Extraction functions for 'logitx' objects

Extraction functions (of type S3 methods) for objects of class 'logitx'

Usage

# S3 method for class 'logitx'
coef(object, ...)
  # S3 method for class 'logitx'
fitted(object, zero.prob=FALSE, ...)
  # S3 method for class 'logitx'
logLik(object, ...)
  # S3 method for class 'logitx'
plot(x, ...)
  # S3 method for class 'logitx'
print(x, signif.stars=TRUE, ...)
  # S3 method for class 'logitx'
summary(object, ...)
  # S3 method for class 'logitx'
toLatex(object, digits = 4, gof = TRUE, nonumber = FALSE, nobs = "T", ...)
  # S3 method for class 'logitx'
vcov(object, ...)

Arguments

object

an object of class 'logitx'

x

an object of class 'logitx'

zero.prob

logical. If FALSE (default), then the probabilities of a one are returned as fitted values. If TRUE, then the zero probabilities are returned as fitted values

signif.stars

logical. If TRUE, then p-values are additionally encoded visually, see printCoefmat

digits

integer, the number of digits in the LaTeX print

gof

logical that determines whether goodness-of-fit information should be included in the LaTeX print

nonumber

logical that determines whether a "nonumber" tag should be added to each equation in the LaTeX print

nobs

character that determines the label for the number of observations in the LaTeX print

...

additional arguments

Value

Various, depending on the method

Author

Genaro Sucarrat, http://www.sucarrat.net/

See also

logitx, logitxSim, gets.logitx

Examples

##simulate from ar(1):
set.seed(123) #for reproducibility
y <- logitxSim(100, ar=0.3)

##estimate and store result:
mymod <- logitx(y, ar=1)

##extract stuff:
coef(mymod)
#>  intercept        ar1 
#> -0.2876818  0.6931466 
fitted(mymod)
#>         2         3         4         5         6         7         8         9 
#> 0.4285715 0.5999999 0.4285715 0.5999999 0.5999999 0.4285715 0.5999999 0.5999999 
#>        10        11        12        13        14        15        16        17 
#> 0.5999999 0.5999999 0.5999999 0.5999999 0.5999999 0.5999999 0.4285715 0.5999999 
#>        18        19        20        21        22        23        24        25 
#> 0.4285715 0.4285715 0.4285715 0.5999999 0.5999999 0.5999999 0.5999999 0.5999999 
#>        26        27        28        29        30        31        32        33 
#> 0.5999999 0.5999999 0.5999999 0.5999999 0.4285715 0.4285715 0.5999999 0.5999999 
#>        34        35        36        37        38        39        40        41 
#> 0.5999999 0.5999999 0.4285715 0.4285715 0.5999999 0.4285715 0.4285715 0.4285715 
#>        42        43        44        45        46        47        48        49 
#> 0.4285715 0.4285715 0.4285715 0.4285715 0.4285715 0.4285715 0.4285715 0.4285715 
#>        50        51        52        53        54        55        56        57 
#> 0.4285715 0.5999999 0.4285715 0.4285715 0.5999999 0.4285715 0.5999999 0.4285715 
#>        58        59        60        61        62        63        64        65 
#> 0.4285715 0.5999999 0.5999999 0.4285715 0.5999999 0.4285715 0.4285715 0.4285715 
#>        66        67        68        69        70        71        72        73 
#> 0.5999999 0.5999999 0.5999999 0.5999999 0.5999999 0.5999999 0.5999999 0.5999999 
#>        74        75        76        77        78        79        80        81 
#> 0.5999999 0.4285715 0.4285715 0.4285715 0.4285715 0.5999999 0.4285715 0.4285715 
#>        82        83        84        85        86        87        88        89 
#> 0.4285715 0.5999999 0.4285715 0.5999999 0.4285715 0.4285715 0.5999999 0.5999999 
#>        90        91        92        93        94        95        96        97 
#> 0.5999999 0.4285715 0.4285715 0.5999999 0.4285715 0.5999999 0.4285715 0.4285715 
#>        98        99       100 
#> 0.5999999 0.4285715 0.4285715 
logLik(mymod)
#> 'log Lik.' -67.11308 (df=2)
plot(mymod)

print(mymod)
#> 
#> Date: Sat Jul 27 15:29:51 2024 
#> Dependent var.: y 
#> Method: Maximum Likelihood (logit) 
#> Variance-Covariance: Ordinary 
#> No. of observations: 99 
#> Sample: 2 to 100 
#> 
#> Estimation results:
#> 
#>               coef std.error  t-stat p-value  
#> intercept -0.28768   0.28868 -0.9966 0.16073  
#> ar1        0.69315   0.40825  1.6979 0.04637 *
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>                        
#> Log-lik.(n=99) -67.1131
summary(mymod)
#>                Length Class  Mode     
#> call             3    -none- call     
#> date             1    -none- character
#> version          1    -none- character
#> control          0    -none- list     
#> y               99    -none- numeric  
#> y.name           1    -none- character
#> y.index         99    -none- numeric  
#> mX             198    -none- numeric  
#> mXnames          2    -none- character
#> logit.method     1    -none- numeric  
#> n                1    -none- numeric  
#> k                1    -none- numeric  
#> df               1    -none- numeric  
#> initial.values   2    -none- numeric  
#> lower            1    -none- numeric  
#> upper            1    -none- numeric  
#> eps.tol          1    -none- numeric  
#> coefficients     2    -none- numeric  
#> logl             1    -none- numeric  
#> convergence      1    -none- numeric  
#> iterations       1    -none- numeric  
#> evaluations      2    -none- numeric  
#> message          1    -none- character
#> fit             99    zoo    numeric  
#> vcov             4    -none- numeric  
toLatex(mymod)
#> \begin{eqnarray}
#>   Pr(y_t = 1| ...) &=& \frac{1}{1 + \exp(-\widehat{h}_t)} \\[2mm] 
#>   \widehat{h}_t &=&  - \underset{(0.2887)}{0.2877} + \underset{(0.4082)}{0.6931}ar1 \\[2mm] 
#>    && LogL=-67.1131 \qquad T = 99 \nonumber 
#> \end{eqnarray}