Exponential Regression with Expon.txt and Logistic regression with logistic.txt

> e<-read.table("Expon.txt")

> e

 

    V1   V2     V3 V4   V5   V6

1    9  606  41393  3 3.04 6.32

2    6  641  23635 18 1.95 8.89

3   28  505  55475 27 6.54 2.05

108  6  817  54429 47 1.90 9.90

109  4  268  34022 54 1.20 9.51

110  6  519  52850 43 2.92 8.62

 

> y<-e[,1]

> x1<-e[,2]

> x2<-e[,3]

> x3<-e[,4]

> x4<-e[,5]

> x5<-e[,6]

> ee<-data.frame(cbind(y,x1,x2,x3,x4,x5))

> ee

     y   x1     x2 x3   x4   x5

1    9  606  41393  3 3.04 6.32

2    6  641  23635 18 1.95 8.89

3   28  505  55475 27 6.54 2.05

108  6  817  54429 47 1.90 9.90

109  4  268  34022 54 1.20 9.51

110  6  519  52850 43 2.92 8.62

 

> regexpon<-glm(y~x1+x2+x3+x4+x5,ee,family=poisson)

> summary(regexpon)

 

Call:

glm(formula = y ~ x1 + x2 + x3 + x4 + x5, family = poisson, data = ee)

 

Deviance Residuals:

       Min          1Q      Median          3Q         Max 

-2.932e+00  -5.887e-01  -9.434e-05   5.927e-01   2.234e+00 

 

Coefficients:

              Estimate Std. Error z value Pr(>|z|)   

(Intercept)  2.942e+00  2.072e-01  14.198  < 2e-16 ***

x1           6.058e-04  1.421e-04   4.262 2.02e-05 ***

x2          -1.169e-05  2.112e-06  -5.534 3.13e-08 ***

x3          -3.726e-03  1.782e-03  -2.091   0.0365 * 

x4           1.684e-01  2.577e-02   6.534 6.39e-11 ***

x5          -1.288e-01  1.620e-02  -7.948 1.89e-15 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

 

(Dispersion parameter for poisson family taken to be 1)

 

    Null deviance: 422.22  on 109  degrees of freedom

Residual deviance: 114.99  on 104  degrees of freedom

AIC: 571.02

 

Number of Fisher Scoring iterations: 4

 

> l<-read.table("logistic.txt")

> y<-l[,2]

> x<-l[,1]

> ll<-data.frame(cbind(y,x))

> reglog<-glm(y~x,ll,family=binomial)

> summary(reglog)

 

Call:

glm(formula = y ~ x, family = binomial, data = ll)

 

Deviance Residuals:

    Min       1Q   Median       3Q      Max 

-1.8992  -0.7509  -0.4140   0.7992   1.9624 

 

Coefficients:

            Estimate Std. Error z value Pr(>|z|) 

(Intercept) -3.05970    1.25935  -2.430   0.0151 *

x            0.16149    0.06498   2.485   0.0129 *

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

 

(Dispersion parameter for binomial family taken to be 1)

 

    Null deviance: 34.296  on 24  degrees of freedom

Residual deviance: 25.425  on 23  degrees of freedom

AIC: 29.425

 

Number of Fisher Scoring iterations: 4