similar to: -log10 of 0

Hi all, why factorial(150) shows the error out of range in 'gammafn'? I have to calculate the number of subset formed by 150 samples taking 10 at a time. How is this possible? best [[alternative HTML version deleted]]

Hi all, in my search for a nice binary2decimal method, I received this nice code (thanx to Uwe Ligges): bindec <- function(b) sum(as.integer(unlist(strsplit(b, ""))) * 2^(floor(log10(b)):0)) It fails, however, with: > bindec(1000) [1] 4 Warning message: longer object length is not a multiple of shorter object length in: as.integer(unlist(strsplit(b, "")))

Dear all, I would like to (i) produce boxplot graphs with axis in logarithm in base 10 and (ii) showing the values on the axis in 10^exponent format rather than 10E+exponent. To illustrate with an example, I have some widely spread data that I chart plot using boxplot() [figure on the left]; the log="y" option of boxplot() I obtained the natural logarithm conversion of the data and

Is there a way to teach xy.coords, when given log="xy", or just "x" or "y" separately, to do a decimal log10 instead of the natural log? Cheers, Alexy

x <- rnorm(1e6); y <- log(x); # or logb(x) or log1p(x) w <- warnings(); print(object.size(w)); ## [1] 480 str(w); $ NaNs produced: language log(x) - attr(*, "dots")= list() - attr(*, "class")= chr "warnings" y <- log2(x); # or log10(x) w <- warnings(); print(object.size(w)); ## [1] 8000536 str(w); ## List of 1 ## $ NaNs produced: language

Dear all, I did a non-linear least square model fit y ~ a * x^b (a) > nls(y ~ a * x^b, start=list(a=1,b=1)) to obtain the coefficients a & b. I did the same with the linearized formula, including a linear model log(y) ~ log(a) + b * log(x) (b) > nls(log10(y) ~ log10(a) + b*log10(x), start=list(a=1,b=1)) (c) > lm(log10(y) ~ log10(x)) I expected coefficient b to be identical

Dear help-list, I would like to perform a linear regression on the log10 of the two vectors ov.mag.min and res.600nm. The slope and intercept of the regression I use to plot a wider range of ov.mag.min in a double log plot. For a linear regression on only tow points, wouldn't I expect the results for two.points.min to match pretty exactly res.600nm? It does not seem to be the case here.

Hi, I have a histogram with values logged to the base 10 hist(log10(x),breaks=60) How do I show the log values on the x-axis and a second x-axis showing the real values? Thanks -- Shane [[alternative HTML version deleted]]

Dear R experts Sorry for this question M1 <- 1:10 lcd1 <- c(11, 22, 33, 44, 11, 22, 33, 33, 22, 11) lcd2 <- c(22, 11, 44, 11, 33, 11, 22, 22, 11, 22) lcd3 <- c(12, 12, 34, 14, 13, 12, 23, 23, 12, 12) #generating variables through sampling pvec <- c("PR1", "PR2", "PR3", "PR4", "PR5", "PR6", "PR7",

Hello List does anyone have an R function for the Lambert W function? I need complex arguments. [the Lamert W function W(z) satisfies W(z)*exp(W(z)) = z but I could'nt even figure out how to use uniroot() for complex z] -- Robin Hankin Uncertainty Analyst Southampton Oceanography Centre SO14 3ZH tel +44(0)23-8059-7743 initialDOTsurname at soc.soton.ac.uk (edit in obvious way; spam

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 digits If all values are small, mx is very small, log10(mx) is

Hi all, I want to label an axis with exponents, but can't get it done with expression. Any hints would be very welcome! # simulated data, somewhat similarly distributed to my real data: set.seed(12); d <- rbeta(1e6, 0.2,2)*150 ; d <- d[d>1e-8] hist( d? , breaks=100) # now on a logarithmically scaled axis: hist(log10(d), breaks=100, xaxt="n") abline(v=

Does mFUN() really need to be 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"),

Hello. My name is Akashah. i work at metabolic laboratory. From my study, i found that volcano plot can help a lot in my section.  i already studied about the volcano plot and get the coding to run in R software, unfortunately, there is may be something wrong with the coding. This is because  no graph appear, but no error (blue color text) was shown on the R console. Below is the coding for

>>>>> Steve Martin >>>>> on Mon, 18 Dec 2023 07:56:46 -0500 writes: > Does mFUN() really need to be 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)

HELLO, anybody... could you help me to check the below coding for volcano. what is the mistake? what the plot could not display? # volcano_plot.r # # Author: Amsha Nahid, Jairus Bowne, Gerard Murray # Purpose: Produces a volcano plot # # Input: Data matrix as specified in Data-matrix-format.pdf # Output: Plots log2(fold change) vs log10(t-test P-value) # #

Hi all, Suppose I want to set the values in a column to the log of the values of another column like so: object$LogDist <- log10(object$Distance) How do I loop through the objects if I have object1, object2, etc to perform this function? object1$LogDist <- log10(object1$Distance) object2$LogDist <- log10(object2$Distance) object3$LogDist <- log10(object3$Distance) I was trying to

Hi all, I would like to test the homoegeneity of variances between several linear model for some analysis of covariance. It seems that the Bartlett's test is a good test to use but I am having problem using with linear model and I cannot find any examples on the internet. There are some examples for comparisons of variances but not linear models. If I take the hellung data set, which is

Hi, I have some data I want to fit with a non-linear function using nls, but it won't solve. > regresjon<-nls(lcfu~lN0+log10(1-(1-10^(k*t))^m), data=cfu_data, > start=(list(lN0 = 7.6, k = -0.08, m = 2))) Error in nls(lcfu ~ lN0 + log10(1 - (1 - 10^(k * t))^m), data = cfu_data, : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Tried to increase minFactor

Hi, The default value for lag.max in ACF implementation is 10*log10(N) There several publications recommending setting lag.max to: - N/4 (Box and Jenkins, 1970; Chatfield, 1975; Anderson, 1976; Pankratz, 1983; Davis, 1986; etc.) - sqrt(N)+10 (Cryer, 1986) - 20<=N<=40 (Brockwell and Davis) Why R uses 10*log10(N) as a default? Please, give me a reference to a book or article where the