Library packages
pkgs <- c("nlme", "dplyr", "magrittr", "tidyr", "ggplot2", "lmtest","lme4","reshape2","scales","extracat","HLMdiag","car","mets")
lapply(pkgs, library, character.only = TRUE)
Question 1
library("HLMdiag")
dta1 <- radon
str(dta1)
## 'data.frame': 919 obs. of 5 variables:
## $ log.radon : num 0.788 0.788 1.065 0 1.131 ...
## $ basement : int 1 0 0 0 0 0 0 0 0 0 ...
## $ uranium : num -0.689 -0.689 -0.689 -0.689 -0.847 ...
## $ county : int 1 1 1 1 2 2 2 2 2 2 ...
## $ county.name: Factor w/ 85 levels "AITKIN","ANOKA",..: 1 1 1 1 2 2 2 2 2 2 ...
dta1$county<- as.factor(dta1$county)
summary(q1 <- lmer(log.radon~ 1+(1|county), data = dta1))
## Linear mixed model fit by REML ['lmerMod']
## Formula: log.radon ~ 1 + (1 | county)
## Data: dta1
##
## REML criterion at convergence: 2259.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4661 -0.5734 0.0441 0.6432 3.3516
##
## Random effects:
## Groups Name Variance Std.Dev.
## county (Intercept) 0.09581 0.3095
## Residual 0.63662 0.7979
## Number of obs: 919, groups: county, 85
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.31258 0.04891 26.84
confint(q1)
## 2.5 % 97.5 %
## .sig01 0.2201867 0.4071878
## .sigma 0.7612997 0.8374554
## (Intercept) 1.2166850 1.4101343
Question 2
dta2 <- ergoStool
str(dta2)
## Classes 'nffGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame': 36 obs. of 3 variables:
## $ effort : num 12 15 12 10 10 14 13 12 7 14 ...
## $ Type : Factor w/ 4 levels "T1","T2","T3",..: 1 2 3 4 1 2 3 4 1 2 ...
## $ Subject: Ord.factor w/ 9 levels "8"<"5"<"4"<"9"<..: 8 8 8 8 9 9 9 9 6 6 ...
## - attr(*, "formula")=Class 'formula' language effort ~ Type | Subject
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## - attr(*, "labels")=List of 2
## ..$ x: chr "Type of stool"
## ..$ y: chr "Effort required to arise"
## - attr(*, "units")=List of 1
## ..$ y: chr "(Borg scale)"
## - attr(*, "FUN")=function (x)
## ..- attr(*, "source")= chr "function (x) mean(x, na.rm = TRUE)"
## - attr(*, "order.groups")= logi TRUE
##Mixed effect model
summary(q2 <- lmer(effort ~ Type + (1 | Subject), data = dta2))
## Linear mixed model fit by REML ['lmerMod']
## Formula: effort ~ Type + (1 | Subject)
## Data: dta2
##
## REML criterion at convergence: 121.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.80200 -0.64317 0.05783 0.70100 1.63142
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 1.775 1.332
## Residual 1.211 1.100
## Number of obs: 36, groups: Subject, 9
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 8.5556 0.5760 14.853
## TypeT2 3.8889 0.5187 7.498
## TypeT3 2.2222 0.5187 4.284
## TypeT4 0.6667 0.5187 1.285
##
## Correlation of Fixed Effects:
## (Intr) TypeT2 TypeT3
## TypeT2 -0.450
## TypeT3 -0.450 0.500
## TypeT4 -0.450 0.500 0.500
##95% confident interval
confint(q2)
## 2.5 % 97.5 %
## .sig01 0.7342354 2.287261
## .sigma 0.8119798 1.390104
## (Intercept) 7.4238425 9.687269
## TypeT2 2.8953043 4.882473
## TypeT3 1.2286377 3.215807
## TypeT4 -0.3269179 1.660251
Question 3
dta3 <- read.table("http://titan.ccunix.ccu.edu.tw/~psycfs/lmm/Data/thetaEEG.txt",header = T)
str(dta3)
## 'data.frame': 19 obs. of 10 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Ch3 : num -3.54 5.72 0.52 0 2.07 1.67 9.13 -0.43 -0.56 1.28 ...
## $ Ch4 : num -3.11 5.07 -0.18 0.74 0.76 ...
## $ Ch5 : num -0.24 6.87 0.9 1.1 3.51 2.77 3.44 -0.31 -1.22 1.89 ...
## $ Ch6 : num 0.42 5.96 0.6 0.13 0.6 4.55 4.8 -0.61 0.67 1.77 ...
## $ Ch7 : num -0.49 8.2 1.27 0.19 3.71 1.8 0.48 -1.04 -0.97 1.83 ...
## $ Ch8 : num 2.13 4.87 1.28 0.07 1.86 4.79 1.63 -0.13 -0.98 0.91 ...
## $ Ch17: num -4.15 5.48 -0.95 0.8 1.49 4.51 9.94 -0.61 0 1.4 ...
## $ Ch18: num 2.87 5.57 1.74 0.25 3.11 3.24 1.34 -0.61 -1.22 1.1 ...
## $ Ch19: num 1.34 6.33 0.79 -0.66 1.8 3.99 1.53 -0.43 -0.91 -0.12 ...
cov(dta3[,2:9])
## Ch3 Ch4 Ch5 Ch6 Ch7 Ch8 Ch17
## Ch3 8.645877 9.526639 5.625497 5.212582 3.849543 2.910530 9.021380
## Ch4 9.526639 12.348605 5.209576 6.170981 2.671112 3.218435 10.720563
## Ch5 5.625497 5.209576 5.560076 4.205233 4.540831 3.504274 5.730063
## Ch6 5.212582 6.170981 4.205233 5.073637 3.083282 3.707812 6.069184
## Ch7 3.849543 2.671112 4.540831 3.083282 5.181343 2.903964 3.451603
## Ch8 2.910530 3.218435 3.504274 3.707812 2.903964 4.281098 3.589004
## Ch17 9.021380 10.720563 5.730063 6.069184 3.451603 3.589004 10.738754
## Ch18 3.547645 2.416258 4.215412 3.657924 3.811479 3.189672 3.378316
## Ch18
## Ch3 3.547645
## Ch4 2.416258
## Ch5 4.215412
## Ch6 3.657924
## Ch7 3.811479
## Ch8 3.189672
## Ch17 3.378316
## Ch18 6.769626
dta3_melt <- melt(dta3,id.vars = "ID",measure.vars = c("Ch3","Ch4","Ch5","Ch6","Ch7","Ch8","Ch17","Ch18","Ch19"),
variable.name = "Channel",value.name = "Response")
str(dta3_melt)
## 'data.frame': 171 obs. of 3 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Channel : Factor w/ 9 levels "Ch3","Ch4","Ch5",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Response: num -3.54 5.72 0.52 0 2.07 1.67 9.13 -0.43 -0.56 1.28 ...
# set ggplot theme to black and white
theme_set(theme_bw())
##GGPLOT
facetshade(dta3_melt, aes(ID, Response), f = . ~ Channel ) +
geom_point( color = alpha(1, 0.1) ) +
geom_hline(yintercept = mean(dta3_melt$Response), linetype = "dashed") +
geom_point( data = dta3_melt ) +
labs(x = "ID", y = "Response")
dta3_melt$ID <- as.factor(dta3_melt$ID)
dta3_melt$Channel <- as.factor(dta3_melt$Channel)
leveneTest(data = dta3_melt, Response~ ID)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 18 4.0777 6.89e-07 ***
## 152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(data = dta3_melt, Response ~ Channel)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 8 0.4616 0.8816
## 162
# random effect by aov
summary(q3_1 <- aov(Response ~ Channel + Error(ID), data = dta3_melt))
##
## Error: ID
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 18 765.7 42.54
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## Channel 8 10.7 1.338 0.495 0.858
## Residuals 144 389.3 2.704
# random effect by lme
summary(q3_2 <- lmer(Response ~ Channel + (1 | ID) - 1, data = dta3_melt))
## Linear mixed model fit by REML ['lmerMod']
## Formula: Response ~ Channel + (1 | ID) - 1
## Data: dta3_melt
##
## REML criterion at convergence: 697
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6502 -0.3882 -0.0002 0.4215 4.6394
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.426 2.104
## Residual 2.704 1.644
## Number of obs: 171, groups: ID, 19
##
## Fixed effects:
## Estimate Std. Error t value
## ChannelCh3 0.9011 0.6126 1.471
## ChannelCh4 1.5895 0.6126 2.595
## ChannelCh5 1.0374 0.6126 1.694
## ChannelCh6 1.1458 0.6126 1.870
## ChannelCh7 0.8511 0.6126 1.389
## ChannelCh8 0.8526 0.6126 1.392
## ChannelCh17 1.4026 0.6126 2.290
## ChannelCh18 0.8542 0.6126 1.395
## ChannelCh19 0.9947 0.6126 1.624
##
## Correlation of Fixed Effects:
## ChnnC3 ChnnC4 ChnnC5 ChnnC6 ChnnC7 ChnnC8 ChnC17 ChnC18
## ChannelCh4 0.621
## ChannelCh5 0.621 0.621
## ChannelCh6 0.621 0.621 0.621
## ChannelCh7 0.621 0.621 0.621 0.621
## ChannelCh8 0.621 0.621 0.621 0.621 0.621
## ChannelCh17 0.621 0.621 0.621 0.621 0.621 0.621
## ChannelCh18 0.621 0.621 0.621 0.621 0.621 0.621 0.621
## ChannelCh19 0.621 0.621 0.621 0.621 0.621 0.621 0.621 0.621
# profile confidence intervals
confint(q3_2)
## 2.5 % 97.5 %
## .sig01 1.49440911 2.972004
## .sigma 1.43604215 1.798638
## ChannelCh3 -0.29635638 2.098462
## ChannelCh4 0.39206468 2.786883
## ChannelCh5 -0.16004059 2.234777
## ChannelCh6 -0.05161953 2.343198
## ChannelCh7 -0.34635638 2.048462
## ChannelCh8 -0.34477743 2.050041
## ChannelCh17 0.20522257 2.600041
## ChannelCh18 -0.34319848 2.051619
## ChannelCh19 -0.20267217 2.192146
Question 4
dta4 <- read.table("http://titan.ccunix.ccu.edu.tw/~psycfs/lmm/Data/skin_resistance.txt",header = T)
str(dta4)
## 'data.frame': 80 obs. of 3 variables:
## $ Electrode: Factor w/ 5 levels "E1","E2","E3",..: 1 2 3 4 5 1 2 3 4 5 ...
## $ Subject : Factor w/ 16 levels "S1","S10","S11",..: 1 1 1 1 1 9 9 9 9 9 ...
## $ Kohm : int 500 400 98 200 250 660 600 600 75 310 ...
summary(q4 <- aov(Kohm ~ Electrode + Error(Subject/Electrode), data = dta4))
##
## Error: Subject
## Df Sum Sq Mean Sq F value Pr(>F)
## Residuals 15 1399155 93277
##
## Error: Subject:Electrode
## Df Sum Sq Mean Sq F value Pr(>F)
## Electrode 4 281575 70394 3.146 0.0205 *
## Residuals 60 1342723 22379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# show grand mean and Electrode means
model.tables(q4, "means")
## Tables of means
## Grand mean
##
## 203.05
##
## Electrode
## Electrode
## E1 E2 E3 E4 E5
## 181.69 287.31 258.00 150.37 137.87
# show Electrode effects
model.tables(q4, se = TRUE)
## Tables of effects
##
## Electrode
## Electrode
## E1 E2 E3 E4 E5
## -21.36 84.26 54.95 -52.68 -65.18
##
## Standard errors of effects
## Electrode
## 37.4
## replic. 16
#Intraclass correlation
((93277-22379)/5)/((93277-22379)/5+(70394-22379)/16+22379)
## [1] 0.358437
Question 5
data(dermalridgesMZ)
dta5 <- dermalridgesMZ
str(dta5)
## 'data.frame': 36 obs. of 5 variables:
## $ sex : Factor w/ 2 levels "female","male": 1 1 1 1 2 2 1 1 1 1 ...
## $ left : int 95 90 93 80 99 94 55 48 41 26 ...
## $ right: num 83 85 90 80 94 99 61 54 24 42 ...
## $ group: Factor w/ 2 levels "normal","psychotic&neurotic": 2 2 2 2 2 2 2 2 2 2 ...
## $ id : int 1 1 2 2 3 3 4 4 5 5 ...
## - attr(*, "na.action")=Class 'omit' Named int [1:12] 37 38 39 40 41 42 43 44 45 46 ...
## .. ..- attr(*, "names")= chr [1:12] "37" "38" "39" "40" ...
dta5$id <- as.factor(dta5$id)
leveneTest(data = dta5, left ~ id)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 17 1.1576e+30 < 2.2e-16 ***
## 18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(data = dta5, left ~ sex)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 2.1467 0.1521
## 34
leveneTest(data = dta5, left ~ group)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.2457 0.6233
## 34
summary(q5 <- lmer(left ~ right + sex +
group + (1 | id), data = dta5))
## Linear mixed model fit by REML ['lmerMod']
## Formula: left ~ right + sex + group + (1 | id)
## Data: dta5
##
## REML criterion at convergence: 246.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0212 -0.5041 -0.0385 0.5361 2.0965
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.00 0.000
## Residual 74.67 8.641
## Number of obs: 36, groups: id, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -1.58701 5.41931 -0.293
## right 0.98097 0.07267 13.500
## sexmale 3.64364 3.04497 1.197
## grouppsychotic&neurotic 0.92776 3.07857 0.301
##
## Correlation of Fixed Effects:
## (Intr) right sexmal
## right -0.869
## sexmale 0.022 -0.229
## grppsychtc& -0.440 0.094 -0.100
confint(q5)
## 2.5 % 97.5 %
## .sig01 0.0000000 5.078684
## .sigma 6.5741586 10.463115
## (Intercept) -11.8743782 8.700349
## right 0.8430274 1.118908
## sexmale -2.1365654 9.423854
## grouppsychotic&neurotic -4.9162182 6.771739
Question 6
dta6 <- read.table("http://titan.ccunix.ccu.edu.tw/~psycfs/lmm/Data/dog_scans.txt",header = T)
str(dta6)
## 'data.frame': 55 obs. of 4 variables:
## $ Dog : Factor w/ 11 levels "Aussie","Corrie",..: 9 11 11 11 11 11 11 7 7 7 ...
## $ Observer : Factor w/ 3 levels "A","B","C": 3 3 2 1 3 2 1 3 2 1 ...
## $ Scan : Factor w/ 29 levels "dog_100","dog_101",..: 14 15 15 15 16 16 16 17 17 17 ...
## $ Thickness: num 2.6 4.93 3.47 5.29 2.54 2.8 3 3.57 3.3 2.66 ...
##Fixed effect
anova(q6 <- lm(Thickness ~ Dog+Observer+Scan - 1, data = dta6))
## Analysis of Variance Table
##
## Response: Thickness
## Df Sum Sq Mean Sq F value Pr(>F)
## Dog 11 1039.59 94.508 104.3095 < 2e-16 ***
## Observer 2 1.85 0.924 1.0202 0.37564
## Scan 18 33.02 1.834 2.0245 0.05348 .
## Residuals 24 21.74 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##Mixed effect model
summary(q6_2 <- lmer(Thickness ~ (1 | Observer) + (1 | Dog) +
(1 | Dog:Observer) + (1 | Scan), data = dta6))
## Linear mixed model fit by REML ['lmerMod']
## Formula: Thickness ~ (1 | Observer) + (1 | Dog) + (1 | Dog:Observer) +
## (1 | Scan)
## Data: dta6
##
## REML criterion at convergence: 177.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8144 -0.5141 -0.0410 0.3252 1.6897
##
## Random effects:
## Groups Name Variance Std.Dev.
## Scan (Intercept) 0.70477 0.8395
## Dog:Observer (Intercept) 0.30706 0.5541
## Dog (Intercept) 0.29388 0.5421
## Observer (Intercept) 0.08284 0.2878
## Residual 0.56554 0.7520
## Number of obs: 55, groups:
## Scan, 29; Dog:Observer, 28; Dog, 11; Observer, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.4157 0.3298 13.39
Question 7
dta7 <- read.table("http://titan.ccunix.ccu.edu.tw/~psycfs/lmm/Data/healthAwareness.txt",header = T)
str(dta7)
## 'data.frame': 45 obs. of 4 variables:
## $ Index : num 42 56 35 40 28 26 38 42 35 53 ...
## $ State : int 1 1 1 1 1 1 1 1 1 1 ...
## $ City : int 1 1 1 1 1 2 2 2 2 2 ...
## $ Household: int 1 2 3 4 5 1 2 3 4 5 ...
dta7 %<>% mutate(city_num = State*10+City)
ggplot(data= dta7,aes(x=city_num,y=Index))+facet_grid(.~State)+geom_point()
dta7$State <- as.factor(dta7$State)
dta7$City <- as.factor(dta7$City)
dta7$Household <- as.factor(dta7$Household)
summary(q7 <- lmer(Index ~ (1 | State) + (State | City) +(City|Household)
, data = dta7))
## Linear mixed model fit by REML ['lmerMod']
## Formula: Index ~ (1 | State) + (State | City) + (City | Household)
## Data: dta7
##
## REML criterion at convergence: 336.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.55296 -0.75218 0.01324 0.68583 1.76317
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Household (Intercept) 1.334e+01 3.652e+00
## City2 1.884e+01 4.341e+00 -0.97
## City3 3.123e+01 5.589e+00 -0.99 1.00
## City (Intercept) 9.438e-14 3.072e-07
## State2 3.650e-12 1.910e-06 -0.97
## State3 8.089e-13 8.994e-07 -0.92 0.79
## State (Intercept) 2.266e+02 1.505e+01
## Residual 9.002e+01 9.488e+00
## Number of obs: 45, groups: Household, 5; City, 3; State, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 41.760 8.811 4.74
Question 8
dta8 <-read.table("http://www.biostat.ucsf.edu/vgsm/1st_ed/data/gababies.txt",header = T)
str(dta8)
## 'data.frame': 1000 obs. of 11 variables:
## $ momid : int 39 39 39 39 39 62 62 62 62 62 ...
## $ birthord: int 1 2 3 4 5 1 2 3 4 5 ...
## $ momage : int 15 17 19 24 25 17 21 25 27 28 ...
## $ timesnc : int 0 2 4 9 10 0 4 8 10 11 ...
## $ delwght : int -1240 -1240 -1240 -1240 -1240 -170 -170 -170 -170 -170 ...
## $ lowbrth : int 0 0 0 0 1 1 1 1 1 1 ...
## $ bweight : int 3720 3260 3910 3320 2480 2381 2835 2381 2268 2211 ...
## $ lastwght: int 2480 2480 2480 2480 2480 2211 2211 2211 2211 2211 ...
## $ initage : int 15 15 15 15 15 17 17 17 17 17 ...
## $ initwght: int 3720 3720 3720 3720 3720 2381 2381 2381 2381 2381 ...
## $ cinitage: num -2.55 -2.55 -2.55 -2.55 -2.55 ...
dta8$momid <- as.factor(dta8$momid)
dta8$birthord <- as.factor(dta8$birthord)
leveneTest(data = dta8, bweight ~ momid)##不顯著
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 199 0.9587 0.6376
## 800
leveneTest(data = dta8, bweight ~ birthord)##不顯著
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.4022 0.8071
## 995
##全部假設fixed effect
summary(q7 <- lm(bweight ~ momid + birthord +momage+timesnc+delwght+lowbrth+lastwght+initage+initwght+cinitage
, data = dta8))
##
## Call:
## lm(formula = bweight ~ momid + birthord + momage + timesnc +
## delwght + lowbrth + lastwght + initage + initwght + cinitage,
## data = dta8)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1937.1 -174.6 7.6 183.6 1399.7
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3323.561 171.174 19.416 < 2e-16 ***
## momid62 -391.962 224.447 -1.746 0.081138 .
## momid79 -381.972 222.915 -1.714 0.087005 .
## momid80 145.036 223.050 0.650 0.515724
## momid84 142.752 222.685 0.641 0.521676
## momid92 -436.908 223.780 -1.952 0.051241 .
## momid108 -274.484 222.673 -1.233 0.218060
## momid113 49.930 226.355 0.221 0.825475
## momid125 142.532 222.566 0.640 0.522095
## momid135 -96.446 222.990 -0.433 0.665487
## momid199 -134.580 222.700 -0.604 0.545809
## momid200 -40.263 222.797 -0.181 0.856638
## momid221 43.510 222.788 0.195 0.845211
## momid247 -264.386 222.653 -1.187 0.235410
## momid304 -159.288 223.243 -0.714 0.475734
## momid336 300.324 222.690 1.349 0.177843
## momid547 -43.973 223.587 -0.197 0.844136
## momid853 152.339 225.839 0.675 0.500161
## momid960 292.118 222.924 1.310 0.190442
## momid1232 -208.047 223.126 -0.932 0.351402
## momid1448 -46.543 222.677 -0.209 0.834488
## momid1638 91.732 224.084 0.409 0.682383
## momid1706 -68.099 222.977 -0.305 0.760137
## momid1785 -291.692 224.262 -1.301 0.193748
## momid1856 -137.930 222.827 -0.619 0.536093
## momid2083 -325.145 223.963 -1.452 0.146958
## momid2166 -692.692 224.262 -3.089 0.002080 **
## momid2292 -138.508 222.923 -0.621 0.534564
## momid2301 -175.482 222.653 -0.788 0.430852
## momid2383 -89.474 222.634 -0.402 0.687875
## momid2519 -399.137 224.375 -1.779 0.075641 .
## momid2598 304.189 222.667 1.366 0.172288
## momid2613 -378.139 223.222 -1.694 0.090657 .
## momid2647 -103.836 226.295 -0.459 0.646465
## momid2735 -82.766 222.960 -0.371 0.710576
## momid2822 -142.375 222.931 -0.639 0.523233
## momid2899 -64.684 222.673 -0.290 0.771517
## momid2906 470.224 223.816 2.101 0.035961 *
## momid2918 -24.017 222.664 -0.108 0.914133
## momid2928 73.431 222.925 0.329 0.741943
## momid3044 447.718 222.653 2.011 0.044681 *
## momid3168 -91.408 222.706 -0.410 0.681592
## momid3308 -231.629 223.380 -1.037 0.300086
## momid3377 -242.434 222.946 -1.087 0.277184
## momid3431 -158.371 222.961 -0.710 0.477720
## momid3438 -67.449 223.065 -0.302 0.762447
## momid3469 50.865 222.571 0.229 0.819291
## momid3477 -83.001 222.975 -0.372 0.709811
## momid3480 -80.180 222.700 -0.360 0.718916
## momid3504 -786.827 224.349 -3.507 0.000478 ***
## momid3526 -172.951 224.099 -0.772 0.440486
## momid3668 152.671 222.587 0.686 0.492982
## momid3797 -83.640 222.918 -0.375 0.707608
## momid3838 -83.089 224.152 -0.371 0.710975
## momid3936 223.194 222.615 1.003 0.316360
## momid4154 -251.804 223.115 -1.129 0.259414
## momid4194 -338.974 222.849 -1.521 0.128634
## momid4341 -178.758 222.817 -0.802 0.422641
## momid4411 -194.544 223.646 -0.870 0.384633
## momid4438 28.518 222.653 0.128 0.898114
## momid4482 391.389 222.667 1.758 0.079178 .
## momid4503 -63.023 223.355 -0.282 0.777890
## momid4566 656.710 222.788 2.948 0.003295 **
## momid4631 -223.732 222.566 -1.005 0.315088
## momid4664 -40.623 222.762 -0.182 0.855347
## momid4690 -143.103 223.537 -0.640 0.522244
## momid4708 260.645 222.739 1.170 0.242278
## momid4714 -723.375 222.931 -3.245 0.001224 **
## momid4755 -552.459 224.431 -2.462 0.014043 *
## momid4845 707.618 224.348 3.154 0.001671 **
## momid4890 -34.175 222.931 -0.153 0.878201
## momid4891 -285.391 222.679 -1.282 0.200347
## momid4987 -250.023 223.355 -1.119 0.263309
## momid5120 -65.237 222.919 -0.293 0.769867
## momid5160 38.203 222.578 0.172 0.863765
## momid5262 -218.335 226.104 -0.966 0.334518
## momid5286 -367.635 223.506 -1.645 0.100396
## momid5337 -87.243 222.941 -0.391 0.695661
## momid5340 -317.982 224.190 -1.418 0.156478
## momid5435 -113.456 222.735 -0.509 0.610630
## momid5501 562.980 222.700 2.528 0.011665 *
## momid5513 231.651 222.657 1.040 0.298474
## momid5554 -314.479 222.659 -1.412 0.158231
## momid5726 -336.285 223.411 -1.505 0.132663
## momid5828 -226.211 222.667 -1.016 0.309978
## momid5847 -99.752 222.685 -0.448 0.654311
## momid6039 -267.122 223.336 -1.196 0.232031
## momid6065 -172.636 226.295 -0.763 0.445761
## momid6123 -86.761 223.369 -0.388 0.697808
## momid6150 135.218 224.348 0.603 0.546869
## momid6201 -202.813 223.685 -0.907 0.364847
## momid6246 -205.282 222.924 -0.921 0.357402
## momid6443 23.454 222.654 0.105 0.916135
## momid6533 77.175 223.177 0.346 0.729584
## momid6534 359.648 222.685 1.615 0.106697
## momid6601 -175.795 222.770 -0.789 0.430271
## momid6610 -86.417 222.664 -0.388 0.698043
## momid6711 -37.012 222.767 -0.166 0.868085
## momid6737 -157.153 224.092 -0.701 0.483329
## momid6790 -781.291 223.352 -3.498 0.000495 ***
## momid6847 -437.797 224.210 -1.953 0.051216 .
## momid6932 -173.186 222.653 -0.778 0.436900
## momid6960 -56.721 222.802 -0.255 0.799115
## momid6981 13.397 224.037 0.060 0.952333
## momid7086 -24.451 222.657 -0.110 0.912585
## momid7184 -378.987 223.244 -1.698 0.089969 .
## momid7209 -152.947 222.840 -0.686 0.492690
## momid7320 40.666 222.896 0.182 0.855282
## momid7406 -101.388 222.767 -0.455 0.649139
## momid7413 -219.048 222.685 -0.984 0.325580
## momid7468 461.274 222.634 2.072 0.038598 *
## momid7472 -145.780 222.700 -0.655 0.512913
## momid7556 -9.200 222.565 -0.041 0.967038
## momid7733 79.758 222.817 0.358 0.720474
## momid7752 -339.167 223.478 -1.518 0.129494
## momid7782 -45.313 222.718 -0.203 0.838833
## momid7791 -370.952 223.394 -1.661 0.097203 .
## momid7866 -143.177 222.762 -0.643 0.520580
## momid7931 -141.876 222.690 -0.637 0.524244
## momid7932 198.805 222.770 0.892 0.372437
## momid8086 62.344 222.735 0.280 0.779625
## momid8231 -230.673 223.830 -1.031 0.303056
## momid8281 -210.423 223.355 -0.942 0.346428
## momid8429 -41.267 222.934 -0.185 0.853192
## momid8485 -64.271 222.587 -0.289 0.772853
## momid8523 -566.797 224.210 -2.528 0.011665 *
## momid8544 -392.811 223.607 -1.757 0.079354 .
## momid8677 -410.251 222.657 -1.843 0.065771 .
## momid8734 -140.555 223.352 -0.629 0.529336
## momid8792 -135.496 223.046 -0.607 0.543707
## momid8918 -95.508 222.923 -0.428 0.668452
## momid9216 -79.850 222.970 -0.358 0.720349
## momid9288 -95.143 222.677 -0.427 0.669298
## momid9504 154.460 222.725 0.694 0.488198
## momid11630 44.438 223.742 0.199 0.842616
## momid11704 -559.138 226.485 -2.469 0.013767 *
## momid11913 29.033 223.204 0.130 0.896540
## momid11935 -262.991 222.679 -1.181 0.237942
## momid11939 115.679 222.659 0.520 0.603534
## momid11973 -189.578 222.971 -0.850 0.395450
## momid12115 139.436 223.050 0.625 0.532062
## momid12256 -186.353 254.434 -0.732 0.464127
## momid12614 -167.239 222.921 -0.750 0.453346
## momid12655 -190.820 223.380 -0.854 0.393230
## momid12751 -158.993 225.708 -0.704 0.481378
## momid12872 -145.974 222.849 -0.655 0.512633
## momid13156 -642.021 224.121 -2.865 0.004285 **
## momid13161 -224.423 223.355 -1.005 0.315308
## momid13221 -50.180 222.700 -0.225 0.821783
## momid13500 -102.116 222.673 -0.459 0.646655
## momid13563 -188.162 223.742 -0.841 0.400614
## momid14020 -178.065 222.571 -0.800 0.423929
## momid14262 -152.401 222.975 -0.683 0.494497
## momid14297 -194.741 230.528 -0.845 0.398498
## momid14792 -337.333 224.142 -1.505 0.132722
## momid14864 -276.232 223.098 -1.238 0.216022
## momid14883 124.257 222.677 0.558 0.576992
## momid14922 -259.494 223.361 -1.162 0.245678
## momid14925 -274.029 223.824 -1.224 0.221200
## momid14935 -74.682 222.924 -0.335 0.737704
## momid14998 242.710 222.788 1.089 0.276300
## momid15044 -55.476 222.705 -0.249 0.803346
## momid15096 756.651 229.392 3.299 0.001015 **
## momid15229 40.191 223.965 0.179 0.857629
## momid15319 -83.633 225.883 -0.370 0.711295
## momid15552 134.977 222.762 0.606 0.544737
## momid15621 -173.693 222.883 -0.779 0.436035
## momid15976 -168.540 222.725 -0.757 0.449441
## momid15985 -177.671 222.587 -0.798 0.424990
## momid16065 -227.682 222.924 -1.021 0.307402
## momid16327 -167.513 222.718 -0.752 0.452197
## momid16625 -136.135 222.571 -0.612 0.540944
## momid16673 34.995 227.456 0.154 0.877765
## momid16735 -106.652 224.758 -0.475 0.635260
## momid16822 -180.059 222.655 -0.809 0.418935
## momid16840 -157.290 222.788 -0.706 0.480390
## momid16908 -89.592 222.706 -0.402 0.687580
## momid16911 -346.497 223.394 -1.551 0.121287
## momid17193 -129.414 222.653 -0.581 0.561246
## momid17229 -116.986 222.653 -0.525 0.599438
## momid17238 -58.069 224.286 -0.259 0.795774
## momid17735 91.884 222.673 0.413 0.679980
## momid17743 -770.547 223.537 -3.447 0.000597 ***
## momid17961 135.591 222.679 0.609 0.542758
## momid18559 134.009 222.679 0.602 0.547476
## momid18849 -250.302 223.320 -1.121 0.262703
## momid18922 632.242 222.817 2.837 0.004663 **
## momid19040 173.718 229.203 0.758 0.448721
## momid19224 -162.147 223.537 -0.725 0.468440
## momid19617 239.895 223.516 1.073 0.283472
## momid19668 -78.174 222.849 -0.351 0.725835
## momid19721 327.939 225.839 1.452 0.146872
## momid19860 -152.961 222.921 -0.686 0.492808
## momid20232 -146.691 223.352 -0.657 0.511520
## momid20233 9.430 223.570 0.042 0.966367
## momid20282 -6.632 224.088 -0.030 0.976396
## momid20296 26.999 222.975 0.121 0.903654
## momid20301 -157.361 227.318 -0.692 0.488983
## momid20498 -282.979 225.643 -1.254 0.210175
## momid20855 109.736 226.248 0.485 0.627793
## birthord2 58.689 36.114 1.625 0.104534
## birthord3 65.561 38.509 1.702 0.089055 .
## birthord4 89.071 43.040 2.070 0.038820 *
## birthord5 108.121 47.937 2.256 0.024373 *
## momage 4.338 3.952 1.098 0.272623
## timesnc NA NA NA NA
## delwght NA NA NA NA
## lowbrth -683.069 31.231 -21.871 < 2e-16 ***
## lastwght NA NA NA NA
## initage NA NA NA NA
## initwght NA NA NA NA
## cinitage NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 351.9 on 794 degrees of freedom
## Multiple R-squared: 0.7075, Adjusted R-squared: 0.632
## F-statistic: 9.37 on 205 and 794 DF, p-value: < 2.2e-16
##Mixed effect model
summary(q7 <- lmer(bweight ~ (1|momid) + birthord +momage+timesnc+delwght+lowbrth+lastwght+initage+initwght+cinitage
, data = dta8))
## Linear mixed model fit by REML ['lmerMod']
## Formula: bweight ~ (1 | momid) + birthord + momage + timesnc + delwght +
## lowbrth + lastwght + initage + initwght + cinitage
## Data: dta8
##
## REML criterion at convergence: 14519.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5013 -0.5884 0.0292 0.6042 4.5802
##
## Random effects:
## Groups Name Variance Std.Dev.
## momid (Intercept) 1218 34.9
## Residual 124004 352.1
## Number of obs: 1000, groups: momid, 200
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2066.23470 105.15239 19.650
## birthord2 48.03047 35.75235 1.343
## birthord3 45.06267 37.17619 1.212
## birthord4 56.74891 39.96545 1.420
## birthord5 66.09795 43.11172 1.533
## momage 1.49560 3.62633 0.412
## timesnc 7.84852 4.83976 1.622
## delwght -0.17052 0.02231 -7.644
## lowbrth -694.85604 26.34420 -26.376
## lastwght 0.39027 0.02719 14.355
##
## Correlation of Fixed Effects:
## (Intr) brthr2 brthr3 brthr4 brthr5 momage timsnc dlwght lwbrth
## birthord2 -0.167
## birthord3 -0.160 0.522
## birthord4 -0.143 0.516 0.569
## birthord5 -0.117 0.502 0.571 0.632
## momage -0.472 0.010 0.017 0.025 0.030
## timesnc 0.310 -0.114 -0.211 -0.313 -0.383 -0.780
## delwght 0.419 0.029 0.055 0.084 0.108 0.076 -0.183
## lowbrth -0.484 0.017 0.027 0.025 0.001 0.031 0.021 -0.194
## lastwght -0.751 -0.008 -0.017 -0.031 -0.051 -0.168 0.201 -0.585 0.442
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 3 columns / coefficients