From c657e1d4ff6271571956f60a1496ce0a8362cfe5 Mon Sep 17 00:00:00 2001 From: timb Date: Mon, 4 Mar 2024 13:04:52 +0000 Subject: [PATCH] Updated README --- DESCRIPTION | 2 +- README.Rmd | 10 +--- README.md | 65 ++++++++++------------- man/figures/README-plot_hypothesis-1.png | Bin 13924 -> 13914 bytes man/figures/README-plot_opamod1-1.png | Bin 37167 -> 37183 bytes man/figures/README-plot_opamod2-1.png | Bin 41710 -> 42097 bytes 6 files changed, 31 insertions(+), 46 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index c335bcd..baebce5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: opa Type: Package Title: An Implementation of Ordinal Pattern Analysis -Version: 0.8.2.026 +Version: 0.8.2.027 Authors@R: person("Timothy", "Beechey", email = "tim.beechey@proton.me", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8858-946X")) Description: Quantifies hypothesis to data fit for repeated measures diff --git a/README.Rmd b/README.Rmd index bbedf8e..68b5c35 100644 --- a/README.Rmd +++ b/README.Rmd @@ -12,7 +12,7 @@ knitr::opts_chunk$set( ) # set plot colors -palette(c("#0072B2", "#999999", "#E69F00", "#56B4E9")) +palette(c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF")) ``` # opa @@ -90,12 +90,6 @@ Individual-level model output can be plotted using: plot(opamod) ``` -To aid interpretation, individual PCCs and c-values can also be plotted relative to user-specified thresholds: - -```{r threshold_plots, fig.width=9, fig.height=4.5, fig.align="center"} -plot(opamod, pcc_threshold = 90, cval_threshold = 0.1) -``` - ### Pairwise comparison of measurement conditions Pairwise comparisons of measurement conditions can be calculated by applying the `compare_conditions()` function to an `opafit` object produced by a call to `opa()`: @@ -111,7 +105,7 @@ print(condition_comparisons) If the data consist of multiple groups a categorical grouping variable can be passed with the `group` keyword to produce results for each group within the data, in addition to individual results. ```{r group_data} -dat$group <- rep(c("A", "B", "C", "D"), 5) +dat$group <- rep(c("A", "B", "C", "D"), each = 5) dat$group <- factor(dat$group, levels = c("A", "B", "C", "D")) opamod2 <- opa(dat[, 1:4], h, group = dat$group) diff --git a/README.md b/README.md index 7c07543..6105aa9 100644 --- a/README.md +++ b/README.md @@ -148,15 +148,6 @@ plot(opamod) -To aid interpretation, individual PCCs and c-values can also be plotted -relative to user-specified thresholds: - -``` r -plot(opamod, pcc_threshold = 90, cval_threshold = 0.1) -``` - - - ### Pairwise comparison of measurement conditions Pairwise comparisons of measurement conditions can be calculated by @@ -189,7 +180,7 @@ can be passed with the `group` keyword to produce results for each group within the data, in addition to individual results. ``` r -dat$group <- rep(c("A", "B", "C", "D"), 5) +dat$group <- rep(c("A", "B", "C", "D"), each = 5) dat$group <- factor(dat$group, levels = c("A", "B", "C", "D")) opamod2 <- opa(dat[, 1:4], h, group = dat$group) @@ -202,33 +193,33 @@ summary(opamod2, digits = 3) #> Ordinal Pattern Analysis of 4 observations for 20 individuals in 4 groups #> #> Between subjects results: -#> PCC cval -#> A 100.000 <0.001 -#> B 86.667 <0.001 -#> C 93.333 <0.001 -#> D 93.333 <0.001 +#> PCC cval +#> A 96.667 <0.001 +#> B 93.333 <0.001 +#> C 86.667 0.002 +#> D 96.667 <0.001 #> #> Within subjects results: #> Individual PCC cval #> A 1 100.000 0.034 -#> A.1 5 100.000 0.035 -#> A.2 9 100.000 0.045 -#> A.3 13 100.000 0.044 -#> A.4 17 100.000 0.047 -#> B 2 100.000 0.053 -#> B.1 6 83.333 0.191 -#> B.2 10 83.333 0.165 -#> B.3 14 83.333 0.166 -#> B.4 18 83.333 0.159 -#> C 3 83.333 0.185 -#> C.1 7 100.000 0.044 -#> C.2 11 100.000 0.043 -#> C.3 15 83.333 0.158 -#> C.4 19 100.000 0.05 -#> D 4 100.000 0.055 -#> D.1 8 100.000 0.047 -#> D.2 12 66.667 0.379 -#> D.3 16 100.000 0.05 +#> A.1 2 100.000 0.035 +#> A.2 3 83.333 0.178 +#> A.3 4 100.000 0.044 +#> A.4 5 100.000 0.047 +#> B 6 83.333 0.18 +#> B.1 7 100.000 0.037 +#> B.2 8 100.000 0.048 +#> B.3 9 100.000 0.043 +#> B.4 10 83.333 0.158 +#> C 11 100.000 0.049 +#> C.1 12 66.667 0.392 +#> C.2 13 100.000 0.043 +#> C.3 14 83.333 0.158 +#> C.4 15 83.333 0.156 +#> D 16 100.000 0.055 +#> D.1 17 100.000 0.047 +#> D.2 18 83.333 0.158 +#> D.3 19 100.000 0.05 #> D.4 20 100.000 0.044 #> #> PCCs were calculated for pairwise ordinal relationships using a difference threshold of 0. @@ -261,10 +252,10 @@ summary(group_comp) #> ********* Group Comparison ********** #> Group 1: A #> Group 2: B -#> Group 1 PCC: 100 -#> Group 2 PCC: 86.66667 -#> PCC difference: 13.33333 -#> cval: 0.43 +#> Group 1 PCC: 96.66667 +#> Group 2 PCC: 93.33333 +#> PCC difference: 3.333333 +#> cval: 0.776 #> Comparison type: two-tailed ``` diff --git a/man/figures/README-plot_hypothesis-1.png b/man/figures/README-plot_hypothesis-1.png index 3e3e6706bdd7decf1e960ebc089c81fb06c7bd39..5a6a4cc3820a51fab267b5bb6463783c0c986453 100644 GIT binary patch literal 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