knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.path = "README-" ) options(tibble.print_min = 5, tibble.print_max = 5)
When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. If Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.
The saturated model is characterized by the assumptions 1 and 2 presented in section 2.3 by Llinas (2006, ISSN:2389-8976).
[1] Humberto Jesus Llinas. (2006). Accuracies in the theory of the logistic models. Revista Colombiana De Estadistica,29(2), 242-244.
[2] Hosmer, D. (2013). Wiley Series in Probability and Statistics Ser. : Applied Logistic Regression (3). New York: John Wiley & Sons, Incorporated.
Humberto Llinas Solano [aut], Universidad del Norte, Barranquilla-Colombia \ Omar Fabregas Cera [aut], Universidad del Norte, Barranquilla-Colombia \ Jorge Villalba Acevedo [cre, aut], Unicolombo, Cartagena-Colombia.
library(devtools) install_github("jlvia1191/ls")
Hosmer, D. (2013) page 3: Age and coranary Heart Disease (CHD) Status of 20 subjects:
library(lsm2) AGE <- c(20,23,24,25,25,26,26,28,28,29,30,30,30,30,30,30,30,32,33,33) CHD <- c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0) data <- data.frame (CHD, AGE ) lsm2(CHD ~ AGE , data)
# Other case.
y <- c(0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1) x1 <- c(2, 2, 2, 2, 2, 5, 5, 5, 5, 6, 6, 6, 8, 8, 11, 11, 11, 1) x2 <- c(3, 3, 3, 3, 3, 6, 6, 6, 6, 8, 8, 8, 9, 9, 12, 12, 12, 12) x3 <- c(4, 4, 4, 4, 4, 7, 7, 7, 7, 9, 9, 9, 10, 10, 13, 13, 13, 13) x4 <- c(1, 1, 1, 1, 1, 9, 9, 9, 9, 10, 10, 10, 4, 4, 2, 2, 2, 2) x5 <- c(32, 32, 32, 32, 32, 20, 20, 20, 20, 21, 21, 21, 19, 19, 16, 16, 16, 16) x6 <- c(15, 15, 15, 15, 15, 18, 18, 18, 18, 16, 16, 16, 25, 25, 20, 20, 20, 20) x7 <- c(28, 28, 28, 28, 28, 23, 23, 23, 23, 32, 32, 32, 24, 24, 32, 32, 32, 32) x8 <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0) x9 <- c(6, 6, 6, 6, 6, 10, 10, 10, 10, 11, 11, 11, 7, 7, 21, 21, 21, 21) x10 <- c(5, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8) data <- data.frame (y, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10) lsm2(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10, data) ## For more ease, use the following notation. lsm(y ~., data)
# Other case.
y <- as.factor(c(1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1)) x1 <- as.factor(c(2, 2, 2, 5, 5, 5, 5, 8, 8, 11, 11, 11)) x2 <- as.factor(c(3, 3, 3, 6, 6, 6, 6, 9, 9, 12, 12, 12)) x3 <- as.factor(c(4, 4, 4, 7, 7, 7, 7, 10, 10, 13, 13, 13)) x4 <- as.factor(c(1, 1, 1, 9, 9, 9, 9, 4, 4, 2, 2, 2)) x5 <- as.factor(c(5, 5, 5, 6, 6, 6, 6, 7, 7, 8, 8, 8)) data <- data.frame (y, x1, x2, x3, x4, x5) lsm2(y ~ x1 + x2 + x3 + x4 + x5, data) ## For more ease, use the following notation. lsm2(y~., data)
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