diff --git a/DESCRIPTION b/DESCRIPTION index e5040043..32b4ef6c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -29,7 +29,8 @@ Authors@R: c( email = "d.firth@warwick.ac.uk"), person(given = "Daniel", family = "Sabanes Bove", role="ctb", - email = "daniel.sabanesbove@gmail.com"), + email = "daniel@rconis.com", + comment = c(ORCID = "0000-0002-0176-9239")), person(given = "Matt", family = "Kumar", role="ctb"), person(given = "Shuguang", family = "Sun", role="ctb"), person(given = "Gavin", family = "Klorfine", role = "aut") diff --git a/NEWS.md b/NEWS.md index 308b95c4..0550e149 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +## Version 0.9.6 + +* Fixed bug in `CMHtest` which previously suppressed `Df` and `Prob` (p-value) for the overall population results. + ## Version 0.9.5 This is a major release of the package, completing work on a sizeable collection of tidy tools diff --git a/R/CMHtest.R b/R/CMHtest.R index f49dd332..782d3d4c 100644 --- a/R/CMHtest.R +++ b/R/CMHtest.R @@ -486,7 +486,12 @@ CMHtest3 <- function(object, types = c("cor", "rmeans", "cmeans", "general")) { V.list <- lapply(object, function(s) s$V) A.list <- lapply(object, function(s) s$A) nt <- sapply(lapply(object, function(s) s$n), sum) - Df <- object[[1]]$table[, "Df"] + + tab1 <- object[[1]]$table + Df <- stats::setNames( + tab1[, "Df"], + rownames(tab1) + ) if (length(types) == 1 && types == "ALL") { types <- c("general", "rmeans", "cmeans", "cor") diff --git a/docs/reference/logseries.html b/docs/reference/logseries.html index f9174227..49cf04cb 100644 --- a/docs/reference/logseries.html +++ b/docs/reference/logseries.html @@ -1,197 +1,8 @@ - -The Logarithmic Series Distribution — dlogseries • vcdExtra - Skip to contents - - -
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The Logarithmic Series Distribution

- Source: R/logseries.R -
logseries.Rd
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The logarithmic series distribution is a long-tailed distribution introduced -by Fisher etal. (1943) in connection with data on the abundance of -individuals classified by species.

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Usage

- 
dlogseries(x, prob = 0.5, log = FALSE)
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-plogseries(q, prob = 0.5, lower.tail = TRUE, log.p = FALSE)
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-qlogseries(p, prob = 0.5, lower.tail = TRUE, log.p = FALSE, max.value = 10000)
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-rlogseries(n, prob = 0.5)
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Arguments

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x, q
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vector of quantiles representing the number of events.

- - -
prob
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parameter for the distribution, 0 < prob < 1

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log, log.p
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logical; if TRUE, probabilities p are given as log(p)

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lower.tail
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logical; if TRUE (default), probabilities are \(P[X \le x]\), otherwise, \(P[X > x]\).

- - -
p
-

vector of probabilities

- - -
max.value
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maximum value returned by qlogseries

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n
-

number of observations for rlogseries

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Value

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dlogseries gives the density, -plogseries gives the cumulative distribution function, -qlogseries gives the quantile function, and -rlogseries generates random deviates.

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Details

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These functions provide the density, distribution function, quantile -function and random generation for the logarithmic series distribution with -parameter prob.

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The logarithmic series distribution with prob = \(p\) has density -$$ p ( x ) = \alpha p^x / x $$ for \(x = 1, 2, \dots\), -where -\(\alpha= -1 / \log(1 - p)\) and \(0 < p < 1\). -% Note that counts x==2 cannot occur.

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References

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https://en.wikipedia.org/wiki/Logarithmic_distribution

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Fisher, R. A. and Corbet, A. S. and Williams, C. B. (1943). The relation -between the number of species and the number of individuals Journal of -Animal Ecology, 12, 42-58.

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See also

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Distributions

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Author

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Michael Friendly, using original code modified from the -gmlss.dist package by Mikis Stasinopoulos.

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Examples

- 

-XL <-expand.grid(x=1:5, p=c(0.33, 0.66, 0.99))
-lgs.df <- data.frame(XL, prob=dlogseries(XL[,"x"], XL[,"p"]))
-lgs.df$p = factor(lgs.df$p)
-str(lgs.df)
-#> 'data.frame':	15 obs. of  3 variables:
-#>  $ x   : int  1 2 3 4 5 1 2 3 4 5 ...
-#>  $ p   : Factor w/ 3 levels "0.33","0.66",..: 1 1 1 1 1 2 2 2 2 2 ...
-#>  $ prob: num  0.82402 0.13596 0.02991 0.0074 0.00195 ...
-
-require(lattice)
-#> Loading required package: lattice
-#> 
-#> Attaching package: ‘lattice’
-#> The following object is masked from ‘package:seriation’:
-#> 
-#>     panel.lines
-#> The following object is masked from ‘package:gnm’:
-#> 
-#>     barley
-mycol <- palette()[2:4]
-xyplot( prob ~ x, data=lgs.df, groups=p,
-  xlab=list('Number of events (k)', cex=1.25),
-  ylab=list('Probability',  cex=1.25),
-  type='b', pch=15:17, lwd=2, cex=1.25, col=mycol,
-  key = list(
-          title = 'p',
-          points = list(pch=15:17, col=mycol, cex=1.25),
-          lines = list(lwd=2, col=mycol),
-          text = list(levels(lgs.df$p)),
-          x=0.9, y=0.98, corner=c(x=1, y=1)
-          )
-  )
-
-
-
-# random numbers
-hist(rlogseries(200, prob=.4), xlab='x')
-
-hist(rlogseries(200, prob=.8), xlab='x')
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- - - - - - + + + + + + + diff --git a/tests/testthat/test-CMHtest.R b/tests/testthat/test-CMHtest.R index 60a0cecb..82bf5926 100644 --- a/tests/testthat/test-CMHtest.R +++ b/tests/testthat/test-CMHtest.R @@ -168,3 +168,17 @@ test_that("5x3x3 schema, ordinal variables", { expected <- get_sas_results(6L) expect_equal(result, expected, tolerance = 1e-3) }) + +test_that("CMHTest based on xtabs() can give overall df and p-value", { + ord_tab <- xtabs(~ TRTP + SEX + AGEGR1, data = data) + result <- CMHtest(ord_tab, types = "rmeans", overall = TRUE) + expect_true(is.finite(result$ALL$table["rmeans", "Df", drop = TRUE][[1]])) + expect_true(is.finite(result$ALL$table["rmeans", "Prob", drop = TRUE][[1]])) +}) + +test_that("CMHTest can give overall df and p-value with multiple types", { + ord_tab <- xtabs(~ TRTP + SEX + AGEGR1, data = data) + result <- CMHtest(ord_tab, types = c("rmeans", "cmeans"), overall = TRUE) + expect_true(all(is.finite(unlist(result$ALL$table[, "Df", drop = TRUE])))) + expect_true(all(is.finite(unlist(result$ALL$table[, "Prob", drop = TRUE])))) +})