In Zibetti/Plothtests: Plot Parametric Hypothesis Tests (Z, T, Chi-squared and F)

knitr::opts_chunk$set(
  collapse = FALSE,
  comment = "#>"
)

library(Plottests)

"If you think without writing, you only think you're thinking." - Leslie Lamport

Ploting and performing a Hyphotesis test on the difference of two means using summary statistics (t-test)

To perform and plot the hyphotesis on the difference of two means just fill the function parameters with the observed statistics and details about your alternative hyphotesis as well with the significance level (alpha).

• alpha - significance level for the critical limits; it must lie between zero and one
• alternative - a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
• var.equal - a char variable indicating whether to treat the two variances as being equal or notequal. If "equal" then the pooled variance is used to estimate the variance otherwise ("notequal") the Welch (or Satterthwaite) approximation to the degrees of freedom is used
• n1 - a single number representing the sample size of sample 1
• m1 - a single number representing the sample mean of sample 1
• v1 - a single number representing the sample variance for sample 1
• n2 - a single number representing the sample size of sample 2
• m2 - a single number representing the sample mean of sample 2
• v2 - a single number representing the sample variance for sample 2
• delta0 - a number indicating the true value of the difference in means of the two sample test.
• annotations - logical, show annotations in plot figure, TRUE ou FALSE (default = TRUE)
• color - integer, color theme for plot, from 1 to 7 so far (default = 3)

plot_T_test(alpha = 0.05, alternative = "two.sided",
         var.equal = "equal",
         n1 = 10, m1 = 2.92, v1 = 1.5,
         n2 = 20, m2 = 4.20, v2 = 1.7,             
         delta0 = 0,
         annotations = TRUE,
         color = 3)

Ploting and performing a Hyphotesis test on the difference of two variances using summary statistics (F-test)

To perform and plot the hyphotesis on the difference of two variances just fill the function parameters with the observed statistics and details about your alternative hyphotesis as well with the significance level (alpha).

Plot F Test to Compare Two Variance, the larger sample variance is always placed in the numerator.

• alpha - significance level for the critical limits; it must lie between zero and one

• alternative - a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

• df1 - degrees of freedom for the sample 1 (n1-1)

• df2 - degrees of freedom for the sample 2 (n2-1)
• v1 - sample 1 variance
• v2 - sample 2 variance
• ratio - the ratio of population variances under the null hypoyhesis (default = 1)
• m2 - a single number representing the sample mean of sample 2
• annotations - logical, show annotations in plot figure, TRUE ou FALSE (default = TRUE)
• color - integer, color theme for plot, from 1 to 7 so far (default = 3)

plot_F_test(alpha = 0.05, alternative = "two.sided",
             df1 = 10, df2 = 10,
             v1 = 1.5, v2 = 2.5,
             ratio = 1,
             annotations = TRUE,
             color = 3)

Ploting and performing a Hyphotesis test on the difference of two variances using summary statistics (F-test)

To perform and plot the hyphotesis on the difference of two variances just fill the function parameters with the observed statistics and details about your alternative hyphotesis as well with the significance level (alpha).

Plot F Test to Compare Two Variance, the larger sample variance is always placed in the numerator.

• alpha - significance level for the critical limits; it must lie between zero and one

• alternative - a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

• df1 - degrees of freedom for the sample 1 (n1-1)

• df2 - degrees of freedom for the sample 2 (n2-1)
• v1 - sample 1 variance
• v2 - sample 2 variance
• ratio - the ratio of population variances under the null hypoyhesis (default = 1)
• m2 - a single number representing the sample mean of sample 2
• annotations - logical, show annotations in plot figure, TRUE ou FALSE (default = TRUE)
• color - integer, color theme for plot, from 1 to 7 so far (default = 3)

plot_F_test(alpha = 0.05, alternative = "two.sided",
             df1 = 10, df2 = 10,
             v1 = 1.5, v2 = 2.5,
             ratio = 1,
             annotations = TRUE,
             color = 3)

Zibetti/Plothtests documentation built on Feb. 5, 2021, 8:32 p.m.