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
R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) ISBN 3-900051-07-0 R es un software libre y viene sin GARANTIA ALGUNA. Usted puede redistribuirlo bajo ciertas circunstancias. Escriba 'license()' o 'licence()' para detalles de distribucion. R es un proyecto colaborativo con muchos contribuyentes. Escriba 'contributors()' para obtener mas informacion y 'citation()' para saber como citar R o paquetes de R en publicaciones. Escriba 'demo()' para demostraciones, 'help()' para el sistema on-line de ayuda, o 'help.start()' para abrir el sistema de ayuda HTML con su navegador. Escriba 'q()' para salir de R. [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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