Description Usage Arguments Value Author(s) References See Also Examples
glm.hermite
is used to fit generalized linear models with count
responses following a Hermite distribution, specified by giving a symbolic
description of the linear predictor. A summary
method providing the
most meaningful information on the fitted model is available for objects of
class glm.hermite
.
1 |
formula |
symbolic description of the model. A typical predictor has the form
|
data |
an optional data frame containing the variables in the model. |
link |
character specification of link function: "log" or "identity". By default
|
start |
a vector containing the starting values for the parameters of the specified
model. Its default value is |
m |
value for parameter |
glm.hermite
returns an object of class glm.hermite
, which is a
list including the following components:
coefs the vector of coefficients.
data an optional data frame containing the variables in the model.
loglik log-likelihood of the fitted model.
vcov
covariance matrix of all coefficients in the model (derived from the Hessian of
the maxLik
output).
hessHessian matrix, returned by the maxLik
output.
fitted.values the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.
wLikelihood ratio test statistic.
pvalLikelihood ratio test p-value.
María Oliveira, Manuel Higueras, David Moriña and Pere Puig
Kemp C D, Kemp A W. Some Properties of the Hermite Distribution. Biometrika 1965;52 (3-4):381–394.
McKendrick A G Applications of Mathematics to Medical Problems. Proceedings of the Edinburgh Mathematical Society 1926;44:98–130.
Kemp A W, Kemp C D. An alternative derivation of the Hermite distribution. Biometrika 1966;53 (3-4):627–628.
Patel Y C. Even Point Estimation and Moment Estimation in Hermite Distribution. Biometrics 1976;32 (4):865–873.
Gupta R P, Jain G C. A Generalized Hermite distribution and Its Properties. SIAM Journal on Applied Mathematics 1974;27:359–363.
Bekelis, D. Convolutions of the Poisson laws in number theory. In Analytic & Probabilistic Methods in Number Theory: Proceedings of the 2nd International Conference in Honour of J. Kubilius, Lithuania 1996;4:283–296.
Zhang J, Huang H. On Nonnegative Integer-Valued Lévy Processes and Applications in Probabilistic Number Theory and Inventory Policies. American Journal of Theoretical and Applied Statistics 2013;2:110–121.
Kotz S. Encyclopedia of statistical sciences. John Wiley 1982-1989.
Kotz S. Univariate discrete distributions. Norman L. Johnson 2005.
Puig P. (2003). Characterizing Additively Closed Discrete Models by a Property of Their Maximum Likelihood Estimators, with an Application to Generalized Hermite Distributions. Journal of the American Statistical Association 2003; 98:687–692.
Distributions
for some other distributions,
qhermite
, phermite
, rhermite
,
hermite-package
1 2 3 |
Loading required package: maxLik
Loading required package: miscTools
Please cite the 'maxLik' package as:
Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.
If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
https://r-forge.r-project.org/projects/maxlik/
$coefs
(Intercept) dispersion.index order
-0.2877067 1.8903869 3.0000000
$loglik
[1] -235.8354
$vcov
[,1] [,2]
[1,] 0.012594266 0.006583366
[2,] 0.006583366 0.017228285
$hess
[,1] [,2]
[1,] -99.22019 37.91456
[2,] 37.91456 -72.53220
$fitted.values
[1] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[8] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[15] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[22] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[29] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[36] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[43] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[50] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[57] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[64] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[71] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[78] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[85] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[92] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[99] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[106] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[113] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[120] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[127] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[134] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[141] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[148] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[155] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[162] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[169] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[176] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[183] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[190] 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815 0.7499815
[197] 0.7499815 0.7499815 0.7499815 0.7499815
$w
[1] 48.66494
$pval
[1] 1.518234e-12
attr(,"class")
[1] "glm.hermite"
attr(,"Call")
glm.hermite(formula = data ~ 1, link = "log", start = NULL, m = 3)
attr(,"x")
(Intercept)
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 1
9 1
10 1
11 1
12 1
13 1
14 1
15 1
16 1
17 1
18 1
19 1
20 1
21 1
22 1
23 1
24 1
25 1
26 1
27 1
28 1
29 1
30 1
31 1
32 1
33 1
34 1
35 1
36 1
37 1
38 1
39 1
40 1
41 1
42 1
43 1
44 1
45 1
46 1
47 1
48 1
49 1
50 1
51 1
52 1
53 1
54 1
55 1
56 1
57 1
58 1
59 1
60 1
61 1
62 1
63 1
64 1
65 1
66 1
67 1
68 1
69 1
70 1
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 1
93 1
94 1
95 1
96 1
97 1
98 1
99 1
100 1
101 1
102 1
103 1
104 1
105 1
106 1
107 1
108 1
109 1
110 1
111 1
112 1
113 1
114 1
115 1
116 1
117 1
118 1
119 1
120 1
121 1
122 1
123 1
124 1
125 1
126 1
127 1
128 1
129 1
130 1
131 1
132 1
133 1
134 1
135 1
136 1
137 1
138 1
139 1
140 1
141 1
142 1
143 1
144 1
145 1
146 1
147 1
148 1
149 1
150 1
151 1
152 1
153 1
154 1
155 1
156 1
157 1
158 1
159 1
160 1
161 1
162 1
163 1
164 1
165 1
166 1
167 1
168 1
169 1
170 1
171 1
172 1
173 1
174 1
175 1
176 1
177 1
178 1
179 1
180 1
181 1
182 1
183 1
184 1
185 1
186 1
187 1
188 1
189 1
190 1
191 1
192 1
193 1
194 1
195 1
196 1
197 1
198 1
199 1
200 1
attr(,"x")attr(,"assign")
[1] 0
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