: >>>>>> Serguei Sokol via R-devel >>>>>> on Mon, 18 Dec 2023 10:29:02 +0100 writes: > > Le 17/12/2023 ? 18:26, Barry Rowlingson a ?crit?: > >> I think what's been missed is that zapsmall works relative to the absolute > >> largest value in the vector. Hence if there's only one > >> item in the vector, it is the largest, so its not zapped. The function's > >> raison d'etre isn't to replace absolutely small values, > &...

...an't think of a case where it would be used on anything but the non-NA > values of x. > I think it would be easier to specify a different mFUN() (and document this > new argument) if the function has one argument and is applied to the non-NA > values of x. > zapsmall <- function(x, > digits = getOption("digits"), > mFUN = function(x) max(abs(x)), > min.d = 0L) { > if (length(digits) == 0L) > stop("invalid 'digits'") > if (all(ina <- is.na(x))) >...

Hi If all values are small, zapsmall does not zap. For example ... > zapsmall(1e-24) [1] 1e-24 > zapsmall(1e-24, digits=3) [1] 1e-24 Problem appears to be in calculation of digits ... mx <- max(abs(x[!ina])) digits = if (mx > 0) max(0, digits - log10(mx)) else d...

Full_Name: Paul Vos Version: 2.0.1 OS: windows XP Submission from: (NULL) (150.216.148.20) > zapsmall(.3-.2-.1,digits=7) [1] -2.775558e-17 This should be zero. By changing the condition if (mx > 0) in zapsmall to if (mx > 1) we get > zapsmall(.3-.2-.1,digits=7) [1] 0

...t;0.600200", which are misleading because, if rounded to > the 6th decimal place, the values to be displayed should > be "0.502122" and "0.600162". > Is this behavior of printCoefmat() intended/normal? Yes, this is "normal" in the sense that zapsmall() is used. I'm not even sure anymore if I was always aware 1998 what exactly the simple zapsmall() function is doing. It does not do what you want here (and actually *typically* want for formatting numbers for display, plotting, etc): You "really want" here and in such situations z...

I was trying to do what is on page 70 of John Chambers' new book namely using trace to invoke the browser by doing trace(zapsmall, edit = TRUE) but , typing above at an R prompt, I get trace(zapsmall, edit=TRUE) sh: EMACS: command not found Error in edit(name, file, title, editor) : problem with running editor EMACS I looked in the archives but maybe it's a problem with the keywords I was using. If anyone could t...

...cbind( rbind( cor1, z=c(.5,.3,.1,.05) ), z=c(.5,.3,.1,.05,1) ) # create new matrix m4 <- m3 %*% chol(cor2) # uncenter and unscale m5 <- sweep( m4, 2, attr(m2, 'scaled:scale'), '*') m5 <- sweep( m5, 2, attr(m2, 'scaled:center'), '+') ##Check they are equal zapsmall(cor(m5))==zapsmall(cor2) Thanks, ben

...let's assume I can live with "only approximations". In another thread, Thorsten Hothorn suggested for glm (slightly edited) library(multcomp) set.seed(290875) # a factor at three levels group <- factor(c(rep(1,10), rep(2, 10), rep(3,10))) # Williams contrasts contrasts(group)<-zapsmall(mginv(contrMat(table(group), type="Will"))) # a binary response z <- factor(rbinom(30, 1, 0.5)) # estimate the model gmod <- glm( z ~ group, family=binomial(link = "logit")) summary(gmod) # exclude the intercept # Should be the following, but does not work due to a confir...

Hi, I have a following vector: > smallch [1] 0.0652840 0.1181300 0.0319370 0.0155700 0.0464110 0.0107850 [7] 0.0158970 0.0375900 0.0603090 0.0310300 0.0105920 0.0540580 [13] -0.0177740 0.0039393 Pretty (R 1.5.1) has problems with zero: > pretty(smallch) [1] -2.000000e-02 -3.469447e-18 2.000000e-02 4.000000e-02 6.000000e-02 [6] 8.000000e-02 1.000000e-01 1.200000e-01

...[6] 6.000000e-01 8.000000e-01 Well that may not be a "bug", but it has this unfortunate consequence: R> pretty(c(-.4,.8)) [1] -4.000000e-01 -2.000000e-01 5.551115e-17 2.000000e-01 4.000000e-01 [6] 6.000000e-01 8.000000e-01 And thus my plot axes look funny unless I apply zapsmall: R> plot(c(-.4,.8), c(-.4,.8), axes=F) R> axis(1, at=pretty(c(-.4,.8))) # Yuck! R> axis(2, at=zapsmall(pretty(c(-.4,.8)))) # Better! So here's my simple suggestion: change the last line of pretty() from: seq(z$l, z$u, length = z$n + 1) to: zapsmall(seq(z$l, z$u...

Hi, I have a following vector: > smallch [1] 0.0652840 0.1181300 0.0319370 0.0155700 0.0464110 0.0107850 [7] 0.0158970 0.0375900 0.0603090 0.0310300 0.0105920 0.0540580 [13] -0.0177740 0.0039393 Pretty (R 1.5.1) has problems with zero: > pretty(smallch) [1] -2.000000e-02 -3.469447e-18 2.000000e-02 4.000000e-02 6.000000e-02 [6] 8.000000e-02 1.000000e-01 1.200000e-01

Dear all, I have a problem with interactions in lmer. I have 2 factors (garden and gebiet) which interact, plus one other variable (home), dataframe arr. When I put: / lmer (biomass ~ home + garden:gebiet + ( 1|Block), data = arr)/ it writes: /Error in lme4::lFormula(formula = biomass ~ home + garden:gebiet + (1 | : rank of X = 28 < ncol(X) = 30/ In the lmer help I found out that if not

Hi All, I would like to ask two questions about printCoefmat(). First, I found a behavior of printCoefmat() that looks strange to me, but I am not sure whether this is an intended behavior: ``` r set.seed(5689417) n <- 10000 x1 <- rnorm(n) x2 <- rnorm(n) y <- .5 * x1 + .6 * x2 + rnorm(n, -0.0002366, .2) dat <- data.frame(x1, x2, y) out <- lm(y ~ x1 + x2, dat) out_summary <-