Colleagues,
g <- function(x) ( x^11 + 1000*x^10 + 500 *x^9 + 1 )
coeffs <- c(1, rep(0, 8), 500, 1000, 1)
roots <- polyroot(coeffs)
Output
[1]0.25770068+3.958197e-01i
[2] -0.34615184+3.782848e-01i
[3] -0.04089779-4.838134e-01i
[4]0.44124314-1.517731e-01i
[5] -0.04089779+4.838134
Colleagues,
f <- function(x) ( x^11 + 1000*x^10 + 500 *x^9 + 1 ) ^0.01
coeffs <- c(1, rep(0, 8), 500, 1000, 1)
roots <- polyroot(coeffs)
# output
[1]0.25770068+3.958197e-01i
[2] -0.34615184+3.782848e-01i
[3] -0.04089779-4.838134e-01i
[4]0.44124314-1.517731e-01i
[5] -0.04089779+
# Get mean, min, max sigma and skew by group
options (digits = 3)
library (ISwR
data(energy)
data %>%
group_by(stature) %>%
summarize(
Mean = mean(expend),
Min = min(expend),
Max = max(expend),
Sigma = sd(expend),
Skew = skew(expend))
# Output
stature Mean Min
Colleagues,
Many of my colleagues come to me for a recommendation for statistical
software.
Since I am an R user, that's my typical answer.
Some colleagues of mine refuse to use it because of its steep learning curve
and lack of a GUI.
They wanted a statistical software that's free and that had a
Colleagues
Given the coefficients of a cubic polynomial, a,b,c,d and
using
discriminant_cubic <- function(a, b, c, d) {
D <- 18 * a * b * c * d - 4 * b^3 * d + b^2 * c^2 - 4 * a * c^2 - 27 * a^2
* d^2
return(D)
}
I can find the discriminant of a cubic polynomial.
Is there an R package which
Colleagues,
The code for prop.trend.test is given by:
function (x, n, score = seq_along(x))
{
method <- "Chi-squared Test for Trend in Proportions"
dname <- paste(deparse1(substitute(x)), "out of",
deparse1(substitute(n)),
",\n using scores:", paste(score, collapse = " "))
x
Colleagues,
I am trying to write a script for the Mantel Haenszel test.
For the MH test, the test statistic is chi-square (MH) = (W-1) * r^2
Where W = sum of the case weights. This is straight forward.
I'm having difficulty with r^2. The r^2 is the squared Pearson correlation
between row and co
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