'SAS' Linear Model
Convert some columns of a data.frame to factors
ANOVA with Type I SS
ANOVA with Type II SS
ANOVA with Type III SS
An example data for meta-analysis - aspirin in coronary heart disease
Internal Functions
An Example Data of Bioequivalence Study
Beautify the output of knitr::kable
Analysis BY variable
Confidence Interval Estimation
Collinearity Diagnostics
F Test with a Set of Contrasts
Correlation test of multiple numeric columns
Correlation test by Fisher's Z transformation
Sum of Square with a Given Contrast Set
Cumulative Alpha for the Fixed Z-value
Coefficient of Variation in percentage
Plot Pairwise Differences
Example Datasets
Drift defined by Lan and DeMets for Group Sequential Design
Get a Contrast Matrix for Type I SS
Get a Contrast Matrix for Type II SS
Get a Contrast Matrix for Type III SS
Expected Mean Square Formula
Estimate Linear Functions
Estimate Linear Function
Estimability Check
Exit Probability with cumulative Z-test in Group Sequential Design
Generalized type 2 inverse matrix, g2 inverse
Generalized inverse matrix of type 2 for linear regression
Geometric Coefficient of Variation in percentage
Geometric Mean without NA
General Linear Model similar to SAS PROC GLM
Is it a correlation matrix?
Kurtosis
Standard Error of Kurtosis
Lower Confidence Limit
Linear Fit
Linear Regression with g2 inverse
Simple Linear Regressions with Each Independent Variable
Least Square Means
Max without NA
Mean without NA
Median without NA
Min without NA
Model Matrix
Independent two groups t-test similar to PROC TTEST with summarized in...
Number of observations
Odds Ratio of two groups
Odds Ratio of two groups with strata by CMH method
Odds Ratio of two groups with strata by inverse variance method
Odds Ratio and Score CI of two groups with strata by MN method
Odds Ratio and Score CI of two groups without strata by the MN method
Plot Confidence and Prediction Bands for Simple Linear Regression
Partial Correlation test of multiple columns
Diagnostic Plot for Regression
Pairwise Difference
Pocock (fixed) Bound for the cumulative Z-test with a final target alp...
Residual Diagnostic Plot for Regression
Inter-Quartile Range
Range
Test with Random Effects
Risk Difference between two groups
Risk Difference between two groups with strata by inverse variance met...
Risk Difference and Score CI between two groups with strata by the MN ...
Risk Difference and Score CI between two groups without strata by the ...
Regression of Linear Least Square, similar to SAS PROC REG
Regression of Conventional Way with Rich Diagnostics
Relative Risk of the two groups
Relative Risk of two groups with strata by inverse variance method
Relative Risk and Score CI of two groups with strata by the MN method
Relative Risk and Score CI of two groups without strata by by MN metho...
'SAS' Linear Model
Satterthwaite Approximation of Variance and Degree of Freedom
Score Confidence Interval for a Proportion or a Binomial Distribution
Standard Deviation
Standard Error of the Sample Mean
Sequential bounds for cumulative Z-test in Group Sequential Design
Confidence interval with the last Z-value for the group sequential des...
Skewness
Standard Error of Skewness
F Test with Slice
Sum of Square
Type III Expected Mean Square Formula
Test Type III SS using error term other than MSE
Independent two means test similar to t.test with summarized input
Trimmed Mean
Table Summary
Table Summary 0 independent(x) variable
Table Summary 1 independent(x) variable
Table Summary 2 independent(x) variables
Table Summary 3 independent(x) variables
Independent two groups t-test comparable to PROC TTEST
Upper Confidence Limit
Univariate Descriptive Statistics
F-Test for the ratio of two groups' variances
White's Model Specification Test
Test for the difference of two groups' means
This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. 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).