help for HLM
The important part of this output is the line Number of groups: 2 You are trying to estimate three variance-covariance parameters with only two levels of Trip. You would need many levels of Trip to be able to do so. When you have only two levels of a categorical variable you must model it with fixed-effects parameters even though from the structure of the experiment it may seem reasonable to use random effects.
On Wed, Sep 5, 2012 at 1:45 PM, Karina Villegas <villegaskary at gmail.com> wrote:
*Dear R experts:* * * *I am running R version 2.12.1 on Windows 2007. I am studying the effects maternal behavior in the body condition of sea lion pups from California. * * * *I'm trying to make a hierarchical linear model* *When I run the full model if I get these results:* *> LevelModel7 <- lme(PBC ~ Sex*Dur.nurse + Sex*Freq.interaction + Sex*Density.females , random=~Sex|Trip, data=Dataset)* *> summary (LevelModel7)* *Linear mixed-effects model fit by REML* * Data: Dataset* * AIC BIC logLik* * 446.1461 478.1074 -211.0730* * * *Random effects:* * Formula: ~Sex | Trip* * Structure: General positive-definite, Log-Cholesky parametrization* * StdDev Corr * *(Intercept) 0.5250613 (Intr)* *Sex 0.0614132 0 * *Residual 1.4865472 * * * *Fixed effects: PBC ~ Sex * Dur.nurse + Sex * Freq.interaction + Sex * Density.females* * Value Std.Error DF t-value p-value* *(Intercept) 3.32002 13.521223 105 0.2455415 0.8065* *Sex 0.76586 1.584268 105 0.4834144 0.6298* *Dur.nurse 1.95957 1.798351 105 1.0896502 0.2784* *Freq.interaction -0.22624 0.219637 105 -1.0300505 0.3054* *Density.females -32.19456 19.335512 105 -1.6650480 0.0989* *Sex:Dur.nurse -0.21788 0.209796 105 -1.0385351 0.3014* *Sex:Freq.interaction 0.02165 0.025693 105 0.8426169 0.4014* *Sex:Density.females 3.15918 2.258348 105 1.3988881 0.1648* * Correlation:* * (Intr) Sex Dr.nrs Frq.nt Dnsty. Sx:Dr. Sx:Fr.* *Sex -0.998 * *Dur.nurse -0.928 0.924 * *Freq.interaction -0.194 0.195 0.103 * *Density.females 0.613 -0.609 -0.809 -0.346 * *Sex:Dur.nurse 0.928 -0.928 -0.996 -0.109 0.802 * *Sex:Freq.interaction 0.196 -0.198 -0.108 -0.994 0.353 0.108 * *Sex:Density.females -0.611 0.609 0.802 0.354 -0.991 -0.807 -0.350* * * *Standardized Within-Group Residuals:* * Min Q1 Med Q3 Max* *-2.46806088 -0.47865795 -0.05134942 0.57369682 2.64445772* * * *Number of Observations: 114* *Number of Groups: 2* * * *When I run simple models (one variable included: females Density, Frequency interaction) I have no problem either.* * * *> LevelModel3 <- lme(PBC ~ Sex*Density.females , random=~Sex|Trip, data=Dataset)* *> summary (LevelModel3)* *Linear mixed-effects model fit by REML* * Data: Dataset* * AIC BIC logLik* * 428.5119 450.1158 -206.2560* * * *Random effects:* * Formula: ~Sex | Trip* * Structure: General positive-definite, Log-Cholesky parametrization* * **StdDev Corr * *(Intercept) 1.076517e+00 (Intr)* *Sex 5.258193e-05 0 * *Residual 1.489254e+00 * * * *Fixed effects: PBC ~ Sex * Density.females* * Value Std.Error DF t-value p-value* *(Intercept) 14.774095 4.876298 109 3.029777 0.0031* *Sex -0.646479 0.566328 109 -1.141528 0.2562* *Density.females -18.980461 10.099637 109 -1.879321 0.0629* *Sex:Density.females 1.892986 1.185115 109 1.597302 0.1131* * Correlation:* * (Intr) Sex Dnsty.* *Sex -0.984 * *Density.females -0.857 0.862 * *Sex:Density.females 0.855 -0.869 -0.995* * * *Standardized Within-Group Residuals:* * Min Q1 Med Q3 Max* *-2.678669640 -0.591855777 0.006108111 0.543417969 2.424555266* * * *Number of Observations: 114* *Number of Groups: 2* *> LevelModel4 <- lme(PBC ~ Sex*Freq.interaction , random=~Sex|Trip, data=Dataset)* *> summary (LevelModel4)* *Linear mixed-effects model fit by REML* * Data: Dataset* * AIC BIC logLik* * 451.1833 472.7872 -217.5917* * * *Random effects:* * Formula: ~Sex | Trip* * Structure: General positive-definite, Log-Cholesky parametrization* * **StdDev Corr * *(Intercept) 1.934101e+00 (Intr)* *Sex 3.442453e-05 0 * *Residual 1.527767e+00 * * * *Fixed effects: PBC ~ Sex * Freq.interaction* * Value Std.Error DF t-value p-value* *(Intercept) 12.595151 4.211533 109 2.990633 0.0034* *Sex -0.535742 0.465667 109 -1.150484 0.2525* *Freq.interaction -0.334393 0.199095 109 -1.679564 0.0959* *Sex:Freq.interaction 0.039834 0.023241 109 1.713935 0.0894* * Correlation:* * (Intr) Sex Frq.nt* *Sex -0.943 * *Freq.interaction -0.745 0.783 * *Sex:Freq.interaction 0.743 -0.788 -0.996* * * *Standardized Within-Group Residuals:* * Min Q1 Med Q3 Max* *-2.79224714 -0.46765869 -0.04400343 0.66192444 2.53486123* * * *Number of Observations: 114* *Number of Groups: 2* * * * * *However, when I include only variable Dur.nurse, I get the following error message:*
LevelModel2 <- lme(PBC ~ Sex*Dur.nurse, random=~Sex|Trip, data=Dataset)
Error in lme.formula(PBC ~ Sex * Dur.nurse, random = ~Sex | Trip, data = Dataset) : nlminb problem, convergence error code = 1 message = iteration limit reached without convergence (9) *We thought it was a problem with the number of iterations and I increased the iterations, but I still get the error:*
LevelModel1 <- lme(PBC ~ Sex*Dur.nurse, random=~Sex|Trip, data=Dataset,
control=lmeControl(maxIter=200))
Error in lme.formula(PBC ~ Sex * Dur.nurse, random = ~Sex | Trip, data =
Dataset, :
nlminb problem, convergence error code = 1
message = iteration limit reached without convergence (9)
*Somebody has any idea?*
*Thanks and regards*
*Karina*
--
Biol. Karina Villegas Cervantes
Estudiante de Maestr?a PCMyL - UNAM
Laboratorio de Ecologia de Pinnipedos Burney J. Le Boueuf.
CICIMAR-IPN
Av. Instituto Politecnico Nacional s/n.Col.Playa Palo de Santa Rita
La Paz Baja California Sur, Mexico.
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