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sasLM (version 0.5.2)

'SAS' Linear Model

Description

This is a core implementation of 'SAS' procedures for linear models - GLM, REG, and ANOVA. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).

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Version

Install

install.packages('sasLM')

Monthly Downloads

729

Version

0.5.2

License

GPL-3

Maintainer

Kyun-Seop Bae

Last Published

April 14th, 2021

Functions in sasLM (0.5.2)

Expected Mean Square Formula

Collinearity Diagnostics

Analysis of Variance similar to SAS PROC ANOVA

Coefficient of Variation in percentage

Analysis BY variable

Standard Error of Kurtosis

Number of observations

Standard Deviation

Generalized inverse matrix of type 2, g2 inverse

Pairwise Difference by Least Significant Difference

Partial Correlation test of multiple columns

Lower Confidence Limit

Test Type III SS using error term other than MSE

Type III Expected Mean Square Formula

General Linear Model similar to SAS PROC GLM

Standard Error of the Sample Mean

ANOVA with Type I SS

Upper Confidence Limit

Table Summary 3 independent(x) variables

Convert some columns of a data.frame to factors

Standard Error of Skewness

Is it a corrleation matrix?

Table Summary 1 independent(x) variable

Regression of Linear Least Square, similar to SAS PROC REG

Sum of Square with a Given Contrast Set

Inter-Quartile Range

Least Square Means

ANOVA with Type II SS

Linear Regression with g2 inverse

Get a Contrast Matrix for Type II SS

Simple Linear Regressions with Each Independent Variable

ANOVA with Type III SS

Mean without NA

Beautify the output of knitr::kable

Diagnostic Plot for Regression

Satterthwaite Approximation of Pooled Variance and Degree of Freedom

Plot Confidence and Prediction Bands for Simple Linear Regression

Table Summary 2 independent(x) variables

Regression of Conventional Way with Rich Diagnostics

Get a Contrast Matrix for Type III SS

'SAS' Linear Model

Estimate Linear Contrast

Get a Contrast Matrix for Type I SS

Estimability Check

Table Summary 0 independent(x) variable

An Example Data of Bioequivalence Study

Correlation test of multiple numeric columns

Confidence Interval Estimation

Internal Functions