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Author

*The author of this computation has been verified*

R Software Module

Rscript (source code is shown below)

Title produced by software

R console

Date of computation

Thu, 06 Aug 2009 16:17:49 -0600

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Aug/07/t12495970717j57dfsiiljzmx8.htm/, Retrieved Wed, 24 Apr 2024 16:53:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=42528, Retrieved Wed, 24 Apr 2024 16:53:23 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

test

Estimated Impact

326

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F   P   [Univariate Data Series] [Herproducering ti...] [2008-12-03 14:38:00] [6fea0e9a9b3b29a63badf2c274e82506]
- RMP     [Standard Deviation-Mean Plot] [SMP airline data:...] [2008-12-14 21:46:29] [82d201ca7b4e7cd2c6f885d29b5b6937]
- RMPD        [R console] [testing reuse fun...] [2009-08-06 22:17:49] [256f657a32c6b2b7628dddcfa9a822d0] [Current]

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Histogram

Boxplots

> par1 = '12'


> {
+     par1 <- as.numeric(par1)
+     (n <- length(RCx))
+     (np <- floor(n/par1))
+     arr <- array(NA, dim = c(par1, np))
+     j <- 0
+     k .... [TRUNCATED]

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = '12' ;

Parameters (R input):

R code (body of R function):

{
    par1 <- as.numeric(par1)
    (n <- length(x))
    (np <- floor(n/par1))
    arr <- array(NA, dim = c(par1, np))
    j <- 0
    k <- 1
    for (i in 1:(np * par1)) {
        j = j + 1
        arr[j, k] <- x[i]
        if (j == par1) {
            j = 0
            k = k + 1
        }
    }
    arr
    arr.mean <- array(NA, dim = np)
    arr.sd <- array(NA, dim = np)
    arr.range <- array(NA, dim = np)
    for (j in 1:np) {
        arr.mean[j] <- mean(arr[, j], na.rm = TRUE)
        arr.sd[j] <- sd(arr[, j], na.rm = TRUE)
        arr.range[j] <- max(arr[, j], na.rm = TRUE) - min(arr[, 
            j], na.rm = TRUE)
    }
    arr.mean
    arr.sd
    arr.range
    (lm1 <- lm(arr.sd ~ arr.mean))
    (lnlm1 <- lm(log(arr.sd) ~ log(arr.mean)))
    (lm2 <- lm(arr.range ~ arr.mean))
    
    plot(arr.mean, arr.sd, main = "Standard Deviation-Mean Plot", 
        xlab = "mean", ylab = "standard deviation")
    
    
    plot(arr.mean, arr.range, main = "Range-Mean Plot", xlab = "mean", 
        ylab = "range")
    
    source("https://automated.biganalytics.eu/cretabc")
    a <- table.start(a)
    a <- table.row.start(a)
    a <- table.element(a, "Standard Deviation-Mean Plot", 4, 
        TRUE)
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "Section", header = TRUE)
    a <- table.element(a, "Mean", header = TRUE)
    a <- table.element(a, "Standard Deviation", header = TRUE)
    a <- table.element(a, "Range", header = TRUE)
    a <- table.row.end(a)
    for (j in 1:np) {
        a <- table.row.start(a)
        a <- table.element(a, j, header = TRUE)
        a <- table.element(a, arr.mean[j])
        a <- table.element(a, arr.sd[j])
        a <- table.element(a, arr.range[j])
        a <- table.row.end(a)
    }
    a <- table.end(a)
    a <- table.start(a)
    a <- table.row.start(a)
    a <- table.element(a, "Regression: S.E.(k) = alpha + beta * Mean(k)", 
        2, TRUE)
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "alpha", header = TRUE)
    a <- table.element(a, lm1$coefficients[[1]])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "beta", header = TRUE)
    a <- table.element(a, lm1$coefficients[[2]])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "S.D.", header = TRUE)
    a <- table.element(a, summary(lm1)$coefficients[2, 2])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "T-STAT", header = TRUE)
    a <- table.element(a, summary(lm1)$coefficients[2, 3])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "p-value", header = TRUE)
    a <- table.element(a, summary(lm1)$coefficients[2, 4])
    a <- table.row.end(a)
    a <- table.end(a)
    a <- table.start(a)
    a <- table.row.start(a)
    a <- table.element(a, "Regression: ln S.E.(k) = alpha + beta * ln Mean(k)", 
        2, TRUE)
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "alpha", header = TRUE)
    a <- table.element(a, lnlm1$coefficients[[1]])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "beta", header = TRUE)
    a <- table.element(a, lnlm1$coefficients[[2]])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "S.D.", header = TRUE)
    a <- table.element(a, summary(lnlm1)$coefficients[2, 2])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "T-STAT", header = TRUE)
    a <- table.element(a, summary(lnlm1)$coefficients[2, 3])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "p-value", header = TRUE)
    a <- table.element(a, summary(lnlm1)$coefficients[2, 4])
    a <- table.row.end(a)
    a <- table.row.start(a)
    a <- table.element(a, "Lambda", header = TRUE)
    a <- table.element(a, 1 - lnlm1$coefficients[[2]])
    a <- table.row.end(a)
    a <- table.end(a)
}

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Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code