This function obtains the summary table for objects of class marZIBPGeoffroy.

# S3 method for class 'ZIBPGeoffroy'
print(object, ...)

# S3 method for class 'marZIBPGeoffroy'
summary(object, ...)

# S3 method for class 'marZIBPGeoffroy'
print(object, ...)

Arguments

object

of class marZIBPGeoffroy.

...

aditional arguments.

Value

Returns the summary table.

Author

Freddy Hernandez-Barajas, fhernanb@unal.edu.co

Examples

# Example 1 ---------------------------------------------------------------
l1 <- 3
l2 <- 4
l0 <- 1.5
psi <- 0.20

set.seed(12345678)
data1 <- rZIBP_Geoffroy(n=500, l1=l1, l2=l2, l0=l0, psi=psi)
data1 <- as.data.frame(data1)

# To fit the model
mod1 <- NULL
mod1 <- marZIBP_Geoffroy(mu1.fo=X1~1,
                         mu2.fo=X2~1,
                         psi.fo=~1,
                         data=data1)

# To obtain the usual summary table
summary(mod1)
#> ---------------------------------------------------------------
#> Fixed effects for log(mu1) 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value  Pr(>|z|)    
#> (Intercept) 1.326544   0.031135  42.606 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------
#> Fixed effects for log(mu2) 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value  Pr(>|z|)    
#> (Intercept) 1.502743   0.029727  50.552 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------
#> Fixed effects for logit(psi) 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value  Pr(>|z|)    
#> (Intercept) -1.51725    0.11649 -13.025 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------
#> Estimation for l0 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value  Pr(>|z|)    
#> (Intercept)  1.35003    0.23462  5.7542 8.706e-09 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------

# To explore the estimations of l1, l2, mu and p

# To obtain E(Y1)=v1 and E(Y2)=v2
mod1$fitted.mu1[1]
#> [1] 3.768
mod1$fitted.mu2[1]
#> [1] 4.494

# To compare sample means with v1 and v2
colMeans(data1)
#>    X1    X2 
#> 3.768 4.494 

# To obtain l0 and psi
mod1$fitted.l0
#> [1] 1.350031
mod1$fitted.psi[1]
#> [1] 0.1798666

# To obtain l1 and l2
mod1$fitted.l1[1]
#> [1] 3.244343
mod1$fitted.l2[1]
#> [1] 4.129565

# Example 2 ---------------------------------------------------------------

gen_data_ZIBP_Geoffroy <- function(n=100) {
  # To generate the covariates
  x1 <- runif(n=n)
  x2 <- runif(n=n)

  # To generate the means
  mu1 <- exp(1 + 1.3 * x1)
  mu2 <- exp(1 + 2.1 * x2)

  # To generate the psi
  logit_inv <- function(x) exp(x) / (1+exp(x))
  psi <- logit_inv(-2.4 + 2.1 * x2)

  # The third lambda
  l0 <- 1

  # To obtain lambdas
  l1 <- mu1 / (1-psi) - l0
  l2 <- mu2 / (1-psi) - l0

  # To generate Y1 and Y2
  y <- NULL
  for (i in 1:n)
    y <- rbind(y, rZIBP_Geoffroy(n=1, l1=l1[i], l2=l2[i],
                                 l0=l0, psi=psi[i]))

  # To create the dataset
  dataset <- data.frame(y1=y[,1], y2=y[,2],
                        x1=x1, x2=x2,
                        mu1=mu1, mu2=mu2,
                        l0=l0, psi=psi,
                        l1=l1, l2=l2)

  return(dataset)
}

set.seed(12)
data2 <- gen_data_ZIBP_Geoffroy(n=300)
head(data2, n=8)
#>   y1 y2         x1        x2      mu1       mu2 l0       psi        l1
#> 1  1  8 0.06936092 0.5996109 2.974777  9.575261  1 0.2421704  2.925391
#> 2  8 27 0.81777520 0.8306384 7.870391 15.554356  1 0.3417154 10.955909
#> 3  0  0 0.94262173 0.7396654 9.257261 12.849403  1 0.3001250 12.227021
#> 4  7  8 0.26938188 0.5644036 3.858183  8.892849  1 0.2288612  4.003228
#> 5  4  4 0.16934812 0.3643197 3.387705  5.841989  1 0.1631563  3.048193
#> 6  0  0 0.03389562 0.9902194 2.840739 21.746671  1 0.4205443  3.902427
#> 7  5  4 0.17878500 0.3031845 3.429521  5.138122  1 0.1463760  3.017601
#> 8  6 18 0.64166537 0.7065033 6.259905 11.985013  1 0.2857036  7.763736
#>          l2
#> 1 11.635111
#> 2 22.628618
#> 3 17.359569
#> 4 10.532100
#> 5  5.980980
#> 6 36.529482
#> 7  5.019186
#> 8 15.778768

mod2 <- NULL
mod2 <- marZIBP_Geoffroy(mu1.fo=y1~x1,
                         mu2.fo=y2~x2,
                         psi.fo=~x2,
                         data=data2)

summary(mod2)
#> ---------------------------------------------------------------
#> Fixed effects for log(mu1) 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value  Pr(>|z|)    
#> (Intercept) 0.994025   0.065509  15.174 < 2.2e-16 ***
#> x1          1.261572   0.089517  14.093 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------
#> Fixed effects for log(mu2) 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value  Pr(>|z|)    
#> (Intercept) 1.025758   0.060641  16.915 < 2.2e-16 ***
#> x2          2.070244   0.097812  21.166 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------
#> Fixed effects for logit(psi) 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value  Pr(>|z|)    
#> (Intercept) -2.68273    0.27843 -9.6353 < 2.2e-16 ***
#> x2           2.44568    0.32549  7.5139 5.739e-14 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------
#> Estimation for l0 
#> ---------------------------------------------------------------
#>             Estimate Std. Error z value Pr(>|z|)   
#> (Intercept)  1.09401    0.39189  2.7916 0.005245 **
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---------------------------------------------------------------