This instruction forces the t.test() command to assume that the variance of the two samples is equal.

You can use them: alternative=”less” or alternative=”greater”, option to specify one-tailed test. The var.equal argument indicates whether or not to assume equal variances when performing a two-sample t-test. Also, you should be careful with the "table" solution, in case there are no TRUE values in the logical vector. var.equal: A logical variable indicating whether to treat the variances in the samples as equal. Example 1: Compute Variance in R. In the examples of this tutorial, I’m going to use the following numeric vector:

How to Perform T-tests in R? To do so, add the var.equal = TRUE instruction to the standard t.test() command. # Compute t-test res - t.test(weight ~ group, data = my_data, var.equal = TRUE) res Two Sample t-test data: weight by group t = 2.7842, df = 16, p-value = 0.01327 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 4.029759 29.748019 sample estimates: mean in group Man mean in group Woman 68.98889 52.10000 One-Sample T-testing in R. To perform analysis, it collects a large amount of data from various sources and tests it on random samples.

I think that table solution is less efficient (look at the code of table function). The standard t test as the special case for two samples; the Kruskal-Wallis test kruskal.test for a nonparametric test for equal location parameters in a one-way layout. If 'TRUE', a simple F test for the equality of means in a one-way analysis of variance is performed. 1. R - Variables - A variable provides us with named storage that our programs can manipulate. So I think the safest is to use na.rm = TRUE: sum(z, na.rm = TRUE) # best way to count TRUE values (which gives 1).

A variable in R can store an atomic vector, group of atomic vectors or a combination

One-Sample. Definition of var: The var R function computes the sample variance of a numeric input vector. The default assumes unequal variance and applies the Welsh approximation to the degrees of freedom; however, you can set this to TRUE to pool the variance. In the following article, I’ll show in three examples how to use the var function in R. So let’s move on to the examples! We can use the var.equal = TRUE option to specify equal variances and a pooled variance estimate. In R, we use the syntax t.test(y, mu = 0) to conduct one-sample tests in R, where