by Mary
Last Updated March 14, 2019 20:19 PM

I am fitting a `glm`

model:

`model_poisson<- glm(y~X, family="poisson"`

)

Where X is a matrix of 5 coloums in which a log trasnformation has been applied (`X<- log(X+0.0001)`

) since the data inside were extremely big.

```
Call:
glm(formula = y ~ X, family = "poisson")
Deviance Residuals:
Min 1Q Median 3Q Max
-298.83 -44.30 -12.06 29.77 1195.29
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.733e+00 5.894e-04 6333.9 <2e-16 ***
Xfemale 5.182e-01 7.667e-05 6759.6 <2e-16 ***
Xmale 9.882e-03 4.329e-05 228.3 <2e-16 ***
Xo 2.170e-01 8.620e-05 2517.5 <2e-16 ***
Xs 7.965e-02 4.736e-05 1681.8 <2e-16 ***
Xt 3.994e-02 4.539e-05 880.1 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 170764926 on 4942 degrees of freedom
Residual deviance: 36935043 on 4937 degrees of freedom
AIC: 36982563
Number of Fisher Scoring iterations: 6
```

How can I read the estimated coefficients?

I need also to access this model throught prediction, I have to fit the model on 80% of the data and test it on the other 20%.

```
ndata <- length(y)
ntraining <- ceiling(0.8*ndata)
ntest <- ndata-ntraining
training_indices<- sample(1:ndata, ntraining, replace=FALSE)
training_m <- m[training_indices]
training_X<- X[training_indices, ]
training_X<- log(training_X+0.00001)
test_set <- m[-training_indices]
test_X<- X[-training_indices, ]
test_X<-as.data.frame(test_X)
```

Hence my model is:

```
model_poisson_training<- glm(training_m ~ training_X, family="poisson")
summary(model_poisson_training)
coef.poisson<-model_poisson_training$coefficients
```

How can I use the test set?

Updated April 30, 2018 11:19 AM

- Serverfault Query
- Superuser Query
- Ubuntu Query
- Webapps Query
- Webmasters Query
- Programmers Query
- Dba Query
- Drupal Query
- Wordpress Query
- Magento Query
- Joomla Query
- Android Query
- Apple Query
- Game Query
- Gaming Query
- Blender Query
- Ux Query
- Cooking Query
- Photo Query
- Stats Query
- Math Query
- Diy Query
- Gis Query
- Tex Query
- Meta Query
- Electronics Query
- Stackoverflow Query
- Bitcoin Query
- Ethereum Query