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R/Examples/micromacrolmExample2.r
In MicroMacroMultilevel: Micro-Macro Multilevel Modeling
######## SETUP: DATA GENERATING PROCESSES ########
set.seed(123)
# Step 1. Generate a G-by-q data frame of group-level predictors (e.g., control variables), z.data
# In this example, G = 40, q = 2
group.id = seq(1, 40)
z.var1 = rnorm(40, mean=0, sd=1)
z.var2 = rnorm(40, mean=100, sd=2)
z.data = data.frame(group.id, z.var1, z.var2)
# Step 2. Generate a G-by-p data frame of group-level means for the predictors that will be used to
# generate x.data
# In this example, there are 3 individual-level predictors, thus p = 3
x.var1.means = rnorm(40, mean=50, sd = .05)
x.var2.means = rnorm(40, mean=20, sd = .05)
x.var3.means = rnorm(40, mean=-10, sd = .05)
x.data.means = data.frame(group.id, x.var1.means, x.var2.means, x.var3.means)
# Step 3. Generate two N-by-p data frames of individual-level predictors, x.data
# One of these two data frames assumes unequal-sized groups (Step 3a), whereas the other assumes
# equal-sized groups (Step 3b):
# Step 3a. Generate the individual-level predictors
# In this example, N = 200 and group size is unequal
x.data.unequal = data.frame( group.id=rep(1:40, times=sample( c(4,5,6), 40, replace=TRUE) )[1:200] )
x.data.unequal = merge( x.data.unequal,
data.frame( group.id, x.var1.means, x.var2.means, x.var3.means ), by="group.id" )
x.data.unequal = within( x.data.unequal, {
x.var1 = x.var1.means + rnorm(200, mean=0, sd = 2)
x.var2 = x.var2.means + rnorm(200, mean=0, sd = 6)
x.var3 = x.var3.means + rnorm(200, mean=0, sd = 1.5)
})
# Step 3b. Generate the individual-level predictors
# In this example, N = 200 and group size is equal
x.data.equal = data.frame( group.id=rep(1:40, each=5) )
x.data.equal = merge( x.data.equal, x.data.means, by="group.id" )
x.data.equal = within( x.data.equal, {
x.var1 = x.var1.means + rnorm(200, mean=0, sd = 2)
x.var2 = x.var2.means + rnorm(200, mean=0, sd = 6)
x.var3 = x.var3.means + rnorm(200, mean=0, sd = 1.5)
})
# Step 3. Generate a G-by-1 data frame of group-level outcome variable, y
# In this example, G = 40
y = rnorm(40, mean=6, sd=5)
apply(x.data.equal,2,mean)
# group.id x.var1.means x.var2.means x.var3.means x.var3 x.var2 x.var1
# 20.500000 50.000393 19.994708 -9.999167 -10.031995 20.185361 50.084635
apply(x.data.unequal,2,mean)
# group.id x.var1.means x.var2.means x.var3.means x.var3 x.var2 x.var1
# 20.460000 50.002286 19.994605 -9.997034 -9.983146 19.986111 50.123591
apply(z.data,2,mean)
# z.var1 z.var2
# 0.04518332 99.98656817
mean(y)
# 6.457797
######## EXAMPLE 1. GROUP SIZE IS DIFFERENT ACROSS GROUPS ########
######## Need to use adjusted.predictors() in the same package ###
# Step 4a. Generate a G-by-1 matrix of group ID, z.gid. Then generate an N-by-1 matrix of
# each individual's group ID, x.gid, where the group sizes are different
z.gid = seq(1:40)
x.gid = x.data.unequal$group.id
# Step 5a. Generate the best linear unbiased predictors that are calcualted from
# individual-level data
x.data = x.data.unequal[,c("x.var1","x.var2","x.var3")]
results = adjusted.predictors(x.data, z.data, x.gid, z.gid)
# Note: Given the fixed random seed, the output should be as below
results$unequal.groups
# TRUE
names(results$adjusted.group.means)
# "BLUP.x.var1" "BLUP.x.var2" "BLUP.x.var3" "z.var1" "z.var2" "gid"
head(results$adjusted.group.means)
# BLUP.x.var1 BLUP.x.var2 BLUP.x.var3 group.id z.var1 z.var2 gid
# 1 50.05308 20.83911 -10.700361 1 -0.56047565 98.61059 1
# 2 48.85559 22.97411 -9.957270 2 -0.23017749 99.58417 2
# 3 50.16357 19.50001 -9.645735 3 1.55870831 97.46921 3
# 4 49.61853 21.25962 -10.459398 4 0.07050839 104.33791 4
# 5 50.49673 21.38353 -9.789924 5 0.12928774 102.41592 5
# 6 50.86154 19.15901 -9.245675 6 1.71506499 97.75378 6
# Step 6a. Fit a micro-macro multilevel model when group sizes are different
model.formula = as.formula(y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2)
model.output = micromacro.lm(model.formula, results$adjusted.group.means, y, results$unequal.groups)
micromacro.summary(model.output)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -13.41505 -2.974074 1.13077 3.566021 6.975819
#
#
# Coefficients:
# Estimate Uncorrected S.E. Corrected S.E. df t Pr(>|t|) r
# (Intercept) 78.1232185 121.5103390 122.1367432 34 0.6396373 0.5266952 0.10904278
# BLUP.x.var1 -0.7589602 1.4954434 1.7177575 34 -0.4418320 0.6614084 0.07555696
# BLUP.x.var2 0.4263309 0.7070773 0.6299759 34 0.6767416 0.5031484 0.11528637
# BLUP.x.var3 0.2658078 2.4662049 2.4051691 34 0.1105152 0.9126506 0.01894980
# z.var1 0.4315941 1.0855707 1.0614535 34 0.4066068 0.6868451 0.06956356
# z.var2 -0.3949955 0.5573789 0.4230256 34 -0.9337390 0.3570228 0.15812040
#
# ---
# Residual standard error: 5.1599 on 34 degrees of freedom
# Multiple R-squared: 0.0400727607, Adjusted R-squared: -0.1010930098
# F-statistic: 0.28387 on 5 and 34 DF, p-value: 0.91869
model.output$statistics
# Estimate Uncorrected S.E. Corrected S.E. df t Pr(>|t|) r
# (Intercept) 78.1232185 121.5103390 122.1367432 34 0.6396373 0.5266952 0.10904278
# BLUP.x.var1 -0.7589602 1.4954434 1.7177575 34 -0.4418320 0.6614084 0.07555696
# BLUP.x.var2 0.4263309 0.7070773 0.6299759 34 0.6767416 0.5031484 0.11528637
# BLUP.x.var3 0.2658078 2.4662049 2.4051691 34 0.1105152 0.9126506 0.01894980
# z.var1 0.4315941 1.0855707 1.0614535 34 0.4066068 0.6868451 0.06956356
# z.var2 -0.3949955 0.5573789 0.4230256 34 -0.9337390 0.3570228 0.15812040
model.output$rsquared
# 0.0400727607
model.output$rsquared.adjusted
# -0.1010930098
######## EXAMPLE 2. GROUP SIZE IS THE SAME ACROSS ALL GROUPS ########
######## Need to use adjusted.predictors() in the same package ######
# Step 4b. Generate a G-by-1 matrix of group ID, z.gid. Then generate an N-by-1 matrix of
# each individual's group ID, x.gid, where group size is the same across all groups
z.gid = seq(1:40)
x.gid = x.data.equal$group.id
# Step 5b. Generate the best linear unbiased predictors that are calcualted from
# individual-level data
x.data = x.data.equal[,c("x.var1","x.var2","x.var3")]
results = adjusted.predictors(x.data, z.data, x.gid, z.gid)
results$unequal.groups
# FALSE
names(results$adjusted.group.means)
# "BLUP.x.var1" "BLUP.x.var2" "BLUP.x.var3" "z.var1" "z.var2" "gid"
results$adjusted.group.means[1:5, ]
# BLUP.x.var1 BLUP.x.var2 BLUP.x.var3 group.id z.var1 z.var2 gid
# 1 50.91373 19.12994 -10.051647 1 -0.56047565 98.61059 1
# 2 50.19068 19.17978 -10.814382 2 -0.23017749 99.58417 2
# 3 50.13390 20.98893 -9.952348 3 1.55870831 97.46921 3
# 4 49.68169 19.60632 -10.612717 4 0.07050839 104.33791 4
# 5 50.28579 22.07469 -10.245505 5 0.12928774 102.41592 5
# Step 6b. Fit a micro-macro multilevel model when group size is the same across groups
model.output2 = micromacro.lm(model.formula, results$adjusted.group.means, y,
results$unequal.groups)
micromacro.summary(model.output2)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -12.94409 -1.898937 0.8615494 3.78739 8.444582
#
#
# Coefficients:
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 135.4109966 134.1478457 34 1.0094161 0.3199052 0.17057636
# BLUP.x.var1 -2.1984308 2.2203278 34 -0.9901379 0.3291012 0.16741080
# BLUP.x.var2 -0.6369600 0.8619558 34 -0.7389706 0.4649961 0.12572678
# BLUP.x.var3 -0.5121002 1.7889594 34 -0.2862559 0.7764192 0.04903343
# z.var1 0.7718147 1.1347170 34 0.6801826 0.5009945 0.11586471
# z.var2 -0.1116209 0.5268130 34 -0.2118795 0.8334661 0.03631307
#
# ---
# Residual standard error: 5.11849 on 34 degrees of freedom
# Multiple R-squared: 0.0554183804, Adjusted R-squared: -0.0834906813
# F-statistic: 0.39895 on 5 and 34 DF, p-value: 0.84607
model.output2$statistics
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 135.4109966 134.1478457 34 1.0094161 0.3199052 0.17057636
# BLUP.x.var1 -2.1984308 2.2203278 34 -0.9901379 0.3291012 0.16741080
# BLUP.x.var2 -0.6369600 0.8619558 34 -0.7389706 0.4649961 0.12572678
# BLUP.x.var3 -0.5121002 1.7889594 34 -0.2862559 0.7764192 0.04903343
# z.var1 0.7718147 1.1347170 34 0.6801826 0.5009945 0.11586471
# z.var2 -0.1116209 0.5268130 34 -0.2118795 0.8334661 0.03631307
model.output2$rsquared
# 0.0554183804
model.output2$rsquared.adjusted
# -0.0834906813
######## EXAMPLE 3 (after EXAMPLE 2). ADDING A MICRO-MICRO INTERACTION TERM ########
model.formula3 = as.formula(y ~ BLUP.x.var1 * BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2)
model.output3 = micromacro.lm(model.formula3, results$adjusted.group.means, y,
results$unequal.groups)
micromacro.summary(model.output3)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 * BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -13.21948 -2.048324 0.7062639 3.843816 7.924922
#
#
# Coefficients:
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) -1.098875e+03 1962.9182021 33 -0.5598169 0.5793848 0.09699214
# BLUP.x.var1 2.231877e+01 38.9620284 33 0.5728339 0.5706400 0.09922547
# BLUP.x.var2 5.988568e+01 96.0256433 33 0.6236426 0.5371496 0.10792809
# BLUP.x.var3 -9.557605e-01 1.9374178 33 -0.4933167 0.6250560 0.08556050
# z.var1 6.116347e-01 1.1727757 33 0.5215274 0.6054822 0.09041443
# z.var2 -8.556163e-02 0.5331509 33 -0.1604829 0.8734790 0.02792560
# BLUP.x.var1:BLUP.x.var2 -1.209354e+00 1.9186909 33 -0.6303016 0.5328380 0.10906688
#
# ---
# Residual standard error: 5.08795 on 33 degrees of freedom
# Multiple R-squared: 0.0666547309, Adjusted R-squared: -0.103044409
# F-statistic: 0.39278 on 6 and 33 DF, p-value: 0.87831
model.output3$statistics
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) -1.098875e+03 1962.9182021 33 -0.5598169 0.5793848 0.09699214
# BLUP.x.var1 2.231877e+01 38.9620284 33 0.5728339 0.5706400 0.09922547
# BLUP.x.var2 5.988568e+01 96.0256433 33 0.6236426 0.5371496 0.10792809
# BLUP.x.var3 -9.557605e-01 1.9374178 33 -0.4933167 0.6250560 0.08556050
# z.var1 6.116347e-01 1.1727757 33 0.5215274 0.6054822 0.09041443
# z.var2 -8.556163e-02 0.5331509 33 -0.1604829 0.8734790 0.02792560
# BLUP.x.var1:BLUP.x.var2 -1.209354e+00 1.9186909 33 -0.6303016 0.5328380 0.10906688
model.output3$rsquared
# 0.0666547309
model.output3$rsquared.adjusted
# -0.103044409
######## EXAMPLE 4 (after EXAMPLE 2). ADDING A MICRO-MACRO INTERACTION TERM ########
model.formula4 = as.formula(y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 * z.var1 + z.var2)
model.output4 = micromacro.lm(model.formula4, results$adjusted.group.means, y,
results$unequal.groups)
micromacro.summary(model.output4)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 * z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -12.99937 -1.909645 0.8775397 3.712013 8.46591
#
#
# Coefficients:
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 129.22731579 146.4817031 33 0.8822079 0.3840456 0.15179313
# BLUP.x.var1 -2.10556192 2.3951160 33 -0.8791064 0.3857003 0.15127172
# BLUP.x.var2 -0.63762927 0.8747645 33 -0.7289153 0.4711953 0.12587857
# BLUP.x.var3 -0.53590189 1.8273917 33 -0.2932605 0.7711594 0.05098372
# z.var1 2.95426548 19.1170600 33 0.1545356 0.8781288 0.02689146
# z.var2 -0.09852267 0.5467583 33 -0.1801942 0.8581021 0.03135236
# BLUP.x.var3:z.var1 0.21489002 1.8788995 33 0.1143702 0.9096374 0.01990534
#
# ---
# Residual standard error: 5.11747 on 33 degrees of freedom
# Multiple R-squared: 0.0557926451, Adjusted R-squared: -0.1158814195
# F-statistic: 0.32499 on 6 and 33 DF, p-value: 0.91909
model.output4$statistics
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 129.22731579 146.4817031 33 0.8822079 0.3840456 0.15179313
# BLUP.x.var1 -2.10556192 2.3951160 33 -0.8791064 0.3857003 0.15127172
# BLUP.x.var2 -0.63762927 0.8747645 33 -0.7289153 0.4711953 0.12587857
# BLUP.x.var3 -0.53590189 1.8273917 33 -0.2932605 0.7711594 0.05098372
# z.var1 2.95426548 19.1170600 33 0.1545356 0.8781288 0.02689146
# z.var2 -0.09852267 0.5467583 33 -0.1801942 0.8581021 0.03135236
# BLUP.x.var3:z.var1 0.21489002 1.8788995 33 0.1143702 0.9096374 0.01990534
model.output4$rsquared
# 0.0557926451
model.output4$rsquared.adjusted
# -0.1158814195
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######## SETUP: DATA GENERATING PROCESSES ########
set.seed(123)
# Step 1. Generate a G-by-q data frame of group-level predictors (e.g., control variables), z.data
# In this example, G = 40, q = 2
group.id = seq(1, 40)
z.var1 = rnorm(40, mean=0, sd=1)
z.var2 = rnorm(40, mean=100, sd=2)
z.data = data.frame(group.id, z.var1, z.var2)
# Step 2. Generate a G-by-p data frame of group-level means for the predictors that will be used to
# generate x.data
# In this example, there are 3 individual-level predictors, thus p = 3
x.var1.means = rnorm(40, mean=50, sd = .05)
x.var2.means = rnorm(40, mean=20, sd = .05)
x.var3.means = rnorm(40, mean=-10, sd = .05)
x.data.means = data.frame(group.id, x.var1.means, x.var2.means, x.var3.means)
# Step 3. Generate two N-by-p data frames of individual-level predictors, x.data
# One of these two data frames assumes unequal-sized groups (Step 3a), whereas the other assumes
# equal-sized groups (Step 3b):
# Step 3a. Generate the individual-level predictors
# In this example, N = 200 and group size is unequal
x.data.unequal = data.frame( group.id=rep(1:40, times=sample( c(4,5,6), 40, replace=TRUE) )[1:200] )
x.data.unequal = merge( x.data.unequal,
data.frame( group.id, x.var1.means, x.var2.means, x.var3.means ), by="group.id" )
x.data.unequal = within( x.data.unequal, {
x.var1 = x.var1.means + rnorm(200, mean=0, sd = 2)
x.var2 = x.var2.means + rnorm(200, mean=0, sd = 6)
x.var3 = x.var3.means + rnorm(200, mean=0, sd = 1.5)
})
# Step 3b. Generate the individual-level predictors
# In this example, N = 200 and group size is equal
x.data.equal = data.frame( group.id=rep(1:40, each=5) )
x.data.equal = merge( x.data.equal, x.data.means, by="group.id" )
x.data.equal = within( x.data.equal, {
x.var1 = x.var1.means + rnorm(200, mean=0, sd = 2)
x.var2 = x.var2.means + rnorm(200, mean=0, sd = 6)
x.var3 = x.var3.means + rnorm(200, mean=0, sd = 1.5)
})
# Step 3. Generate a G-by-1 data frame of group-level outcome variable, y
# In this example, G = 40
y = rnorm(40, mean=6, sd=5)
apply(x.data.equal,2,mean)
# group.id x.var1.means x.var2.means x.var3.means x.var3 x.var2 x.var1
# 20.500000 50.000393 19.994708 -9.999167 -10.031995 20.185361 50.084635
apply(x.data.unequal,2,mean)
# group.id x.var1.means x.var2.means x.var3.means x.var3 x.var2 x.var1
# 20.460000 50.002286 19.994605 -9.997034 -9.983146 19.986111 50.123591
apply(z.data,2,mean)
# z.var1 z.var2
# 0.04518332 99.98656817
mean(y)
# 6.457797
######## EXAMPLE 1. GROUP SIZE IS DIFFERENT ACROSS GROUPS ########
######## Need to use adjusted.predictors() in the same package ###
# Step 4a. Generate a G-by-1 matrix of group ID, z.gid. Then generate an N-by-1 matrix of
# each individual's group ID, x.gid, where the group sizes are different
z.gid = seq(1:40)
x.gid = x.data.unequal$group.id
# Step 5a. Generate the best linear unbiased predictors that are calcualted from
# individual-level data
x.data = x.data.unequal[,c("x.var1","x.var2","x.var3")]
results = adjusted.predictors(x.data, z.data, x.gid, z.gid)
# Note: Given the fixed random seed, the output should be as below
results$unequal.groups
# TRUE
names(results$adjusted.group.means)
# "BLUP.x.var1" "BLUP.x.var2" "BLUP.x.var3" "z.var1" "z.var2" "gid"
head(results$adjusted.group.means)
# BLUP.x.var1 BLUP.x.var2 BLUP.x.var3 group.id z.var1 z.var2 gid
# 1 50.05308 20.83911 -10.700361 1 -0.56047565 98.61059 1
# 2 48.85559 22.97411 -9.957270 2 -0.23017749 99.58417 2
# 3 50.16357 19.50001 -9.645735 3 1.55870831 97.46921 3
# 4 49.61853 21.25962 -10.459398 4 0.07050839 104.33791 4
# 5 50.49673 21.38353 -9.789924 5 0.12928774 102.41592 5
# 6 50.86154 19.15901 -9.245675 6 1.71506499 97.75378 6
# Step 6a. Fit a micro-macro multilevel model when group sizes are different
model.formula = as.formula(y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2)
model.output = micromacro.lm(model.formula, results$adjusted.group.means, y, results$unequal.groups)
micromacro.summary(model.output)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -13.41505 -2.974074 1.13077 3.566021 6.975819
#
#
# Coefficients:
# Estimate Uncorrected S.E. Corrected S.E. df t Pr(>|t|) r
# (Intercept) 78.1232185 121.5103390 122.1367432 34 0.6396373 0.5266952 0.10904278
# BLUP.x.var1 -0.7589602 1.4954434 1.7177575 34 -0.4418320 0.6614084 0.07555696
# BLUP.x.var2 0.4263309 0.7070773 0.6299759 34 0.6767416 0.5031484 0.11528637
# BLUP.x.var3 0.2658078 2.4662049 2.4051691 34 0.1105152 0.9126506 0.01894980
# z.var1 0.4315941 1.0855707 1.0614535 34 0.4066068 0.6868451 0.06956356
# z.var2 -0.3949955 0.5573789 0.4230256 34 -0.9337390 0.3570228 0.15812040
#
# ---
# Residual standard error: 5.1599 on 34 degrees of freedom
# Multiple R-squared: 0.0400727607, Adjusted R-squared: -0.1010930098
# F-statistic: 0.28387 on 5 and 34 DF, p-value: 0.91869
model.output$statistics
# Estimate Uncorrected S.E. Corrected S.E. df t Pr(>|t|) r
# (Intercept) 78.1232185 121.5103390 122.1367432 34 0.6396373 0.5266952 0.10904278
# BLUP.x.var1 -0.7589602 1.4954434 1.7177575 34 -0.4418320 0.6614084 0.07555696
# BLUP.x.var2 0.4263309 0.7070773 0.6299759 34 0.6767416 0.5031484 0.11528637
# BLUP.x.var3 0.2658078 2.4662049 2.4051691 34 0.1105152 0.9126506 0.01894980
# z.var1 0.4315941 1.0855707 1.0614535 34 0.4066068 0.6868451 0.06956356
# z.var2 -0.3949955 0.5573789 0.4230256 34 -0.9337390 0.3570228 0.15812040
model.output$rsquared
# 0.0400727607
model.output$rsquared.adjusted
# -0.1010930098
######## EXAMPLE 2. GROUP SIZE IS THE SAME ACROSS ALL GROUPS ########
######## Need to use adjusted.predictors() in the same package ######
# Step 4b. Generate a G-by-1 matrix of group ID, z.gid. Then generate an N-by-1 matrix of
# each individual's group ID, x.gid, where group size is the same across all groups
z.gid = seq(1:40)
x.gid = x.data.equal$group.id
# Step 5b. Generate the best linear unbiased predictors that are calcualted from
# individual-level data
x.data = x.data.equal[,c("x.var1","x.var2","x.var3")]
results = adjusted.predictors(x.data, z.data, x.gid, z.gid)
results$unequal.groups
# FALSE
names(results$adjusted.group.means)
# "BLUP.x.var1" "BLUP.x.var2" "BLUP.x.var3" "z.var1" "z.var2" "gid"
results$adjusted.group.means[1:5, ]
# BLUP.x.var1 BLUP.x.var2 BLUP.x.var3 group.id z.var1 z.var2 gid
# 1 50.91373 19.12994 -10.051647 1 -0.56047565 98.61059 1
# 2 50.19068 19.17978 -10.814382 2 -0.23017749 99.58417 2
# 3 50.13390 20.98893 -9.952348 3 1.55870831 97.46921 3
# 4 49.68169 19.60632 -10.612717 4 0.07050839 104.33791 4
# 5 50.28579 22.07469 -10.245505 5 0.12928774 102.41592 5
# Step 6b. Fit a micro-macro multilevel model when group size is the same across groups
model.output2 = micromacro.lm(model.formula, results$adjusted.group.means, y,
results$unequal.groups)
micromacro.summary(model.output2)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -12.94409 -1.898937 0.8615494 3.78739 8.444582
#
#
# Coefficients:
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 135.4109966 134.1478457 34 1.0094161 0.3199052 0.17057636
# BLUP.x.var1 -2.1984308 2.2203278 34 -0.9901379 0.3291012 0.16741080
# BLUP.x.var2 -0.6369600 0.8619558 34 -0.7389706 0.4649961 0.12572678
# BLUP.x.var3 -0.5121002 1.7889594 34 -0.2862559 0.7764192 0.04903343
# z.var1 0.7718147 1.1347170 34 0.6801826 0.5009945 0.11586471
# z.var2 -0.1116209 0.5268130 34 -0.2118795 0.8334661 0.03631307
#
# ---
# Residual standard error: 5.11849 on 34 degrees of freedom
# Multiple R-squared: 0.0554183804, Adjusted R-squared: -0.0834906813
# F-statistic: 0.39895 on 5 and 34 DF, p-value: 0.84607
model.output2$statistics
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 135.4109966 134.1478457 34 1.0094161 0.3199052 0.17057636
# BLUP.x.var1 -2.1984308 2.2203278 34 -0.9901379 0.3291012 0.16741080
# BLUP.x.var2 -0.6369600 0.8619558 34 -0.7389706 0.4649961 0.12572678
# BLUP.x.var3 -0.5121002 1.7889594 34 -0.2862559 0.7764192 0.04903343
# z.var1 0.7718147 1.1347170 34 0.6801826 0.5009945 0.11586471
# z.var2 -0.1116209 0.5268130 34 -0.2118795 0.8334661 0.03631307
model.output2$rsquared
# 0.0554183804
model.output2$rsquared.adjusted
# -0.0834906813
######## EXAMPLE 3 (after EXAMPLE 2). ADDING A MICRO-MICRO INTERACTION TERM ########
model.formula3 = as.formula(y ~ BLUP.x.var1 * BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2)
model.output3 = micromacro.lm(model.formula3, results$adjusted.group.means, y,
results$unequal.groups)
micromacro.summary(model.output3)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 * BLUP.x.var2 + BLUP.x.var3 + z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -13.21948 -2.048324 0.7062639 3.843816 7.924922
#
#
# Coefficients:
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) -1.098875e+03 1962.9182021 33 -0.5598169 0.5793848 0.09699214
# BLUP.x.var1 2.231877e+01 38.9620284 33 0.5728339 0.5706400 0.09922547
# BLUP.x.var2 5.988568e+01 96.0256433 33 0.6236426 0.5371496 0.10792809
# BLUP.x.var3 -9.557605e-01 1.9374178 33 -0.4933167 0.6250560 0.08556050
# z.var1 6.116347e-01 1.1727757 33 0.5215274 0.6054822 0.09041443
# z.var2 -8.556163e-02 0.5331509 33 -0.1604829 0.8734790 0.02792560
# BLUP.x.var1:BLUP.x.var2 -1.209354e+00 1.9186909 33 -0.6303016 0.5328380 0.10906688
#
# ---
# Residual standard error: 5.08795 on 33 degrees of freedom
# Multiple R-squared: 0.0666547309, Adjusted R-squared: -0.103044409
# F-statistic: 0.39278 on 6 and 33 DF, p-value: 0.87831
model.output3$statistics
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) -1.098875e+03 1962.9182021 33 -0.5598169 0.5793848 0.09699214
# BLUP.x.var1 2.231877e+01 38.9620284 33 0.5728339 0.5706400 0.09922547
# BLUP.x.var2 5.988568e+01 96.0256433 33 0.6236426 0.5371496 0.10792809
# BLUP.x.var3 -9.557605e-01 1.9374178 33 -0.4933167 0.6250560 0.08556050
# z.var1 6.116347e-01 1.1727757 33 0.5215274 0.6054822 0.09041443
# z.var2 -8.556163e-02 0.5331509 33 -0.1604829 0.8734790 0.02792560
# BLUP.x.var1:BLUP.x.var2 -1.209354e+00 1.9186909 33 -0.6303016 0.5328380 0.10906688
model.output3$rsquared
# 0.0666547309
model.output3$rsquared.adjusted
# -0.103044409
######## EXAMPLE 4 (after EXAMPLE 2). ADDING A MICRO-MACRO INTERACTION TERM ########
model.formula4 = as.formula(y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 * z.var1 + z.var2)
model.output4 = micromacro.lm(model.formula4, results$adjusted.group.means, y,
results$unequal.groups)
micromacro.summary(model.output4)
# Call:
# micromacro.lm( y ~ BLUP.x.var1 + BLUP.x.var2 + BLUP.x.var3 * z.var1 + z.var2, ...)
#
# Residuals:
# Min 1Q Median 3Q Max
# -12.99937 -1.909645 0.8775397 3.712013 8.46591
#
#
# Coefficients:
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 129.22731579 146.4817031 33 0.8822079 0.3840456 0.15179313
# BLUP.x.var1 -2.10556192 2.3951160 33 -0.8791064 0.3857003 0.15127172
# BLUP.x.var2 -0.63762927 0.8747645 33 -0.7289153 0.4711953 0.12587857
# BLUP.x.var3 -0.53590189 1.8273917 33 -0.2932605 0.7711594 0.05098372
# z.var1 2.95426548 19.1170600 33 0.1545356 0.8781288 0.02689146
# z.var2 -0.09852267 0.5467583 33 -0.1801942 0.8581021 0.03135236
# BLUP.x.var3:z.var1 0.21489002 1.8788995 33 0.1143702 0.9096374 0.01990534
#
# ---
# Residual standard error: 5.11747 on 33 degrees of freedom
# Multiple R-squared: 0.0557926451, Adjusted R-squared: -0.1158814195
# F-statistic: 0.32499 on 6 and 33 DF, p-value: 0.91909
model.output4$statistics
# Estimate S.E. df t Pr(>|t|) r
# (Intercept) 129.22731579 146.4817031 33 0.8822079 0.3840456 0.15179313
# BLUP.x.var1 -2.10556192 2.3951160 33 -0.8791064 0.3857003 0.15127172
# BLUP.x.var2 -0.63762927 0.8747645 33 -0.7289153 0.4711953 0.12587857
# BLUP.x.var3 -0.53590189 1.8273917 33 -0.2932605 0.7711594 0.05098372
# z.var1 2.95426548 19.1170600 33 0.1545356 0.8781288 0.02689146
# z.var2 -0.09852267 0.5467583 33 -0.1801942 0.8581021 0.03135236
# BLUP.x.var3:z.var1 0.21489002 1.8788995 33 0.1143702 0.9096374 0.01990534
model.output4$rsquared
# 0.0557926451
model.output4$rsquared.adjusted
# -0.1158814195
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