likelihoodR (version 1.1.5)

L_logistic_regress: Likelihood Support for Logistic Regression

Description

This function calculates the supports for multiple logistic regression. A binary dependent variable is entered into the function, followed by up to 6 predictor variables (which need to be dummy coded if nominal and more than 2 levels). Outputs give the overall support for the full model versus the null (constant) model, supports for each of the predictor variables. Outputs include the usual chi-squared and p values.

Usage

L_logistic_regress(yv, p1, p2=NULL, p3=NULL, p4=NULL, p5=NULL, p6=NULL, verb=TRUE)

Value

$S.FNc - support for the full versus null (constant value) model.

$S.each - support for each of the predictors, from first to last.

$chi.sq.FN - chi-squared for full versus null model.

$df - degrees of freedom for chi-squared.

$chi.sq.FN.p - p value for chi-squared.

$p.vals - p values for each of the predictors

Arguments

yv

a binomial numeric vector for dependent variable.

p1

vector for predictor variable, same length as yv.

p2

vector for predictor variable, same length as yv, default = NULL.

p3

vector for predictor variable, same length as yv, default = NULL.

p4

vector for predictor variable, same length as yv, default = NULL.

p5

vector for predictor variable, same length as yv, default = NULL.

p6

vector for predictor variable, same length as yv, default = NULL.

verb

show output, default = TRUE.

References

Cahusac, P.M.B. (2020) Evidence-Based Statistics, Wiley, ISBN : 978-1119549802

1 Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 1974;19(6):716.

Glover S, Dixon P. Likelihood ratios: a simple and flexible statistic for empirical psychologists. Psychonomic Bulletin & Review. 2004;11(5):791.

Tabachnick BG, Fidell LS. Using Multivariate Statistics. Boston: Pearson Education; 2007.

Examples

# prescription errors example, p 114
p_error <- c(rep(1,6),rep(0,9))
score <- c(4,	5,	6,	5,	4,	6,	6,	4,
5,	8,	9,	7,	10,	8,	9)
med1 <- c(1,	1,	0,	0,	1,	1,	0,	0,
0,	0,	0,	0,	0,	0,	1)
med2 <- c(0,	0,	1,	0,	0,	0,	1,	0,
0,	1,	1,	0,	1,	1,	0)
m1 = L_logistic_regress(p_error, score, med1, med2)
m1