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--- neuroimagen:altdti [2019/03/25 08:59]
osotolongo [Todas las redes]
+++ neuroimagen:altdti [2020/08/04 10:58] (current)
@@ Line 1724: / Line 1724: @@
 F-statistic: 8.719 on 2 and 46 DF,  p-value: 0.0006158
 </code>
+===== Riesgo - No riesgo =====
+Vamosaplantear el problema de manera distinta. Supongamos que la contribucion del APOE depende solo de la presencia del alelo $\epsilon$-4 y clasifiquemos los sujetos segun esto, en //con riesgo// o //sin riesgo//.
+<code R>
+> okdata$Risk <- ifelse (okdata$APOE==2 , 1, 0)
+</code>
+Pero ahora voy a hacer una cosa un poco mas complicada,
+<code R get_lms2.r>
+library(QuantPsyc)
+x<-read.csv("facehbi_dti_np.csv")
+Color=c("red","blue")
+scan("npvars.names", what = character())->np
+scan("nivars.names", what = character())->ni
+sink(file = "facehbi_dti_np_models.txt", append = TRUE, type = "output", split = FALSE)
+for(i in 1:length(np)){
+        for(j in 1:length(ni)){
+                y.data <- x[c(ni[j], np[i], "female", "Edad", "Escolaridad", "SUVR", "Risk")]
+                y.data <- y.data[complete.cases(y.data),]
+                a <- lm( paste ('y.data$', np[i], ' ~ y.data$', ni[j], ' + y.data$SUVR +y.data$Risk + y.data$female + y.data$Edad + y.data$Escolaridad + ', 'y.data$', ni[j], '*y.data$Risk'))
+                writeLines(paste("NP: ", np[i], " NI: ", ni[j]))
+                writeLines(paste("R2: ", summary(a)$adj.r.squared, " p-value: ", 1-pf(summary(a)$fstatistic[1], summary(a)$fstatistic[2], summary(a)$fstatistic[3])))
+                writeLines(paste("p-value (", ni[j],"): ", summary(a)$coef[2,4], " p-value (SUVR): ", summary(a)$coef[3,4]))
+                beta <- lm.beta(a)
+                for(k in 1:length(beta)){
+                        writeLines(paste(names(beta[k]), ": ", beta[k]))
+                }
+                writeLines(paste("-------"))
+        }
+}
+sink()
+</code>
+Asi que pruebo con el global,
+<code R>
+> write.csv(okdata, file="facehbi_dti_np.csv")
+> source("get_lms2.r")
+</code>
+y luego,
+<code bash>
+[osotolongo@detritus dti_model]$ ./checkr2.pl
+Analizing facehbi_dti_np_models.txt ...
+DMN_FA, funcioExecutiva_velocprocess_IM
+r2 = 0.312799089824939, p-value = 2.93098878501041e-14
+pv_DMN_FA = 0.476462254461098, pv_SUVR = 0.179281559330912
+SN_FA, funcioExecutiva_velocprocess_IM
+r2 = 0.311504329049638, p-value = 3.47499806707674e-14
+pv_SN_FA = 0.551975051522526, pv_SUVR = 0.181998130409768
+FPCustom_FA, funcioExecutiva_velocprocess_IM
+r2 = 0.311678871879767, p-value = 3.39728245535298e-14
+pv_FPCustom_FA = 0.151421806156447, pv_SUVR = 0.201473541858229
+</code>
+puaf, a ver,
+<code R>
+> m <- lm(okdata$funcioExecutiva_velocprocess_IM ~ okdata$SUVR + okdata$Edad + okdata$Escolaridad + okdata$female + okdata$DMN_FA*okdata$Risk)
+> summary(m)
+Call:
+lm(formula = okdata$funcioExecutiva_velocprocess_IM ~ okdata$SUVR +
+    okdata$Edad + okdata$Escolaridad + okdata$female + okdata$DMN_FA *
+    okdata$Risk)
+Residuals:
+    Min      1Q  Median      3Q     Max
+-1.4094 -0.5672 -0.1264  0.3442  4.3680
+Coefficients:
+                           Estimate Std. Error t value Pr(>|t|)
+(Intercept)                -4.12971    1.33068  -3.103  0.00221 **
+okdata$SUVR                 0.58201    0.43176   1.348  0.17928
+okdata$Edad                 0.05310    0.00865   6.139 4.82e-09 ***
+okdata$Escolaridad         -0.04301    0.01362  -3.157  0.00186 **
+okdata$female              -0.38688    0.13066  -2.961  0.00346 **
+okdata$DMN_FA               2.24716    3.14978   0.713  0.47646
+okdata$Risk                 5.03080    2.38260   2.111  0.03605 *
+okdata$DMN_FA:okdata$Risk -15.70430    7.30118  -2.151  0.03276 *
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Residual standard error: 0.8286 on 188 degrees of freedom
+  (4 observations deleted due to missingness)
+Multiple R-squared:  0.3375,	Adjusted R-squared:  0.3128
+F-statistic: 13.68 on 7 and 188 DF,  p-value: 2.936e-14
+</code>
+No, gracias. :-\

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