R help - Apr 2015 - Question on funcion and npar

On Thu, 11 Feb 2010, Luciano La Sala wrote:
> Dear R crew:
>
> I am sorry this question has been posted before, but I can't seem to
solve
> this problem yet.
Just for the others reader who might not recall that you asked virtually 
the same question last week. This was my reply:
   https://stat.ethz.ch/pipermail/r-help/2010-February/227040.html
> I have a simple dataset consisting of two variables: cestode intensity and
> chick size (defined as CAPI).
> Intensity is a count and clearly overdispersed, with way too many zeroes.
> I'm interested in looking at the association between these two
variables,
> i.e. how well does chick size predict tape intensity?
>
> Since I have a small sample size, I fit a zero inflated negat. Binomial
(not
> Poisson) model using the "pscl" package.
>
> I built tried two models and got the outputs below.
>
>> model <- zeroinfl(Int_Cesto ~ CAPI, dist = "negbin", EM =
TRUE)
>
> Call:
> zeroinfl(formula = Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE)
>
> Count model coefficients (negbin with log link):
> (Intercept)         CAPI
>   -2.99182      0.06817
> Theta = 0.4528
>
> Zero-inflation model coefficients (binomial with logit link):
> (Intercept)         CAPI
>    12.1364      -0.1572
>
>> summary(model)
>
> Call:
> zeroinfl(formula = Int_Cesto ~ CAPI, dist = "negbin", EM = TRUE)
>
> Pearson residuals:
>     Min       1Q   Median       3Q      Max
> -0.62751 -0.38842 -0.21303 -0.06899  7.29566
>
> Count model coefficients (negbin with log link):
>            Estimate Std. Error z value Pr(>|z|)
> (Intercept) -2.99182    3.39555  -0.881   0.3783
> CAPI         0.06817    0.04098   1.664   0.0962 .
> Log(theta)  -0.79222    0.45031  -1.759   0.0785 .
>
> Zero-inflation model coefficients (binomial with logit link):
>            Estimate Std. Error z value Pr(>|z|)
> (Intercept) 12.13636    3.71918   3.263  0.00110 **
> CAPI        -0.15720    0.04989  -3.151  0.00163 **
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
>
> Theta = 0.4528
> Number of iterations in BFGS optimization: 1
> Log-likelihood: -140.2 on 5 Df
>
> QUESTIONS
>
> 1. Is my model adequately specified?
See my reply linked above. I guess it's hard to say more than that.
> 2. CAPI is included in blocks 1 of output containing negative binomial
> regression coefficients for CAPI, and is also included also in block 2
> corresponding to the inflation model. Does this make sense?
Dito.
> If I specify my model slightly differently, I get what I believe is more
> reasonable results:
>
>> model12 <- zeroinfl(Int_Cesto ~ 1|CAPI, dist = "negbin",
EM = TRUE)
>> model12
>
> Call:
> zeroinfl(formula = Int_Cesto ~ 1 | CAPI, dist = "negbin", EM =
TRUE)
>
> Count model coefficients (negbin with log link):
> (Intercept)
>      2.692
> Theta = 0.4346
>
> Zero-inflation model coefficients (binomial with logit link):
> (Intercept)         CAPI
>    13.2476      -0.1708
>
>> summary(model12)
>
> Call:
> zeroinfl(formula = Int_Cesto ~ 1 | CAPI, dist = "negbin", EM =
TRUE)
>
> Pearson residuals:
>     Min       1Q   Median       3Q      Max
> -0.61616 -0.36902 -0.19466 -0.06666  4.85481
>
> Count model coefficients (negbin with log link):
>            Estimate Std. Error z value Pr(>|z|)
> (Intercept)   2.6924     0.3031   8.883   <2e-16 ***
> Log(theta)   -0.8334     0.4082  -2.042   0.0412 *
>
> Zero-inflation model coefficients (binomial with logit link):
>            Estimate Std. Error z value Pr(>|z|)
> (Intercept) 13.24757    3.64531   3.634 0.000279 ***
> CAPI        -0.17078    0.04921  -3.471 0.000519 ***
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
>
> Theta = 0.4346
> Number of iterations in BFGS optimization: 1
> Log-likelihood: -141.9 on 4 Df
>
> QUESTION:
>
> 1.    Is this model specification and output more reasonable?
> 2.    CAPI appears only in the second block that corresponds to the
> inflation model.
You can apply standard model selection techniques. Hands-on examples for 
that are in the vignette that I pointed you to last week:
   vignette("countreg", package = "pscl")

Different model selection strategies may however yield different models. 
AIC will prefer the model with CAPI in the count equation. In contrast, 
a Wald test at 5% level would drop it. You could also look at the BIC and 
at the LR test. My guess is though that the answer will be: CAPI has some 
weak but practically not very relevant influence on the mean in the count 
component.

But I strongly recommend that you try to get more familiar with the 
zero-inflated model and the general model selection strategies. Or you 
could try to get help from a local statistician to obtain an appropriate 
model and interpret its results.

hth,
Z
>
> Thanks in advance!
> Luciano
>
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Dear everyone,

The following function, taken from Quick-R, gets measures of central 
tendency and spread for a numeric vector x.

I can't figure out what the argument npar means in each instance.
Any tips will be most appreciated.

mysummary <- function(x, npar=TRUE, print=TRUE) {
   if (!npar) {
     center <- mean(x); spread <- sd(x)
   } else {
     center <- median(x); spread <- mad(x)
   }
   if (print & !npar) {
     cat("Mean=", center, "\n", "SD=", spread,
"\n")
   } else if (print & npar) {
     cat("Median=", center, "\n", "MAD=", spread,
"\n")
   }
   result <- list(center=center,spread=spread)
   return(result)
}


-- 
Luciano F. La Sala
Consejo Nacional de Investigaciones Cient?ficas y T?cnicas (CONICET)
C?tedra de Epidemiolog?a
Departamento de Biolog?a, Bioqu?mica y Farmacia
Universidad Nacional del Sur
San Juan 670
Bah?a Blanca (8000)
Argentina