How to use the package?
The following code uses the ToothGrowth-data set that is part of the data sets included in R (the current file contains some modifications though for testing the reading and writing routines: read_omv and write_omv). With this data set, a syntax to conduct an ANOVA is run.
The results should be similar to those obtained when running the same analysis in jamovi (using the GUI). To do so, open the file menu (☰) choose Open, Data Library and ToothGrowth. Afterwards, click on the ANOVA-button in the Analyses-tab and choose ANOVA. There, you assign the variable len to Dependent Variable and supp and dose to Fixed Factors. Afterwards, you choose / tick Overall Model Test and ω². Open the drop-down menu Assumption Checks and tick Homogeneity test and Normality test. The results should be identical apart from that the table output looks nicer in jamovi (not only text, as below), numbers are rounded and maybe one or two other cosmetic differences.
If you want to copy the syntax generated in jamovi, you have to switch on the Syntax Mode. Afterwards, the syntax is shown at the top of the analysis and can be copied from there.
library(jmvReadWrite)
library(jmv)
data = read_omv(fleNme = system.file("extdata", "ToothGrowth.omv", package = "jmvReadWrite"))
jmv::ANOVA(
formula = len ~ supp + dose + supp:dose,
data = data,
effectSize = c("omega"),
modelTest = TRUE,
homo = TRUE,
norm = TRUE)
#>
#> ANOVA
#>
#> ANOVA - len
#> ────────────────────────────────────────────────────────────────────────────────────────────────
#> Sum of Squares df Mean Square F p ω²
#> ────────────────────────────────────────────────────────────────────────────────────────────────
#> Overall model 2740.1033 5 548.02067 41.557178 < .0000001
#> supp 205.3500 1 205.35000 15.571979 0.0002312 0.0554519
#> dose 2426.4343 2 1213.21717 91.999965 < .0000001 0.6925788
#> supp:dose 108.3190 2 54.15950 4.106991 0.0218603 0.0236466
#> Residuals 712.1060 54 13.18715
#> ────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> ASSUMPTION CHECKS
#>
#> Homogeneity of Variances Test (Levene's)
#> ────────────────────────────────────────
#> F df1 df2 p
#> ────────────────────────────────────────
#> 1.940130 5 54 0.1027298
#> ────────────────────────────────────────
#>
#>
#> Normality Test (Shapiro-Wilk)
#> ─────────────────────────────
#> Statistic p
#> ─────────────────────────────
#> 0.9849884 0.6694242
#> ─────────────────────────────Since version 0.2.0, read_omv also extracts the syntax from analyses that you may have conducted in the jamovi-GUI and that are stored in the .omv-file. To extract them, you have to set the parameter getSyn = TRUE when calling read_omv (default is FALSE). When the parameter is set, the analyses are stored in the attribute syntax. They can be used as shown in the following examples:
library(jmvReadWrite)
data = read_omv(fleNme = system.file("extdata", "ToothGrowth.omv", package = "jmvReadWrite"), getSyn = TRUE)
# shows the syntax of the analyses from the .omv-file
attr(data, 'syntax')
#> [[1]]
#> [1] "jmv::ANOVA(formula = len ~ supp + dose2 + supp:dose2, data = data, effectSize = \"partEta\", modelTest = TRUE, qq = TRUE, emMeans = ~ dose2:supp)"
#>
#> [[2]]
#> [1] "jmv::ancova(formula = len ~ supp + dose, data = data, effectSize = \"partEta\", modelTest = TRUE)"
# runs the command of the first analysis
eval(parse(text=attr(data, 'syntax')[[1]]))
#>
#> ANOVA
#>
#> ANOVA - len
#> ────────────────────────────────────────────────────────────────────────────────────────────────
#> Sum of Squares df Mean Square F p η²p
#> ────────────────────────────────────────────────────────────────────────────────────────────────
#> Overall model 2740.1033 5 548.02067 41.557178 < .0000001
#> supp 205.3500 1 205.35000 15.571979 0.0002312 0.2238254
#> dose2 2426.4343 2 1213.21717 91.999965 < .0000001 0.7731092
#> supp:dose2 108.3190 2 54.15950 4.106991 0.0218603 0.1320279
#> Residuals 712.1060 54 13.18715
#> ────────────────────────────────────────────────────────────────────────────────────────────────
# runs the command of the second analysis and assigns the output from that analysis to the variable result2
eval(parse(text=paste0('result2 = ', attr(data, 'syntax')[[2]])))
names(result2)
#> [1] "main" "assump" "contrasts" "postHoc" "emm" "residsOV"
# → "main" "assump" "contrasts" "postHoc" "emm" (the names of the five output tables)The jmvReadWrite-package also enables you to write .omv-files in order to use them in jamovi. Let’s assume that you have a large collection of log-files (e.g., from an experiment) that you compile and process (summarize, filter, etc.) in R in order to later analyse them in jamovi. You will have those processed log-files stored in a data frame (called, e.g., data) which you then write to a file that you can open in jamovi afterwards. Although jamovi reads R-data files (.RData, .rda, .rds) write_omv permits to store jamovi-specific attributes (such as variable labels) in addition.