neuroimagen:altdti
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neuroimagen:altdti [2019/03/22 10:25] – [Todas las redes] osotolongo | neuroimagen:altdti [2020/08/04 10:58] (current) – external edit 127.0.0.1 | ||
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Line 1532: | Line 1532: | ||
[osotolongo@detritus dti_model]$ ./ | [osotolongo@detritus dti_model]$ ./ | ||
Analizing facehbi_dti_np_models_all.txt ... | Analizing facehbi_dti_np_models_all.txt ... | ||
+ | |||
DMN_FA, funcioExecutiva_velocprocess_IM | DMN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.302226155665606, | r2 = 0.302226155665606, | ||
pv_DMN_FA = 0.883576950793585, | pv_DMN_FA = 0.883576950793585, | ||
+ | |||
LN_FA, funcioExecutiva_velocprocess_IM | LN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.302579788141454, | r2 = 0.302579788141454, | ||
pv_LN_FA = 0.731754149108559, | pv_LN_FA = 0.731754149108559, | ||
+ | |||
SN_FA, funcioExecutiva_velocprocess_IM | SN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.302699583800708, | r2 = 0.302699583800708, | ||
pv_SN_FA = 0.698477685239432, | pv_SN_FA = 0.698477685239432, | ||
+ | |||
FPCustom_FA, | FPCustom_FA, | ||
r2 = 0.303857285316447, | r2 = 0.303857285316447, | ||
Line 1551: | Line 1555: | ||
[osotolongo@detritus dti_model]$ ./ | [osotolongo@detritus dti_model]$ ./ | ||
Analizing facehbi_dti_np_models_0.txt ... | Analizing facehbi_dti_np_models_0.txt ... | ||
+ | |||
DMN_FA, funcioExecutiva_velocprocess_IM | DMN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.379552223636583, | r2 = 0.379552223636583, | ||
pv_DMN_FA = 0.639790744923555, | pv_DMN_FA = 0.639790744923555, | ||
+ | |||
LN_FA, funcioExecutiva_velocprocess_IM | LN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.373746758451691, | r2 = 0.373746758451691, | ||
pv_LN_FA = 0.922726301726418, | pv_LN_FA = 0.922726301726418, | ||
+ | |||
SN_FA, funcioExecutiva_velocprocess_IM | SN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.373504904382816, | r2 = 0.373504904382816, | ||
pv_SN_FA = 0.978604565669928, | pv_SN_FA = 0.978604565669928, | ||
+ | |||
FPCustom_FA, | FPCustom_FA, | ||
r2 = 0.376679557258316, | r2 = 0.376679557258316, | ||
pv_FPCustom_FA = 0.734462972707933, | pv_FPCustom_FA = 0.734462972707933, | ||
+ | |||
DMN_FA, memoria_wms | DMN_FA, memoria_wms | ||
r2 = 0.384900678414176, | r2 = 0.384900678414176, | ||
pv_DMN_FA = 0.971755525011924, | pv_DMN_FA = 0.971755525011924, | ||
+ | |||
LN_FA, memoria_wms | LN_FA, memoria_wms | ||
r2 = 0.476189288707085, | r2 = 0.476189288707085, | ||
pv_LN_FA = 0.0571602758952463, | pv_LN_FA = 0.0571602758952463, | ||
+ | |||
SN_FA, memoria_wms | SN_FA, memoria_wms | ||
r2 = 0.4105573407532, | r2 = 0.4105573407532, | ||
pv_SN_FA = 0.327134537994976, | pv_SN_FA = 0.327134537994976, | ||
+ | |||
FPCustom_FA, | FPCustom_FA, | ||
r2 = 0.38938771125582, | r2 = 0.38938771125582, | ||
Line 1584: | Line 1596: | ||
[osotolongo@detritus dti_model]$ ./ | [osotolongo@detritus dti_model]$ ./ | ||
Analizing facehbi_dti_np_models_2.txt ... | Analizing facehbi_dti_np_models_2.txt ... | ||
+ | |||
DMN_FA, funcioExecutiva_velocprocess_IM | DMN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.485798156550142, | r2 = 0.485798156550142, | ||
pv_DMN_FA = 0.00443564461139048, | pv_DMN_FA = 0.00443564461139048, | ||
+ | |||
LN_FA, funcioExecutiva_velocprocess_IM | LN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.391142446695136, | r2 = 0.391142446695136, | ||
pv_LN_FA = 0.298247246087403, | pv_LN_FA = 0.298247246087403, | ||
+ | |||
SN_FA, funcioExecutiva_velocprocess_IM | SN_FA, funcioExecutiva_velocprocess_IM | ||
r2 = 0.473679765360014, | r2 = 0.473679765360014, | ||
pv_SN_FA = 0.0075840881661726, | pv_SN_FA = 0.0075840881661726, | ||
+ | |||
FPCustom_FA, | FPCustom_FA, | ||
r2 = 0.420481875101633, | r2 = 0.420481875101633, | ||
pv_FPCustom_FA = 0.0767718841930776, | pv_FPCustom_FA = 0.0767718841930776, | ||
+ | |||
DMN_FA, memoria_fnameProf | DMN_FA, memoria_fnameProf | ||
r2 = 0.327588088792215, | r2 = 0.327588088792215, | ||
pv_DMN_FA = 0.773974302319369, | pv_DMN_FA = 0.773974302319369, | ||
+ | |||
LN_FA, memoria_fnameProf | LN_FA, memoria_fnameProf | ||
r2 = 0.382914152316493, | r2 = 0.382914152316493, | ||
pv_LN_FA = 0.0561917139565343, | pv_LN_FA = 0.0561917139565343, | ||
+ | |||
SN_FA, memoria_fnameProf | SN_FA, memoria_fnameProf | ||
r2 = 0.327676768593875, | r2 = 0.327676768593875, | ||
pv_SN_FA = 0.76680425794751, | pv_SN_FA = 0.76680425794751, | ||
+ | |||
FPCustom_FA, | FPCustom_FA, | ||
r2 = 0.331099185559454, | r2 = 0.331099185559454, | ||
pv_FPCustom_FA = 0.584035234528739, | pv_FPCustom_FA = 0.584035234528739, | ||
+ | |||
DMN_FA, memoria_fnameNom | DMN_FA, memoria_fnameNom | ||
r2 = 0.328367049483929, | r2 = 0.328367049483929, | ||
pv_DMN_FA = 0.898296424312723, | pv_DMN_FA = 0.898296424312723, | ||
+ | |||
LN_FA, memoria_fnameNom | LN_FA, memoria_fnameNom | ||
r2 = 0.330867944990235, | r2 = 0.330867944990235, | ||
pv_LN_FA = 0.679077371093828, | pv_LN_FA = 0.679077371093828, | ||
+ | |||
SN_FA, memoria_fnameNom | SN_FA, memoria_fnameNom | ||
r2 = 0.331778400581458, | r2 = 0.331778400581458, | ||
pv_SN_FA = 0.633252108652689, | pv_SN_FA = 0.633252108652689, | ||
+ | |||
FPCustom_FA, | FPCustom_FA, | ||
r2 = 0.334618953643581, | r2 = 0.334618953643581, | ||
pv_FPCustom_FA = 0.524784142484717, | pv_FPCustom_FA = 0.524784142484717, | ||
+ | |||
DMN_FA, memoria_wms | DMN_FA, memoria_wms | ||
r2 = 0.30272247894919, | r2 = 0.30272247894919, | ||
pv_DMN_FA = 0.228059068988977, | pv_DMN_FA = 0.228059068988977, | ||
+ | |||
</ | </ | ||
+ | |||
+ | Voy a mirar un poco, | ||
+ | |||
+ | <code R> | ||
+ | > m0 <- lm(okdata0$funcioExecutiva_velocprocess_IM ~ okdata0$SUVR + okdata0$Edad) | ||
+ | > summary(m0) | ||
+ | |||
+ | Call: | ||
+ | lm(formula = okdata0$funcioExecutiva_velocprocess_IM ~ okdata0$SUVR + | ||
+ | okdata0$Edad) | ||
+ | |||
+ | Residuals: | ||
+ | Min 1Q Median | ||
+ | -1.3337 -0.5111 -0.1135 | ||
+ | |||
+ | Coefficients: | ||
+ | | ||
+ | (Intercept) | ||
+ | okdata0$SUVR -6.70012 | ||
+ | okdata0$Edad | ||
+ | --- | ||
+ | Signif. codes: | ||
+ | |||
+ | Residual standard error: 0.9002 on 26 degrees of freedom | ||
+ | Multiple R-squared: | ||
+ | F-statistic: | ||
+ | |||
+ | > m1 <- lm(okdata1$funcioExecutiva_velocprocess_IM ~ okdata1$SUVR + okdata1$Edad) | ||
+ | > summary(m1) | ||
+ | |||
+ | Call: | ||
+ | lm(formula = okdata1$funcioExecutiva_velocprocess_IM ~ okdata1$SUVR + | ||
+ | okdata1$Edad) | ||
+ | |||
+ | Residuals: | ||
+ | Min 1Q Median | ||
+ | -1.2138 -0.5730 -0.1959 | ||
+ | |||
+ | Coefficients: | ||
+ | Estimate Std. Error t value Pr(> | ||
+ | (Intercept) | ||
+ | okdata1$SUVR | ||
+ | okdata1$Edad | ||
+ | --- | ||
+ | Signif. codes: | ||
+ | |||
+ | Residual standard error: 0.8937 on 118 degrees of freedom | ||
+ | (1 observation deleted due to missingness) | ||
+ | Multiple R-squared: | ||
+ | F-statistic: | ||
+ | |||
+ | > m2 <- lm(okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$SUVR + okdata2$Edad) | ||
+ | > summary(m2) | ||
+ | |||
+ | Call: | ||
+ | lm(formula = okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$SUVR + | ||
+ | okdata2$Edad) | ||
+ | |||
+ | Residuals: | ||
+ | | ||
+ | -1.74708 -0.28264 -0.07348 | ||
+ | |||
+ | Coefficients: | ||
+ | | ||
+ | (Intercept) | ||
+ | okdata2$SUVR | ||
+ | okdata2$Edad | ||
+ | --- | ||
+ | Signif. codes: | ||
+ | |||
+ | Residual standard error: 0.8034 on 46 degrees of freedom | ||
+ | Multiple R-squared: | ||
+ | F-statistic: | ||
+ | </ | ||
+ | |||
+ | |||
+ | ===== 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) | ||
+ | </ | ||
+ | |||
+ | Pero ahora voy a hacer una cosa un poco mas complicada, | ||
+ | |||
+ | <code R get_lms2.r> | ||
+ | library(QuantPsyc) | ||
+ | x< | ||
+ | Color=c(" | ||
+ | scan(" | ||
+ | scan(" | ||
+ | sink(file = " | ||
+ | |||
+ | for(i in 1: | ||
+ | for(j in 1: | ||
+ | y.data <- x[c(ni[j], np[i], " | ||
+ | y.data <- y.data[complete.cases(y.data), | ||
+ | a <- lm( paste (' | ||
+ | writeLines(paste(" | ||
+ | writeLines(paste(" | ||
+ | writeLines(paste(" | ||
+ | beta <- lm.beta(a) | ||
+ | for(k in 1: | ||
+ | writeLines(paste(names(beta[k]), | ||
+ | } | ||
+ | writeLines(paste(" | ||
+ | } | ||
+ | } | ||
+ | sink() | ||
+ | </ | ||
+ | |||
+ | Asi que pruebo con el global, | ||
+ | |||
+ | <code R> | ||
+ | > write.csv(okdata, | ||
+ | > source(" | ||
+ | </ | ||
+ | |||
+ | y luego, | ||
+ | |||
+ | <code bash> | ||
+ | [osotolongo@detritus dti_model]$ ./ | ||
+ | Analizing facehbi_dti_np_models.txt ... | ||
+ | |||
+ | DMN_FA, funcioExecutiva_velocprocess_IM | ||
+ | r2 = 0.312799089824939, | ||
+ | pv_DMN_FA = 0.476462254461098, | ||
+ | |||
+ | SN_FA, funcioExecutiva_velocprocess_IM | ||
+ | r2 = 0.311504329049638, | ||
+ | pv_SN_FA = 0.551975051522526, | ||
+ | |||
+ | FPCustom_FA, | ||
+ | r2 = 0.311678871879767, | ||
+ | pv_FPCustom_FA = 0.151421806156447, | ||
+ | </ | ||
+ | |||
+ | 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 | ||
+ | -1.4094 -0.5672 -0.1264 | ||
+ | |||
+ | Coefficients: | ||
+ | | ||
+ | (Intercept) | ||
+ | okdata$SUVR | ||
+ | okdata$Edad | ||
+ | okdata$Escolaridad | ||
+ | okdata$female | ||
+ | okdata$DMN_FA | ||
+ | okdata$Risk | ||
+ | okdata$DMN_FA: | ||
+ | --- | ||
+ | Signif. codes: | ||
+ | |||
+ | Residual standard error: 0.8286 on 188 degrees of freedom | ||
+ | (4 observations deleted due to missingness) | ||
+ | Multiple R-squared: | ||
+ | F-statistic: | ||
+ | </ | ||
+ | |||
+ | No, gracias. :-\ | ||
neuroimagen/altdti.txt · Last modified: 2020/08/04 10:58 by 127.0.0.1