search for: sexfemale

Hello! If I run this example: library(nlme) fm1 <- lme(distance ~ age+Sex, Orthodont, random = ~ age + Sex| Subject) If I run: summary(fm1) then I can see the fixed effects for age and sex (17.7 for intercept, 0.66 for age, and -1.66 for SexFemale) If I run: ranef(fm1) Then it looks like it's producing the random effects for each subgroup (in this example - each subject). For example, for MO1 it's: 1.25 for intercept, 0.106 for age, and -1.52 for SexFemale. So, in order to get the the total effects, i.e., the regression equation, f...

Hi, R or S+ users, I want to make a simple transformation for the model, but for the whole design matrix. The model is distance ~ age * Sex, where Sex is a factor. So the design matrix may look like the following: (Intercept) age SexFemale age:SexFemale 1 1 8 0 0 2 1 10 0 0 3 1 12 0 0 4 1 14 0 0 5 1 8 0 0 6 1 10 0 0 7 1 12...

...Plus, 2000, pp. 135-137). One thing I don't understand is: When I run the command fm1Orth.lm <- lm( distance ~ age, Orthodont ) followed by fm2Orth.lm <- update( fm1Orth.lm, formula = distance ~ Sex*age ) and then do summary(fm2Orth.lm) in the output, there's "SexFemale" instead of "Sex" (see below). Why? Call: lm(formula = distance ~ Sex + age + Sex:age, data = Orthodont) Residuals: Min 1Q Median 3Q Max -5.6156 -1.3219 -0.1682 1.3299 5.2469 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 16...

...also show in each panel the pooled OLS regression line for each Sex in the corresponding panel, generated by the following model: Ortho.OLS <- lm(distance ~ age * Sex, data=Orthodont) Sex is a factor, with Male=0, so the coefficients are: > coef(Ortho.OLS) (Intercept) age SexFemale age:SexFemale 16.3406250 0.7843750 1.0321023 -0.3048295 I anticipate wanting to fit other models to these data, and also displaying the model-predicted regression lines in the same or similar plot, e.g., for a simple linear mixed model: Ortho.MLM <- lme(distance ~ age * Sex, da...

Dear R users, Using R lme function, I found that both fixed and random effects of variable A on variable B are significant. Now, I'd like to analyze what variables are predicting differences in the slope. In other words, I'd like to know what variables (e.g., variable C) are predicting individual differences in the effects of A on B. I have many data points for A and B for each

....7563 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 1.807425 1.431592 Fixed effects: distance ~ age + Sex Value Std.Error DF t-value p-value (Intercept) 17.706713 0.8339225 80 21.233044 0.0000 age 0.660185 0.0616059 80 10.716263 0.0000 SexFemale -2.321023 0.7614168 25 -3.048294 0.0054 Correlation: (Intr) age age -0.813 SexFemale -0.372 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.74889609 -0.55034466 -0.02516628 0.45341781 3.65746539 Number of Obs...

...Estimate Std.err Wald Pr(>|W|) (Intercept) 3.07e+16 7.20e+14 1821.29 0.00000 age 6.38e+13 2.22e+13 8.24 0.00409 RACEBlack 1.48e+16 6.28e+14 555.35 0.00000 RACEOther -1.84e+16 6.17e+14 887.78 0.00000 SEXFemale 1.84e+16 5.23e+14 1235.19 0.00000 FEVERYes -4.41e+15 4.74e+14 86.73 0.00000 FEVERUnknown 1.76e+16 1.60e+15 120.55 0.00000 compared to the estimates from the glm model: Estimate Std. Error z value Pr(>|z|) (Intercept)...

Hi, Can anybody tell me what the message below means and how to overcome it. Thanks, Neil Warning message: X matrix deemed to be singular; variable 2 in: coxph(Surv(age_at_death, death) ~ project$pluralgp + project$yrborn + ......... >

...*sex , design = shs, family = quasibinomial()) m2 <- svyglm( intuse ~ (pmin(age, 35) + pmax(age, 35))*sex , design = shs, family = quasibinomial()) summary(m) # OK. Coincide con el libro summary(m2) # OK. Coincide con el libro svycontrast(m2, quote('pmin(age, 35)' +'pmin(age, 35):sexfemale' )) # ¡Falla! ## Error en deriv.default(expr, names(datalist)) : ## expresión inválida en 'FindSubexprs' ## sessionInfo() # Mi configuración ## R version 2.12.0 (2010-10-15) ## Platform: i486-pc-linux-gnu (32-bit) ## locale: ## [1] LC_CTYPE=es_ES.UTF-8 LC_NUMERIC=C ## [3] L...

...kelihood: -253.629 Fixed: distance ~ Sex (Intercept) Sex1 23.808239 1.160511 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 1.595838 2.220312 Number of Observations: 108 Number of Groups: 27 > model.matrix(m, data=Orthodont) (Intercept) SexFemale 1 1 0 2 1 0 3 1 0 . . . 106 1 1 107 1 1 108 1 1 attr(,"assign") [1] 0 1 attr(,"contrasts") attr(,"contrasts")$Sex [1] "contr.treatment" ------...

...Sex: fm2 = lme(distance ~ I(age-8) + Sex, random = ~ 1 + I(age-8) | Subject, data = Orthodont) R# summary(fm2) Fixed effects: distance ~ I(age - 8) + Sex Value Std.Error DF t-value p-value (Intercept) 22.917 0.5134 80 44.64 0.000 I(age - 8) 0.660 0.0713 80 9.27 0.000 SexFemale -2.145 0.7575 25 -2.83 0.009 Along the lines of question #1, how would you get a growth model adjusting for these Sex differences in initial status? I am looking for something similar to adjusting for baseline differences between Sexes in ANCOVA. I know Lord would not approve, but this...

...x1 > ? 23.808239??? 1.160511 > > Random effects: > ?Formula: ~1 | Subject > ??????? (Intercept) Residual > StdDev:??? 1.595838 2.220312 > > Number of Observations: 108 > Number of Groups: 27 > > > model.matrix(m, data=Orthodont) > > ??? (Intercept) SexFemale > 1???????????? 1???????? 0 > 2???????????? 1???????? 0 > 3???????????? 1???????? 0 > > . . . > > 106?????????? 1???????? 1 > 107?????????? 1???????? 1 > 108?????????? 1???????? 1 > attr(,"assign") > [1] 0 1 > attr(,"contrasts") > attr(,&...

Hello R experts I have two linear regressions for sexes (Male, Female, Unknown). All have a good correlation between body length (response variable) and head length (explanatory variable). I know it is not recommended, but for a good practical reason (the purpose of study is to find a single conversion factor from head length to body length), the regressions need to go through the origin (0