search for: sexm

...) Residuals: Min 1Q Median 3Q Max -0.156304 -0.036740 0.002953 0.039081 0.213696 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.538260 4.956850 1.924 0.0556 . ageJ -15.943787 11.211551 -1.422 0.1564 sexM -11.844042 6.195258 -1.912 0.0572 . year -0.004657 0.002474 -1.883 0.0611 . ageJ:sexM 18.887391 13.657536 1.383 0.1681 ageJ:year 0.007923 0.005590 1.417 0.1578 sexM:year 0.005977 0.003091 1.934 0.0545 . ageJ:sexM:year -0.00945...

...3Q Max -86.90 -26.28 -7.68 22.52 123.74 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 29.430 6.248 4.710 4.80e-06 *** TestNumber2 56.231 8.837 6.364 1.47e-09 *** TestNumber3 75.972 10.061 7.551 1.82e-12 *** SexM 7.101 9.845 0.721 0.472 TestNumber2:SexM -16.483 13.854 -1.190 0.236 TestNumber3:SexM -24.571 15.343 -1.601 0.111 --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Residual standard error: 40.97 on 188 degrees of freedom Mult...

...ect named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value p-value (Intercept) 0.23268607 0.09350662 3990 2.4884449 1.287080e-02 sexM -0.15338225 0.03169762 3990 -4.8389206 1.354802e-06 standLRT 0.38593558 0.01677195 3990 23.0107762 4.005182e-110 vrmid 50% 0.07606394 0.09389376 61 0.8101064 4.210281e-01 vrtop 25% 0.24561327 0.10483374 61 2.3428838 2.241317e-02 intakemid 50% -0.41469716 0.0317...

...ect named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value p-value (Intercept) 0.23268607 0.09350662 3990 2.4884449 1.287080e-02 sexM -0.15338225 0.03169762 3990 -4.8389206 1.354802e-06 standLRT 0.38593558 0.01677195 3990 23.0107762 4.005182e-110 vrmid 50% 0.07606394 0.09389376 61 0.8101064 4.210281e-01 vrtop 25% 0.24561327 0.10483374 61 2.3428838 2.241317e-02 intakemid 50% -0.41469716 0.0317...

Dear list, I am new to this list and I am new to the world of R. Additionally I am not very firm in statistics either but have to deal. So here is my problem: I have a dataset (which I attach at the end of the post) with a binomial response variable (alive or not) and three fixed factors (trapping,treat,sex). I do have repeated measures and would like to include one (enclosure) random factor. I

...ect named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value p-value (Intercept) 0.23268607 0.09350662 3990 2.4884449 1.287080e-02 sexM -0.15338225 0.03169762 3990 -4.8389206 1.354802e-06 standLRT 0.38593558 0.01677195 3990 23.0107762 4.005182e-110 vrmid 50% 0.07606394 0.09389376 61 0.8101064 4.210281e-01 vrtop 25% 0.24561327 0.10483374 61 2.3428838 2.241317e-02 intakemid 50% -0.41469716 0.0317...

...iance Residuals: Min 1Q Median 3Q Max -1.82241 -0.85632 0.06675 0.61981 1.47874 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -3.47939 0.46167 -7.537 4.83e-14 *** Dose 0.10597 0.01286 8.243 < 2e-16 *** SexM 0.15501 0.63974 0.242 0.809 Dose:SexM -0.01821 0.01707 -1.067 0.286 --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 225.455 on 23 degrees of freedom Residual deviance...

...glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.9935 0.5527 -5.416 6.09e-08 *** sexM 0.1750 0.7783 0.225 0.822 ldose 0.9060 0.1671 5.422 5.89e-08 *** sexM:ldose 0.3529 0.2700 1.307 0.191 --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 (Dispersion parameter for binomial family taken to be 1) Null deviance:...

...+ random= ~rt|id, na.action=na.omit) > intervals(GLU)$fixed lower est. upper (Intercept) 67.3467070345 7.362307e+01 7.989944e+01 rt *0.0148050160* 6.249304e-02 1.101811e-01 cd4 -0.0032133709 -6.566501e-04 1.900071e-03 sexM 0.9601467541 3.565606e+00 6.171066e+00 age *0.2436472425* 3.420025e-01 4.403578e-01 rfOTHER -5.0933264232 -1.198439e+00 2.696449e+00 rfETEROSESSUALE -2.1085096013 1.974725e+00 6.057960e+00 rfOMOSESSUALE -5.8156940466 -1.891870e+00 2.031953e+00 nadir...

...Deviance Residuals: Min 1Q Median 3Q Max -0.94787 -0.36158 0.04914 0.63592 1.56417 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 2.7634 0.5231 5.282 1.28e-07 *** ldose -0.7793 0.1550 -5.028 4.96e-07 *** sexM 0.7219 0.8477 0.852 0.39444 ldose:sexM -0.8085 0.3131 -2.582 0.00981 ** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 136.1139 on 11 degrees of freedom Residual devianc...

...0001 grade.L 1.42974 0.0597163 196971 23.9423 <.0001 grade.Q -2.77355 0.0384930 196971 -72.0533 <.0001 grade.C -0.33285 0.0364386 196971 -9.1345 <.0001 grade^4 1.10184 0.0355329 196971 31.0089 <.0001 grade^5 1.48623 0.0349311 196971 42.5477 <.0001 sexM -1.83115 0.0770710 134707 -23.7592 <.0001 ethnicityH 1.85379 0.0836131 134707 22.1711 <.0001 ethnicityM 3.09373 0.4232054 134707 7.3102 <.0001 ethnicityO 10.47489 0.4083131 134707 25.6541 <.0001 ethnicityW 10.16495 0.1245893 134707 81.5876 <.0001 Correlat...

Dear all, How to obtain the odds ratio (OR) and 95% confidence interval (CI) with 1 standard deviation (SD) change of a continuous variable in logistic regression? for example, to investigate the risk of obesity for stroke. I choose the happening of stroke (positive) as the dependent variable, and waist circumference as an independent variable. Then I wanna to obtain the OR and 95% CI with

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

library(effects) mod <- lm(Measurement ~ Age + Sex, data=d) e <-effect("Sex",mod) The effect is evaluated at the mean age. > e Sex effect Sex F M 43.33083 44.48531 > > e$model.matrix (Intercept) Age SexM 1 1 130.5859 0 23 1 130.5859 1 To evaluate the effect at Age=120 I tried: e <-effect("Sex",mod,xlevels=list(Age=c(120))) but the effect was still evaluated at 130.5859. Is this an incorrect usage of xlevels? Thanks, David

Full_Name: Soren Feodor Nielsen Version: 2.5.0 OS: linux-gnu Submission from: (NULL) (130.225.103.21) The print-out of xtable in the following example is wrong; instead of yielding the correct ci's for the second model it repeats the ci's from the first model. require(xtable) require(MASS) data(cats) b1<-lm(Hwt~Sex,cats) b2<-lm(Hwt~Sex+Bwt,cats)

...summary(file.name, corr=F) and got the following table: Deviance Residuals: Min 1Q Median 3Q Max -14.118 -4.808 -1.466 2.033 33.882 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 8.696e+00 1.893e+00 4.594 1.06e-05 *** sexm -3.791e+00 1.364e+00 -2.779 0.00631 ** trtccc -1.050e+00 4.325e+00 -0.243 0.80859 trtcga3 2.450e+00 4.325e+00 0.566 0.57211 trtcga4 -2.300e+00 4.325e+00 -0.532 0.59584 trtg 1.550e+00 2.497e+00 0.621 0.53593 trtga4 -5.550e...

I am struiggling a bit with this function 'hatvalues'. I would like a little more undrestanding than taking the black-box and using the values. I looked at the Fortran source and it is quite opaque to me. So I am asking for some help in understanding the theory. First, I take the simplest case of a single variant. For this I turn o John Fox's book, "Applied Regression Analysis

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is

...d effects. At a certain point, I get a model (m17) where the fixed effects are like this (full output is at end of message): > print(m17, corr=F) ... Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.97887 0.19699 -10.045 < 2e-16 *** sexM 0.45553 0.14387 3.166 0.001544 ** ... is_discTRUE 0.24676 0.15204 1.623 0.104576 poly(wfreq, 2)1 -119.72397 11.00516 -10.879 < 2e-16 *** poly(wfreq, 2)2 17.35646 5.44456 3.188 0.001433 ** poly(wlen_p, 2)1 -13.60798 7.26926 -1....