It says the data set stats are used here. Line 8 provides the basic information for a plot.Note that the different options (layers) are connected using the symbol "+". ggplot() can generate all kinds of plot and allow the use of difference options.To generate the plot, the ggplot() function from the ggplot2 package is used. For the summarySE() function, (1) the first input the name of the dataframe to be used, (2) measurevar tells the target variable to be analyzed, and (3) groupvars tells the grouping variable to be used.The function summarySE() from Rmisc is first used to obtain the needed information that includes the mean and se of hvltt2 for each groups the four groups.To generate the plot, two R packages Rmisc and ggplot2 are used.+ labels=c("Memory", "Reasoning", "Speed", "Control")) + geom_errorbar(aes(ymin=hvltt2-se, ymax=hvltt2+se), > ggplot(stats, aes(x=factor(group), y=hvltt2)) + The R code below calculates the information in the table and generates a bar plot as shown in the output. For the ACTIVE data, the information is given in the following table. To generate a such a plot, we need a data frame with the following information: the grouping variable, the mean of each group and the standard error. Such a plot can be generated using the R package ggplot2. In addition, the standard error or the confidence interval for the mean of each group can be added to the bars. The height of each bar is proportional to the mean of each group. With a fairly large sample size, it can be useful to make a barplot to show the group information. > all.pvalue .na p.adjust(.na, method="holm") Specifically for this example, the null and alternative hypotheses As an example, we test whether there is any difference in the four groups in terms of memory performance measured by the Hopkins Verbal Learning Test, denoted by the hvltt2 variable in the data set. In the ACTIVE data, we have 4 groups: one control group and three training groups - memory, reasoning, and speed training groups. In other words, it investigates the effect a grouping variable on the outcome variable. One-way ANOVA typically evaluates whether there is difference in means of three or more groups, although it also works for two-group analysis. Simply speaking, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the t-test to more than two groups. In ANOVA, the observed variance in a particular variable, usually an outcome variable, is partitioned into components attributable to different sources of variation: typically the between-group variation and the within-group variation. Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences between group means, developed by R.A.
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