[R] GLM problems.
Daniel Jimenez
jimenez.daniel77 at gmail.com
Thu Mar 1 18:44:42 CET 2007
Dear R users I'm new in R management and maybe It's a silly question. I'm
working with GLM to obtain predictive models. I have some problesm with the
prediction instruction:
> DatosTotal <- read.csv("Var_perdizcsv.csv", sep =";")
> edvariable <- edit(DatosTotal)
> pre <- predict(rlfinal, DatosTotal, type = 'probs')
Erro en match.arg(type) : 'arg' should be one of link, response, terms
>
I check the database and the name variables are the same... I do not know
what happen. Please help me.
I attach the complet proccess.
Thank you.
--
Daniel Jiménez García
------------ próxima parte ------------
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[Previously saved workspace restored]
> Datos <- read.csv("variables_perdiz_01_03.csv", sep =";")
> attach(Datos)
> library(MASS)
>
> rl0 <- glm(PRESAUS ~ 1, family = binomial)
>
> rl1 <- stepAIC(rl0, direction = c("both"), scope = PRESAUS ~ 1 + elev + vias + orient + pend +
+ freg + frsec + labsec + matarb + matcl + matden + ripar + visec + pinar + forest + matorrales +
+ abandonos + ombro + termic, keep = extractAIC)
Start: AIC= 99.04
PRESAUS ~ 1
Df Deviance AIC
+ freg 1 91.415 95.415
+ matorrales 1 93.457 97.457
+ ripar 1 93.766 97.766
+ matarb 1 94.975 98.975
<none> 97.041 99.041
+ forest 1 95.043 99.043
+ abandonos 1 95.093 99.093
+ matden 1 95.133 99.133
+ ombro 1 95.655 99.655
+ pend 1 95.986 99.986
+ termic 1 96.156 100.156
+ vias 1 96.641 100.641
+ elev 1 96.680 100.680
+ pinar 1 96.710 100.710
+ orient 1 96.775 100.775
+ labsec 1 96.987 100.987
+ visec 1 97.028 101.028
+ matcl 1 97.040 101.040
+ frsec 1 97.041 101.041
Step: AIC= 95.42
PRESAUS ~ freg
Df Deviance AIC
+ termic 1 87.885 93.885
+ abandonos 1 88.134 94.134
+ forest 1 88.550 94.550
+ elev 1 88.668 94.668
+ ripar 1 88.802 94.802
<none> 91.415 95.415
+ matorrales 1 89.648 95.648
+ matarb 1 90.023 96.023
+ matden 1 90.443 96.443
+ pinar 1 90.796 96.796
+ vias 1 91.154 97.154
+ ombro 1 91.156 97.156
+ pend 1 91.233 97.233
+ frsec 1 91.340 97.340
+ matcl 1 91.345 97.345
+ visec 1 91.347 97.347
+ labsec 1 91.378 97.378
+ orient 1 91.410 97.410
- freg 1 97.041 99.041
Step: AIC= 93.89
PRESAUS ~ freg + termic
Df Deviance AIC
+ abandonos 1 83.370 91.370
+ matorrales 1 83.491 91.491
+ matden 1 83.771 91.771
<none> 87.885 93.885
+ ripar 1 86.312 94.312
+ elev 1 86.501 94.501
+ forest 1 86.719 94.719
+ pend 1 86.743 94.743
+ visec 1 87.118 95.118
+ matarb 1 87.242 95.242
- termic 1 91.415 95.415
+ orient 1 87.416 95.416
+ vias 1 87.498 95.498
+ labsec 1 87.544 95.544
+ ombro 1 87.718 95.718
+ pinar 1 87.720 95.720
+ frsec 1 87.856 95.856
+ matcl 1 87.878 95.878
- freg 1 96.156 100.156
Step: AIC= 91.37
PRESAUS ~ freg + termic + abandonos
Df Deviance AIC
+ matden 1 80.343 90.343
+ matorrales 1 80.823 90.823
<none> 83.370 91.370
+ visec 1 81.465 91.465
+ ripar 1 81.791 91.791
+ forest 1 82.089 92.089
+ labsec 1 82.335 92.335
+ pend 1 82.699 92.699
+ vias 1 83.057 93.057
+ elev 1 83.091 93.091
+ matarb 1 83.166 93.166
+ pinar 1 83.193 93.193
+ orient 1 83.242 93.242
+ ombro 1 83.282 93.282
+ matcl 1 83.357 93.357
+ frsec 1 83.369 93.369
- abandonos 1 87.885 93.885
- termic 1 88.134 94.134
- freg 1 94.026 100.026
Step: AIC= 90.34
PRESAUS ~ freg + termic + abandonos + matden
Df Deviance AIC
<none> 80.343 90.343
+ ripar 1 78.766 90.766
+ visec 1 78.862 90.862
- matden 1 83.370 91.370
- abandonos 1 83.771 91.771
+ pend 1 79.825 91.825
+ labsec 1 79.837 91.837
+ matorrales 1 79.861 91.861
+ forest 1 79.965 91.965
+ matarb 1 80.009 92.009
+ frsec 1 80.087 92.087
+ vias 1 80.171 92.171
+ matcl 1 80.217 92.217
+ elev 1 80.240 92.240
+ ombro 1 80.322 92.322
+ pinar 1 80.329 92.329
+ orient 1 80.342 92.342
- termic 1 87.726 95.726
- freg 1 90.534 98.534
> tabla <- data.frame(rl1$keep)
> tabla
X1 X2 X3 X4 X5
1 1.0000 2.00000 3.00000 4.0000 5.00000
2 99.0406 95.41544 93.88513 91.3704 90.34343
> rlfinal <- glm(PRESAUS ~ freg + termic + abandonos + matden , family = binomial)
> summary (rlfinal)
Call:
glm(formula = PRESAUS ~ freg + termic + abandonos + matden, family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.08022 -0.82070 0.04674 0.90439 1.95034
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.888148 2.533235 -2.324 0.02011 *
freg -0.004076 0.001440 -2.830 0.00465 **
termic 0.017090 0.006828 2.503 0.01231 *
abandonos -0.002735 0.001556 -1.758 0.07871 .
matden 0.002493 0.001534 1.625 0.10414
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 97.041 on 69 degrees of freedom
Residual deviance: 80.343 on 65 degrees of freedom
AIC: 90.343
Number of Fisher Scoring iterations: 4
> DatosTotal <- read.csv("Var_perdizcsv.csv", sep =";")
> edvariable <- edit(DatosTotal)
> pre <- predict(rlfinal, DatosTotal, type = 'probs')
Erro en match.arg(type) : 'arg' should be one of link, response, terms
>
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