Dear all, I am fitting a LOGIT model on this Data...........
Data <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
0, 1, 1, 0, 1, 0, 47, 58, 82, 100, 222, 164, 161, 70, 219, 81,
209, 182, 185, 104, 126, 192, 95, 245, 97, 177, 125, 56, 85,
199, 298, 145, 78, 144, 178, 146, 132, 98, 120, 148, 123, 282,
79, 34, 104, 91, 199, 101, 109, 117, 1.1, 0.92, 1.72, 2.18, 1.75,
2.26, 2.07, 1.43, 1.92, 1.82, 2.34, 2.12, 1.81, 1.35, 1.26, 2.07,
2.04, 1.55, 1.89, 1.68, 0.76, 1.96, 1.29, 1.81, 1.72, 2.39, 1.68,
2.29, 2.34, 2.21, 1.42, 1.97, 2.12, 1.9, 1.15, 1.7, 1.24, 1.55,
2.04, 1.59, 2.07, 2, 1.84, 2.04, 51.2, 48.5, 50.8, 54.4, 52.4,
56.7, 54.6, 52.7, 52.3, 53, 55.4, 53.5, 51.6, 48.5, 49.3, 53.9,
55.7, 51.2, 54, 52.2, 51.1, 54, 55, 52.9, 53.7, 55.8, 50.4, 58.8,
54.5, 53.5, 48.8, 54.5, 52.1, 56, 56.2, 53.3, 50.9, 53.2, 51.7,
54.3, 53.7, 54.7, 47, 56.9, 0.321, 0.224, 0.127, 0.063, 0.021,
0.027, 0.139, 0.218, 0.008, 0.012, 0.076, 0.299, 0.04, 0.069,
0.33, 0.017, 0.166, 0.003, 0.01, 0.076, 0.454, 0.032, 0.266,
0.018, 0.038, 0.067, 0.075, 0.064, 0.065, 0.065, 0.09, 0.016,
0.061, 0.019, 0.389, 0.037, 0.161, 0.127, 0.017, 0.222, 0.026,
0.012, 0.057, 0.022, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
0, 1, 1, 0, 1, 0, 0, 1, 0), .Dim = c(44L, 6L), .Dimnames = list(
c("Obs 1", "Obs 2", "Obs 3", "Obs 4", "Obs 5", "Obs 6", "Obs 7",
"Obs 8", "Obs 9", "Obs 10", "Obs 11", "Obs 12", "Obs 13",
"Obs 14", "Obs 15", "Obs 16", "Obs 17", "Obs 18", "Obs 19",
"Obs 20", "Obs 21", "Obs 22", "Obs 23", "Obs 24", "Obs 25",
"Obs 26", "Obs 27", "Obs 28", "Obs 29", "Obs 30", "Obs 31",
"Obs 32", "Obs 33", "Obs 34", "Obs 35", "Obs 36", "Obs 37",
"Obs 38", "Obs 39", "Obs 40", "Obs 41", "Obs 42", "Obs 43",
"Obs 44"), c("Y", "X 1", "X 2", "X 3", "X 4", "X 5")))
glm(Data[,1] ~ Data[,-1], binomial(link = logit))
Call: glm(formula = Data[, 1] ~ Data[, -1], family = binomial(link = logit))
Coefficients:
(Intercept) Data[, -1]X 1 Data[, -1]X 2 Data[, -1]X 3 Data[,
-1]X 4 Data[, -1]X 5
10.99326 0.01943 10.61013 -0.66763
70.98785 17.33126
Degrees of Freedom: 43 Total (i.e. Null); 38 Residual
Null Deviance: 44.58
Residual Deviance: 17.46 AIC: 29.46
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
However I am getting a warning mesage as "fitted probabilities
numerically 0 or 1 occurred". Here my question is, have I made any
mistakes with my above implementation? Is it just because, I have too
less number of '0' in my response Variable?
Thanks for your help.
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